@article {68709, title = {BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets.}, journal = {Nature Methods}, volume = {20}, year = {2023}, month = {2023 Jun 01}, abstract = {

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

}, issn = {1548-7105}, doi = {10.1038/s41592-023-01848-5}, author = {Manubens-Gil, Linus and Zhou, Zhi and Chen, Hanbo and Ramanathan, Arvind and Liu, Xiaoxiao and Liu, Yufeng and Bria, Alessandro and Gillette, Todd and Ruan, Zongcai and Yang, Jian and Radojevi{\'c}, Miroslav and Zhao, Ting and Cheng, Li and Qu, Lei and Liu, Siqi and Bouchard, Kristofer E and Gu, Lin and Cai, Weidong and Ji, Shuiwang and Roysam, Badrinath and Wang, Ching-Wei and Yu, Hongchuan and Sironi, Amos and Iascone, Daniel Maxim and Zhou, Jie and Bas, Erhan and Conde-Sousa, Eduardo and Aguiar, Paulo and Li, Xiang and Li, Yujie and Nanda, Sumit and Wang, Yuan and Muresan, Leila and Fua, Pascal and Ye, Bing and He, Hai-Yan and Staiger, Jochen F and Peter, Manuel and Cox, Daniel N and Simonneau, Michel and Oberlaender, Marcel and Jefferis, Gregory and Ito, Kei and Gonzalez-Bellido, Paloma and Kim, Jinhyun and Rubel, Edwin and Cline, Hollis T and Zeng, Hongkui and Nern, Aljoscha and Chiang, Ann-Shyn and Yao, Jianhua and Roskams, Jane and Livesey, Rick and Stevens, Janine and Liu, Tianming and Dang, Chinh and Guo, Yike and Zhong, Ning and Tourassi, Georgia and Hill, Sean and Hawrylycz, Michael and Koch, Christof and Meijering, Erik and Ascoli, Giorgio A and Peng, Hanchuan} } @article {46901, title = {BigNeuron: Large-scale 3D neuron reconstruction from optical microscopy images.}, journal = {Neuron}, volume = {87}, year = {2015}, month = {2015 Jul 15}, pages = {252-6}, abstract = {

Understanding the structure of single neurons is critical for understanding how they function within neural circuits. BigNeuron is a new community effort that combines modern bioimaging informatics, recent leaps in labeling and microscopy, and the widely recognized need for openness and standardization to provide a community resource for automated reconstruction of dendritic and axonal morphology of single neurons.

Understanding the structure of single neurons is critical for understanding how they function within neural circuits. BigNeuron is a new community effort that combines modern bioimaging informatics, recent leaps in labeling and microscopy, and the widely recognized need for openness and standardization to provide a community resource for automated reconstruction of dendritic and axonal morphology of single neurons.

}, doi = {10.1016/j.neuron.2015.06.036}, author = {Peng, Hanchuan and Hawrylycz, Michael and Roskams, Jane and Hill, Sean and Spruston, Nelson and Meijering, Erik and Ascoli, Giorgio A} } @article {46813, title = {BlastNeuron for automated comparison, retrieval and clustering of 3D neuron morphologies.}, journal = {Neuroinformatics}, volume = {13}, year = {2015}, month = {2015 Jun 3}, pages = {487-99}, abstract = {

Characterizing the identity and types of neurons in the brain, as well as their associated function, requires a means of quantifying and comparing 3D neuron morphology. Presently, neuron comparison methods are based on statistics from neuronal morphology such as size and number of branches, which are not fully suitable for detecting local similarities and differences in the detailed structure. We developed BlastNeuron to compare neurons in terms of their global appearance, detailed arborization patterns, and topological similarity. BlastNeuron first compares and clusters 3D neuron reconstructions based on global morphology features and moment invariants, independent of their orientations, sizes, level of reconstruction and other variations. Subsequently, BlastNeuron performs local alignment between any pair of retrieved neurons via a tree-topology driven dynamic programming method. A 3D correspondence map can thus be generated at the resolution of single reconstruction nodes. We applied BlastNeuron to three datasets: (1) 10,000+ neuron reconstructions from a public morphology database, (2) 681 newly and manually reconstructed neurons, and (3) neurons reconstructions produced using several independent reconstruction methods. Our approach was able to accurately and efficiently retrieve morphologically and functionally similar neuron structures from large morphology database, identify the local common structures, and find clusters of neurons that share similarities in both morphology and molecular profiles.

}, issn = {1559-0089}, doi = {10.1007/s12021-015-9272-7}, author = {Wan, Yinan and Long, Fuhui and Qu, Lei and Xiao, Hang and Hawrylycz, Michael and Myers, Eugene W and Peng, Hanchuan} } @conference {47229, title = {Interactive exemplar-based segmentation toolkit for biomedical image analysis.}, booktitle = {International Symposium on Biomedical Imaging}, year = {2015}, month = {2015 Apr}, abstract = {

In the field of biomedical imaging analysis on single-cell level, reliable and fast segmentation of the cell nuclei from the background on three-dimensional images is highly needed for the further analysis. In this work we propose an interactive cell segmentation toolkit that first establishes a set of exemplar regions from user input through an easy and intuitive interface in both 2D and 3D in real-time, then
extracts the shape and intensity features from those exemplars. Based on a local contrast-constrained region growing scheme, each connected component in the whole image would be filtered by the features from exemplars, forming an {\textquotedblleft}exemplar-matching{\textquotedblright} group which passed the filtering and would be part of the final segmentation result, and a {\textquotedblleft}non-exemplar-matching{\textquotedblright} group in which components
would be further segmented by the gradient vector field based algorithm. The results of the filtering process are visualized back to the user in near real-time, thus enhancing the experience in exemplar selecting and parameter tuning. The toolkit is distributed as a plugin within the open source Vaa3D system (http://vaa3d.org).

}, author = {Li, Xiang and Zhou, Zhi and Keller, Philipp J and Zeng, Hongkui and Liu, Tianming and Peng, Hanchuan} } @article {45418, title = {Atlas-builder software and the eNeuro atlas: resources for developmental biology and neuroscience.}, journal = {Development}, volume = {141}, year = {2014}, month = {2014 Jun}, pages = {2524-32}, abstract = {

A major limitation in understanding embryonic development is the lack of cell type-specific markers. Existing gene expression and marker atlases provide valuable tools, but they typically have one or more limitations: a lack of single-cell resolution; an inability to register multiple expression patterns to determine their precise relationship; an inability to be upgraded by users; an inability to compare novel patterns with the database patterns; and a lack of three-dimensional images. Here, we develop new {\textquoteright}atlas-builder{\textquoteright} software that overcomes each of these limitations. A newly generated atlas is three-dimensional, allows the precise registration of an infinite number of cell type-specific markers, is searchable and is open-ended. Our software can be used to create an atlas of any tissue in any organism that contains stereotyped cell positions. We used the software to generate an {\textquoteright}eNeuro{\textquoteright} atlas of the Drosophila embryonic CNS containing eight transcription factors that mark the major CNS cell types (motor neurons, glia, neurosecretory cells and interneurons). We found neuronal, but not glial, nuclei occupied stereotyped locations. We added 75 new Gal4 markers to the atlas to identify over 50\% of all interneurons in the ventral CNS, and these lines allowed functional access to those interneurons for the first time. We expect the atlas-builder software to benefit a large proportion of the developmental biology community, and the eNeuro atlas to serve as a publicly accessible hub for integrating neuronal attributes - cell lineage, gene expression patterns, axon/dendrite projections, neurotransmitters--and linking them to individual neurons.

}, keywords = {Animals, Axons, Cell Lineage, Central Nervous System, Computational Biology, Databases, Genetic, Dendrites, Drosophila melanogaster, Drosophila Proteins, Gene Expression Profiling, Gene Expression Regulation, Developmental, Genetic Markers, Interneurons, Mice, Neurons, Neurotransmitter Agents, Rats, Software}, issn = {1477-9129}, doi = {10.1242/dev.108720}, author = {Heckscher, Ellie S and Long, Fuhui and Layden, Michael J and Chuang, Chein-Hui and Manning, Laurina and Richart, Jourdain and Pearson, Joseph C and Crews, Stephen T and Peng, Hanchuan and Myers, Eugene and Doe, Chris Q} } @article {45064, title = {Virtual finger boosts three-dimensional imaging and microsurgery as well as terabyte volume image visualization and analysis.}, journal = {Nature Communications}, volume = {5}, year = {2014}, month = {2014 Jul 11}, pages = {4342}, abstract = {

Three-dimensional (3D) bioimaging, visualization and data analysis are in strong need of powerful 3D exploration techniques. We develop virtual finger (VF) to generate 3D curves, points and regions-of-interest in the 3D space of a volumetric image with a single finger operation, such as a computer mouse stroke, or click or zoom from the 2D-projection plane of an image as visualized with a computer. VF provides efficient methods for acquisition, visualization and analysis of 3D images for roundworm, fruitfly, dragonfly, mouse, rat and human. Specifically, VF enables instant 3D optical zoom-in imaging, 3D free-form optical microsurgery, and 3D visualization and annotation of terabytes of whole-brain image volumes. VF also leads to orders of magnitude better efficiency of automated 3D reconstruction of neurons and similar biostructures over our previous systems. We use VF to generate from images of 1,107 Drosophila GAL4 lines a projectome of a Drosophila brain.

}, issn = {2041-1723}, doi = {10.1038/ncomms5342}, author = {Peng, Hanchuan and Tang, Jianyong and Xiao, Hang and Bria, Alessandro and Zhou, Jianlong and Butler, Victoria and Zhou, Zhi and Gonzalez-Bellido, Paloma T and Oh, Seung W and Chen, Jichao and Mitra, Ananya and Tsien, Richard W and Zeng, Hongkui and Ascoli, Giorgio A and Iannello, Giulio and Hawrylycz, Michael and Myers, Eugene and Long, Fuhui} } @article {6881, title = {Wiring economy can account for cell body placement across species and brain areas.}, journal = {Current biology : CB}, volume = {24}, year = {2014}, month = {2014 Feb 3}, pages = {R109-10}, abstract = {The placement of neuronal cell bodies relative to the neuropile differs among species and brain areas. Cell bodies can be either embedded as in mammalian cortex or segregated as in invertebrates and some other vertebrate brain areas. Why are there such different arrangements? Here we suggest that the observed arrangements may simply be a reflection of wiring economy, a general principle that tends to reduce the total volume of the neuropile and hence the volume of the inclusions in it. Specifically, we suggest that the choice of embedded versus segregated arrangement is determined by which neuronal component - the cell body or the neurite connecting the cell body to the arbor - has a smaller volume. Our quantitative predictions are in agreement with existing and new measurements.}, issn = {1879-0445}, doi = {10.1016/j.cub.2013.12.012}, author = {Rivera-Alba, Marta and Peng, Hanchuan and de Polavieja, Gonzalo G and Chklovskii, Dmitri B} } @article {6101, title = {APP2: automatic tracing of 3D neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree.}, journal = {Bioinformatics (Oxford, England)}, volume = {29}, year = {2013}, month = {2013 Jun 1}, pages = {1448-54}, abstract = {Tracing of neuron morphology is an essential technique in computational neuroscience. However, despite a number of existing methods, few open-source techniques are completely or sufficiently automated and at the same time are able to generate robust results for real 3D microscopy images.}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btt170}, author = {Xiao, Hang and Peng, Hanchuan} } @article {6284, title = {Clonal development and organization of the adult Drosophila central brain.}, journal = {Current biology : CB}, volume = {23}, year = {2013}, month = {2013 Apr 22}, pages = {633-43}, abstract = {BACKGROUND: The insect brain can be divided into neuropils that are formed by neurites of both local and remote origin. The complexity of the interconnections obscures how these neuropils are established and interconnected through development. The Drosophila central brain develops from a fixed number of neuroblasts (NBs) that deposit neurons in regional clusters. RESULTS: By determining individual NB clones and pursuing their projections into specific neuropils, we unravel the regional development of the brain neural network. Exhaustive clonal analysis revealed 95 stereotyped neuronal lineages with characteristic cell-body locations and neurite trajectories. Most clones show complex projection patterns, but despite the complexity, neighboring clones often coinnervate the same local neuropil or neuropils and further target a restricted set of distant neuropils. CONCLUSIONS: These observations argue for regional clonal development of both neuropils and neuropil connectivity throughout the Drosophila central brain.}, issn = {1879-0445}, doi = {10.1016/j.cub.2013.02.057}, author = {Yu, Hung-Hsiang and Awasaki, Takeshi and Schroeder, Mark David and Long, Fuhui and Yang, Jacob S and He, Yisheng and Ding, Peng and Kao, Jui-Chun and Wu, Gloria Yueh-Yi and Peng, Hanchuan and Myers, Gene and Lee, Tzumin} } @article {6100, title = {A distance-field based automatic neuron tracing method.}, journal = {BMC Bioinformatics}, volume = {14}, year = {2013}, month = {2013 Mar 12}, pages = {93}, abstract = {

Automatic 3D digital reconstruction (tracing) of neurons embedded in noisy microscopic images is challenging, especially when the cell morphology is complex.

}, issn = {1471-2105}, doi = {10.1186/1471-2105-14-93}, author = {Yang, Jinzhu and Gonzalez-Bellido, Paloma T and Peng, Hanchuan} } @article {5523, title = {Eight pairs of descending visual neurons in the dragonfly give wing motor centers accurate population vector of prey direction.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {110}, year = {2013}, month = {2013 Jan 8}, pages = {696-701}, chapter = {696}, abstract = {

Intercepting a moving object requires prediction of its future location. This complex task has been solved by dragonflies, who intercept their prey in midair with a 95\% success rate. In this study, we show that a group of 16 neurons, called target-selective descending neurons (TSDNs), code a population vector that reflects the direction of the target with high accuracy and reliability across 360{\textdegree}. The TSDN spatial (receptive field) and temporal (latency) properties matched the area of the retina where the prey is focused and the reaction time, respectively, during predatory flights. The directional tuning curves and morphological traits (3D tracings) for each TSDN type were consistent among animals, but spike rates were not. Our results emphasize that a successful neural circuit for target tracking and interception can be achieved with few neurons and that in dragonflies this information is relayed from the brain to the wing motor centers in population vector form.

}, issn = {1091-6490}, doi = {10.1073/pnas.1210489109}, author = {Gonzalez-Bellido, Paloma T and Peng, Hanchuan and Yang, Jinzhu and Georgopoulos, Apostolos P and Olberg, Robert M} } @article {4974, title = {Bioimage informatics: a new category in Bioinformatics.}, journal = {Bioinformatics}, volume = {28}, year = {2012}, month = {2012 Apr 15}, pages = {1057}, issn = {1367-4811}, doi = {10.1093/bioinformatics/bts111}, author = {Peng, Hanchuan and Bateman, Alex and Valencia, Alfonso and Wren, Jonathan D} } @article {4984, title = {Biological imaging software tools.}, journal = {Nature Methods}, volume = {9}, year = {2012}, month = {2012 Jun 28}, pages = {697-710}, abstract = {

Few technologies are more widespread in modern biological laboratories than imaging. Recent advances in optical technologies and instrumentation are providing hitherto unimagined capabilities. Almost all these advances have required the development of software to enable the acquisition, management, analysis and visualization of the imaging data. We review each computational step that biologists encounter when dealing with digital images, the inherent challenges and the overall status of available software for bioimage informatics, focusing on open-source options.

}, issn = {1548-7105}, doi = {10.1038/nmeth.2084}, author = {Eliceiri, Kevin W and Berthold, Michael R and Goldberg, Ilya G and Ib{\'a}{\~n}ez, Luis and Manjunath, B S and Martone, Maryann E and Murphy, Robert F and Peng, Hanchuan and Plant, Anne L and Roysam, Badrinath and Stuurmann, Nico and Swedlow, Jason R and Tomancak, Pavel and Carpenter, Anne E.} } @article {5423, title = {A GAL4-driver line resource for Drosophila neurobiology.}, journal = {Cell Reports}, volume = {2}, year = {2012}, month = {2012 Oct 25}, pages = {991-1001}, abstract = {

We established a collection of 7,000 transgenic lines of Drosophila melanogaster. Expression of GAL4 in each line is controlled by a different, defined fragment of genomic DNA that serves as a transcriptional enhancer. We used confocal microscopy of dissected nervous systems to determine the expression patterns driven by each fragment in the adult brain and ventral nerve cord. We present image data on 6,650 lines. Using both manual and machine-assisted annotation, we describe the expression patterns in the most useful lines. We illustrate the utility of these data for identifying novel neuronal cell types, revealing brain asymmetry, and describing the nature and extent of neuronal shape stereotypy. The GAL4 lines allow expression of exogenous genes in distinct, small subsets of the adult nervous system. The set of DNA fragments, each driving a documented expression pattern, will facilitate the generation of additional constructs for manipulating neuronal function. synapse was substantially elevated, at the endocytic zone there was no enhanced polymerization activity. We conclude that actin subserves spatially diverse, independently regulated processes throughout spines. Perisynaptic actin forms a uniquely dynamic structure well suited for direct, active regulation of the synapse.

For the overall strategy and methods used to produce the GAL4 lines:
Pfeiffer, B.D., Jenett, A., Hammonds, A.S., Ngo, T.T., Misra, S., Murphy, C., Scully, A., Carlson, J.W., Wan, K.H., Laverty, T.R., Mungall, C., Svirskas, R., Kadonaga, J.T., Doe, C.Q., Eisen, M.B., Celniker, S.E., Rubin, G.M. (2008). Tools for neuroanatomy and neurogenetics in Drosophila. Proc. Natl. Acad. Sci. USA 105, 9715-9720. http://www.pnas.org/content/105/28/9715.full.pdf+html synapse was substantially elevated, at the endocytic zone there was no enhanced polymerization activity. We conclude that actin subserves spatially diverse, independently regulated processes throughout spines. Perisynaptic actin forms a uniquely dynamic structure well suited for direct, active regulation of the synapse.

For data on expression in the embryo:
Manning, L., Purice, M.D., Roberts, J., Pollard, J.L., Bennett, A.L., Kroll, J.R., Dyukareva, A.V., Doan, P.N., Lupton, J.R., Strader, M.E., Tanner, S., Bauer, D., Wilbur, A., Tran, K.D., Laverty, T.R., Pearson, J.C., Crews, S.T., Rubin, G.M., and Doe, C.Q. (2012) Annotated embryonic CNS expression patterns of 5000 GMR GAL4 lines: a resource for manipulating gene expression and analyzing cis-regulatory motifs. Cell Reports (2012) Doi: 10.1016/j.celrep.2012.09.009 http://www.cell.com/cell-reports/fulltext/S2211-1247(12)00290-2 synapse was substantially elevated, at the endocytic zone there was no enhanced polymerization activity. We conclude that actin subserves spatially diverse, independently regulated processes throughout spines. Perisynaptic actin forms a uniquely dynamic structure well suited for direct, active regulation of the synapse.

For data on expression in imaginal discs:
Jory, A., Estella, C., Giorgianni, M.W., Slattery, M., Laverty, T.R., Rubin, G.M., and Mann, R.S. (2012) A survey of 6300 genomic fragments for cis-regulatory activity in the imaginal discs of Drosophila melanogaster. Cell Reports (2012) Doi: 10.1016/j.celrep.2012.09.010 http://www.cell.com/cell-reports/fulltext/S2211-1247(12)00291-4 synapse was substantially elevated, at the endocytic zone there was no enhanced polymerization activity. We conclude that actin subserves spatially diverse, independently regulated processes throughout spines. Perisynaptic actin forms a uniquely dynamic structure well suited for direct, active regulation of the synapse.

For data on expression in the larval nervous system:
Li, H.-H., Kroll, J.R., Lennox, S., Ogundeyi, O., Jeter, J., Depasquale, G., and Truman, J.W. (2013) A GAL4 driver resource for developmental and behavioral studies on the larval CNS of Drosophila. Cell Reports (submitted).

}, issn = {2211-1247}, doi = {10.1016/j.celrep.2012.09.011}, author = {Jenett, Arnim and Rubin, Gerald M and Ngo, Teri-T B and Shepherd, David and Murphy, Christine and Dionne, Heather and Pfeiffer, Barret D and Cavallaro, Amanda and Hall, Donald and Jeter, Jennifer and Iyer, Nirmala and Fetter, Dona and Hausenfluck, Joanna H and Peng, Hanchuan and Trautman, Eric T and Svirskas, Robert R and Myers, Eugene W and Iwinski, Zbigniew R and Aso, Yoshinori and Depasquale, Gina M and Enos, Adrianne and Hulamm, Phuson and Lam, Shing Chun Benny and Li, Hsing-Hsi and Laverty, Todd R and Long, Fuhui and Qu, Lei and Murphy, Sean D and Rokicki, Konrad and Safford, Todd and Shaw, Kshiti and Simpson, Julie H and Sowell, Allison and Tae, Susana and Yu, Yang and Zugates, Christopher T} } @article {4487, title = {mGRASP enables mapping mammalian synaptic connectivity with light microscopy.}, journal = {Nature Methods}, volume = {9}, year = {2012}, month = {2012 Jan}, pages = {96-102}, abstract = {

The GFP reconstitution across synaptic partners (GRASP) technique, based on functional complementation between two nonfluorescent GFP fragments, can be used to detect the location of synapses quickly, accurately and with high spatial resolution. The method has been previously applied in the nematode and the fruit fly but requires substantial modification for use in the mammalian brain. We developed mammalian GRASP (mGRASP) by optimizing transmembrane split-GFP carriers for mammalian synapses. Using in silico protein design, we engineered chimeric synaptic mGRASP fragments that were efficiently delivered to synaptic locations and reconstituted GFP fluorescence in vivo. Furthermore, by integrating molecular and cellular approaches with a computational strategy for the three-dimensional reconstruction of neurons, we applied mGRASP to both long-range circuits and local microcircuits in the mouse hippocampus and thalamocortical regions, analyzing synaptic distribution in single neurons and in dendritic compartments.

}, issn = {1548-7105}, doi = {10.1038/nmeth.1784}, author = {Kim, Jinhyun and Zhao, Ting and Petralia, Ronald S and Yu, Yang and Peng, Hanchuan and Myers, Eugene and Magee, Jeffrey C} } @article {4975, title = {Visualization and analysis of 3D microscopic images.}, journal = {PLoS Computational Biology}, volume = {8}, year = {2012}, month = {2012 Jun}, pages = {e1002519}, abstract = {

In a wide range of biological studies, it is highly desirable to visualize and analyze three-dimensional (3D) microscopic images. In this primer, we first introduce several major methods for visualizing typical 3D images and related multi-scale, multi-time-point, multi-color data sets. Then, we discuss three key categories of image analysis tasks, namely segmentation, registration, and annotation. We demonstrate how to pipeline these visualization and analysis modules using examples of profiling the single-cell gene-expression of C. elegans and constructing a map of stereotyped neurite tracts in a fruit fly brain.

}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1002519}, author = {Long, Fuhui and Zhou, Jianlong and Peng, Hanchuan} } @article {4310, title = {Anisotropic path searching for automatic neuron reconstruction.}, journal = {Medical Image Analysis}, volume = {15}, year = {2011}, month = {2011 Oct}, pages = {680-9}, abstract = {

Full reconstruction of neuron morphology is of fundamental interest for the analysis and understanding of their functioning. We have developed a novel method capable of automatically tracing neurons in three-dimensional microscopy data. In contrast to template-based methods, the proposed approach makes no assumptions about the shape or appearance of neurite structure. Instead, an efficient seeding approach is applied to capture complex neuronal structures and the tracing problem is solved by computing the optimal reconstruction with a weighted graph. The optimality is determined by the cost function designed for the path between each pair of seeds and by topological constraints defining the component interrelations and completeness. In addition, an automated neuron comparison method is introduced for performance evaluation and structure analysis. The proposed algorithm is computationally efficient and has been validated using different types of microscopy data sets including Drosophila{\textquoteright}s projection neurons and fly neurons with presynaptic sites. In all cases, the approach yielded promising results.

}, issn = {1361-8423}, doi = {10.1016/j.media.2011.05.013}, author = {Xie, Jun and Zhao, Ting and Lee, Tzumin and Myers, Eugene and Peng, Hanchuan} } @article {5375, title = {Automated high speed stitching of large 3D microscopic images.}, journal = {2011 8TH IEEE International Symposium on Biomedical Imaging: From Nano to Macro}, year = {2011}, month = {2011 Mar 30}, pages = {238-41}, abstract = {

High-resolution microscopic imaging of biological samples often produces multiple 3D image tiles to cover a large field of view of specimen. Usually each tile has a large size, in the range of hundreds of megabytes to several gigabytes. For many of our image data sets, existing software tools are often unable to stitch those 3D tiles into a panoramic view, thus impede further data analysis. We propose a simple, but accurate, robust, and automatic method to stitch a group of image tiles without a priori adjacency information of them. We first use a multiscale strategy to register a pair of 3D image tiles rapidly, achieving about 8~10 times faster speed and 10 times less memory requirement compared to previous methods. Then we design a minimum-spanning-tree based method to determine the optimal adjacency of tiles. We have successfully stitched large image stacks of model animals including C. elegans, fruit fly, dragonfly, and mouse, which could not be stitched by several existing methods.

}, issn = {1945-7928}, doi = {10.1109/isbi.2011.5872396}, author = {Yu, Yang and Peng, Hanchuan} } @article {2700, title = {Automated reconstruction of neuronal morphology based on local geometrical and global structural models.}, journal = {Neuroinformatics}, volume = {9}, year = {2011}, month = {2011 May 6}, pages = {247-61}, abstract = {

Digital reconstruction of neurons from microscope images is an important and challenging problem in neuroscience. In this paper, we propose a model-based method to tackle this problem. We first formulate a model structure, then develop an algorithm for computing it by carefully taking into account morphological characteristics of neurons, as well as the image properties under typical imaging protocols. The method has been tested on the data sets used in the DIADEM competition and produced promising results for four out of the five data sets.

}, issn = {1559-0089}, doi = {10.1007/s12021-011-9120-3}, author = {Zhao, Ting and Xie, Jun and Amat, Fernando and Clack, Nathan and Ahammad, Parvez and Peng, Hanchuan and Long, Fuhui and Myers, Eugene} } @conference {2721, title = {Automatic 3D neuron tracing using all-paths pruning.}, booktitle = {Conference on Intelligent Systems for Molecular Biology}, year = {2011}, month = {2011}, address = {Vienna}, abstract = {

Motivation: Digital reconstruction, or tracing, of 3D neuron structures is critical toward reverse engineering the wiring and functions of a brain. However, despite a number of existing studies, this task is still challenging, especially when a 3D microscopic image has low signal-to-noise ratio (SNR) and fragmented neuron segments. Published work can handle these hard situations only by introducing global prior information, such as where a neurite segment starts and terminates. However, manual incorporation of such global information can be very time consuming. Thus, a completely automatic approach for these hard situations is highly desirable.

Results: We have developed an automatic graph algorithm, called the all-path pruning (APP), to trace the 3D structure of a neuron. To avoid potential mis-tracing of some parts of a neuron, an APP first produces an initial over-reconstruction, by tracing the optimal geodesic shortest path from the seed location to every possible destination voxel/pixel location in the image. Since the initial reconstruction contains all the possible paths and thus could contain redundant structural components (SC), we simplify the entire reconstruction without compromising its connectedness by pruning the redundant structural elements, using a new maximal-covering minimal-redundant (MCMR) subgraph algorithm. We show that MCMR has a linear computational complexity and will converge. We examined the performance of our method using challenging 3D neuronal image datasets of model organisms (e.g. fruit fly).

}, author = {Long, Fuhui and Peng, Hanchuan and Myers, Eugene} } @article {4157, title = {BrainAligner: 3D registration atlases of Drosophila brains.}, journal = {Nature Methods}, volume = {8}, year = {2011}, month = {2011 Jun}, pages = {493-500}, abstract = {

Analyzing Drosophila melanogaster neural expression patterns in thousands of three-dimensional image stacks of individual brains requires registering them into a canonical framework based on a fiducial reference of neuropil morphology. Given a target brain labeled with predefined landmarks, the BrainAligner program automatically finds the corresponding landmarks in a subject brain and maps it to the coordinate system of the target brain via a deformable warp. Using a neuropil marker (the antibody nc82) as a reference of the brain morphology and a target brain that is itself a statistical average of data for 295 brains, we achieved a registration accuracy of 2 μm on average, permitting assessment of stereotypy, potential connectivity and functional mapping of the adult fruit fly brain. We used BrainAligner to generate an image pattern atlas of 2954 registered brains containing 470 different expression patterns that cover all the major compartments of the fly brain.

}, keywords = {Algorithms, Animals, Brain, Computer-Assisted, Drosophila melanogaster, Drosophila Proteins, Gene Expression, Genetically Modified, Green Fluorescent Proteins, Image Processing, Neuropil, Recombinant Proteins, Software, Transcription Factors}, issn = {1548-7105}, doi = {10.1038/nmeth.1602}, author = {Peng, Hanchuan and Chung, Phuong and Long, Fuhui and Qu, Lei and Jenett, Arnim and Seeds, Andrew M and Myers, Eugene W and Simpson, Julie H} } @article {5297, title = {Proof-editing is the bottleneck of 3D neuron reconstruction: the problem and solutions.}, journal = {Neuroinformatics}, volume = {9}, year = {2011}, month = {2011 Sep}, pages = {103-5}, keywords = {Algorithms, Animals, Computer-Assisted, Data Mining, Humans, Image Cytometry, Image Processing, Imaging, Neuroanatomical Tract-Tracing Techniques, Neurons, Software, Software Design, Three-Dimensional}, issn = {1559-0089}, doi = {10.1007/s12021-010-9090-x}, author = {Peng, Hanchuan and Long, Fuhui and Zhao, Ting and Myers, Eugene} } @article {3839, title = {Simultaneous recognition and segmentation of cells: application in C. elegans.}, journal = {Bioinformatics}, volume = {27}, year = {2011}, month = {2011 Aug 17}, pages = {2895-902}, abstract = {

MOTIVATION: Automatic recognition of cell identities is critical for quantitative measurement, targeting, and manipulation of cells of model animals at single-cell resolution. It has been shown to be a powerful tool for studying gene expression and regulation, cell lineages, and cell fates. Existing methods first segment cells, before applying a recognition algorithm in the second step. As a result, the segmentation errors in the first step directly affect and complicate the subsequent cell recognition step. Moreover, in new experimental settings, some of the image features that have been previously relied upon to recognize cells may not be easy to reproduce, due to limitations on the number of color channels available for fluorescent imaging or to the cost of building transgenic animals. An approach that is more accurate and relies on only a single signal channel is clearly desirable. RESULTS: We have developed a new method, called SRS (for Simultaneous Recognition and Segmentation of cells), and applied it to 3D image stacks of the model organism C. elegans. Given a 3D image stack of the animal and a 3D atlas of target cells, SRS is effectively an atlas-guided voxel classification process: cell recognition is realized by smoothly deforming the atlas to best fit the image, where the segmentation is obtained naturally via classification of all image voxels. The method achieved a 97.7\% overall recognition accuracy in recognizing a key class of marker cells, the body wall muscle (BWM) cells, on a data set of 175 C. elegans image stacks containing 14,118 manually curated BWM cells providing the "ground-truth" for accuracy. This result was achieved without any additional fiducial image features. SRS also automatically identified 14 of the image stacks as involving {\textpm}90-degree rotations. With these stacks excluded from the data set, the recognition accuracy rose to 99.1\%. We also show SRS is generally applicable to other cell-types, e.g. intestinal cells. AVAILABILITY: The supplementary movies can be downloaded from our website http://penglab.janelia.org/proj/celegans_seganno. The method has been implemented as a plug-in program within the V3D system (http://penglab.janelia.org/proj/v3d) and will be released in the V3D plugin source code repository.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btr480}, author = {Qu, Lei and Long, Fuhui and Liu, Xiao and Kim, Stuart and Myers, Eugene and Peng, Hanchuan} } @article {2698, title = {Automatic neuron tracing in volumetric microscopy images with anisotropic path searching.}, journal = {Medical Image Computing and Computer-Assisted Intervention: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention}, volume = {13}, year = {2010}, month = {2010}, pages = {472-9}, abstract = {

Full reconstruction of neuron morphology is of fundamental interest for the analysis and understanding of neuron function. We have developed a novel method capable of tracing neurons in three-dimensional microscopy data automatically. In contrast to template-based methods, the proposed approach makes no assumptions on the shape or appearance of neuron{\textquoteright}s body. Instead, an efficient seeding approach is applied to find significant pixels almost certainly within complex neuronal structures and the tracing problem is solved by computing an graph tree structure connecting these seeds. In addition, an automated neuron comparison method is introduced for performance evaluation and structure analysis. The proposed algorithm is computationally efficient. Experiments on different types of data show promising results.

}, keywords = {Algorithms, Animals, Anisotropy, Artificial Intelligence, Automated, Cells, Computer-Assisted, Cultured, Humans, Image Enhancement, Image Interpretation, Imaging, Microscopy, Neurons, Pattern Recognition, Reproducibility of Results, Sensitivity and Specificity, Three-Dimensional}, author = {Xie, Jun and Zhao, Ting and Lee, Tzumin and Myers, Eugene and Peng, Hanchuan} } @article {2183, title = {Automatic reconstruction of 3D neuron structures using a graph-augmented deformable model.}, journal = {Bioinformatics}, volume = {26}, year = {2010}, month = {2010 Jun 15}, pages = {i38-46}, abstract = {

Digital reconstruction of 3D neuron structures is an important step toward reverse engineering the wiring and functions of a brain. However, despite a number of existing studies, this task is still challenging, especially when a 3D microscopic image has low single-to-noise ratio and discontinued segments of neurite patterns.

}, keywords = {Animals, Caenorhabditis elegans, Computer-Assisted, Drosophila, Image Processing, Imaging, Mice, Neurons, Three-Dimensional}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btq212}, author = {Peng, Hanchuan and Ruan, Zongcai and Atasoy, Deniz and Sternson, Scott} } @article {2146, title = {A principal skeleton algorithm for standardizing confocal images of fruit fly nervous systems.}, journal = {Bioinformatics}, volume = {26}, year = {2010}, month = {2010 Apr 15}, pages = {1091-7}, abstract = {

The fruit fly (Drosophila melanogaster) is a commonly used model organism in biology. We are currently building a 3D digital atlas of the fruit fly larval nervous system (LNS) based on a large collection of fly larva GAL4 lines, each of which targets a subset of neurons. To achieve such a goal, we need to automatically align a number of high-resolution confocal image stacks of these GAL4 lines. One commonly employed strategy in image pattern registration is to first globally align images using an affine transform, followed by local non-linear warping. Unfortunately, the spatially articulated and often twisted LNS makes it difficult to globally align the images directly using the affine method. In a parallel project to build a 3D digital map of the adult fly ventral nerve cord (VNC), we are confronted with a similar problem.

}, keywords = {Algorithms, Animals, Cell Line, Computer-Assisted, Confocal, Drosophila melanogaster, Image Processing, Imaging, Microscopy, Nervous System, Three-Dimensional}, issn = {1367-4811}, doi = {10.1007/s12021-010-9090-x}, author = {Qu, Lei and Peng, Hanchuan} } @article {2696, title = {Segmentation of center brains and optic lobes in 3D confocal images of adult fruit fly brains.}, journal = {Methods}, volume = {50}, year = {2010}, month = {2010 Feb}, pages = {63-9}, abstract = {

Automatic alignment (registration) of 3D images of adult fruit fly brains is often influenced by the significant displacement of the relative locations of the two optic lobes (OLs) and the center brain (CB). In one of our ongoing efforts to produce a better image alignment pipeline of adult fruit fly brains, we consider separating CB and OLs and align them independently. This paper reports our automatic method to segregate CB and OLs, in particular under conditions where the signal to noise ratio (SNR) is low, the variation of the image intensity is big, and the relative displacement of OLs and CB is substantial. We design an algorithm to find a minimum-cost 3D surface in a 3D image stack to best separate an OL (of one side, either left or right) from CB. This surface is defined as an aggregation of the respective minimum-cost curves detected in each individual 2D image slice. Each curve is defined by a list of control points that best segregate OL and CB. To obtain the locations of these control points, we derive an energy function that includes an image energy term defined by local pixel intensities and two internal energy terms that constrain the curve{\textquoteright}s smoothness and length. Gradient descent method is used to optimize this energy function. To improve both the speed and robustness of the method, for each stack, the locations of optimized control points in a slice are taken as the initialization prior for the next slice. We have tested this approach on simulated and real 3D fly brain image stacks and demonstrated that this method can reasonably segregate OLs from CBs despite the aforementioned difficulties.

}, keywords = {Algorithms, Animals, Automation, Brain, Brain Mapping, Computer Graphics, Computer Simulation, Computer-Assisted, Confocal, Drosophila melanogaster, Image Enhancement, Image Interpretation, Imaging, Microscopy, Models, Nonmammalian, Optic Lobe, Reproducibility of Results, Statistical, Three-Dimensional}, issn = {1095-9130}, doi = {10.1016/j.ymeth.2009.08.004}, author = {Lam, Shing Chun Benny and Ruan, Zongcai and Zhao, Ting and Long, Fuhui and Jenett, Arnim and Simpson, Julie and Myers, Eugene W and Peng, Hanchuan} } @article {2328, title = {VAA3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets.}, journal = {Nature Biotechnology}, volume = {28}, year = {2010}, month = {2010 Apr}, pages = {348-53}, abstract = {

The V3D system provides three-dimensional (3D) visualization of gigabyte-sized microscopy image stacks in real time on current laptops and desktops. V3D streamlines the online analysis, measurement and proofreading of complicated image patterns by combining ergonomic functions for selecting a location in an image directly in 3D space and for displaying biological measurements, such as from fluorescent probes, using the overlaid surface objects. V3D runs on all major computer platforms and can be enhanced by software plug-ins to address specific biological problems. To demonstrate this extensibility, we built a V3D-based application, V3D-Neuron, to reconstruct complex 3D neuronal structures from high-resolution brain images. V3D-Neuron can precisely digitize the morphology of a single neuron in a fruitfly brain in minutes, with about a 17-fold improvement in reliability and tenfold savings in time compared with other neuron reconstruction tools. Using V3D-Neuron, we demonstrate the feasibility of building a 3D digital atlas of neurite tracts in the fruitfly brain.

}, keywords = {Computer Graphics, Computer-Assisted, Databases, Factual, Image Interpretation, Imaging, Information Storage and Retrieval, Microscopy, Radiology Information Systems, Three-Dimensional, User-Computer Interface}, issn = {1546-1696}, doi = {10.1038/nbt.1612}, author = {Peng, Hanchuan and Ruan, Zongcai and Long, Fuhui and Simpson, Julie H and Myers, Eugene W} } @article {2149, title = {A 3D digital atlas of C. elegans and its application to single-cell analyses.}, journal = {Nature Methods}, volume = {6}, year = {2009}, month = {2009 Sep}, pages = {667-72}, abstract = {

We built a digital nuclear atlas of the newly hatched, first larval stage (L1) of the wild-type hermaphrodite of Caenorhabditis elegans at single-cell resolution from confocal image stacks of 15 individual worms. The atlas quantifies the stereotypy of nuclear locations and provides other statistics on the spatial patterns of the 357 nuclei that could be faithfully segmented and annotated out of the 558 present at this developmental stage. We then developed an automated approach to assign cell names to each nucleus in a three-dimensional image of an L1 worm. We achieved 86\% accuracy in identifying the 357 nuclei automatically. This computational method will allow high-throughput single-cell analyses of the post-embryonic worm, such as gene expression analysis, or ablation or stimulation of cells under computer control in a high-throughput functional screen.

}, keywords = {Animals, Automatic Data Processing, Biological, Caenorhabditis elegans, Cell Lineage, Cell Nucleus, Models}, issn = {1548-7105}, doi = {10.1007/s12021-010-9090-x}, author = {Long, Fuhui and Peng, Hanchuan and Liu, Xiao and Kim, Stuart K and Myers, Eugene} } @article {2697, title = {Analysis of cell fate from single-cell gene expression profiles in C. elegans.}, journal = {Cell}, volume = {139}, year = {2009}, month = {2009 Oct 30}, pages = {623-33}, abstract = {

The C. elegans cell lineage provides a unique opportunity to look at how cell lineage affects patterns of gene expression. We developed an automatic cell lineage analyzer that converts high-resolution images of worms into a data table showing fluorescence expression with single-cell resolution. We generated expression profiles of 93 genes in 363 specific cells from L1 stage larvae and found that cells with identical fates can be formed by different gene regulatory pathways. Molecular signatures identified repeating cell fate modules within the cell lineage and enabled the generation of a molecular differentiation map that reveals points in the cell lineage when developmental fates of daughter cells begin to diverge. These results demonstrate insights that become possible using computational approaches to analyze quantitative expression from many genes in parallel using a digital gene expression atlas.

}, keywords = {Animals, Caenorhabditis elegans, Caenorhabditis elegans Proteins, Cell Differentiation, Cell Lineage, Gene Expression Profiling}, issn = {1097-4172}, doi = {10.1016/j.cell.2009.08.044}, author = {Liu, Xiao and Long, Fuhui and Peng, Hanchuan and Aerni, Sarah J and Jiang, Min and S{\'a}nchez-Blanco, Adolfo and Murray, John I and Preston, Elicia and Mericle, Barbara and Batzoglou, Serafim and Myers, Eugene W and Kim, Stuart K} } @article {2148, title = {Imaging informatics for personalised medicine: applications and challenges.}, journal = {International Journal of Functional Informatics and Personalised Medicine}, volume = {2}, year = {2009}, month = {2009}, pages = {125-35}, abstract = {

Imaging informatics has emerged as a major research theme in biomedicine in the last few decades. Currently, personalised, predictive and preventive patient care is believed to be one of the top priorities in biomedical research and practice. Imaging informatics plays a major role in biomedicine studies. This paper reviews main applications and challenges of imaging informatics in biomedicine.

}, issn = {1756-2112}, doi = {10.1007/s12021-010-9090-x}, author = {Liu, Tianming and Peng, Hanchuan and Zhou, Xiaobo} } @article {2331, title = {VANO: a volume-object image annotation system.}, journal = {Bioinformatics}, volume = {25}, year = {2009}, month = {2009 Mar 1}, pages = {695-7}, abstract = {

Volume-object annotation system (VANO) is a cross-platform image annotation system that enables one to conveniently visualize and annotate 3D volume objects including nuclei and cells. An application of VANO typically starts with an initial collection of objects produced by a segmentation computation. The objects can then be labeled, categorized, deleted, added, split, merged and redefined. VANO has been used to build high-resolution digital atlases of the nuclei of Caenorhabditis elegans at the L1 stage and the nuclei of Drosophila melanogaster{\textquoteright}s ventral nerve cord at the late embryonic stage. AVAILABILITY: Platform independent executables of VANO, a sample dataset, and a detailed description of both its design and usage are available at research.janelia.org/peng/proj/vano. VANO is open-source for co-development.

}, keywords = {Animals, Caenorhabditis elegans, Computer-Assisted, Databases, Drosophila, Factual, Image Processing, Software, User-Computer Interface}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btp046}, author = {Peng, Hanchuan and Long, Fuhui and Myers, Eugene W} } @conference {2719, title = {Automatic recognition of cells (ARC) for 3D images of C. elegans.}, booktitle = {Proceedings of the 2008 Conference on Computational Molecular Biology (RECOMB) (Singapore, 2008)}, year = {2008}, month = {2008 Apr 2}, abstract = {

The development of high-resolution microscopy makes possible the high-throughput screening of cellular information, such as gene expression at single cell resolution. One of the critical enabling techniques yet to be developed is the automatic recognition or annotation of specific cells in a 3D image stack. In this paper, we present a novel graph-based algorithm, ARC, that determines cell identities in a 3D confocal image of C. elegans based on their highly stereotyped arrangement. This is an essential step in our work on gene expression analysis of C. elegans at the resolution of single cells. Our ARC method integrates both the absolute and relative spatial locations of cells in a C. elegans body. It uses a marker-guided, spatially-constrained, two-stage bipartite matching to find the optimal match between cells in a subject image and cells in 15 template images that have been manually annotated and vetted. We applied ARC to the recognition of cells in 3D confocal images of the first larval stage (L1) of C. elegans hermaphrodites, and achieved an average accuracy of 94.91\%.

}, author = {Long, Fuhui and Peng, Hanchuan and Liu, X and Kim, Stuart K and Myers, Eugene} } @article {2151, title = {Bioimage informatics: a new area of engineering biology.}, journal = {Bioinformatics}, volume = {24}, year = {2008}, month = {2008 Sep 1}, pages = {1827-36}, abstract = {

In recent years, the deluge of complicated molecular and cellular microscopic images creates compelling challenges for the image computing community. There has been an increasing focus on developing novel image processing, data mining, database and visualization techniques to extract, compare, search and manage the biological knowledge in these data-intensive problems. This emerging new area of bioinformatics can be called {\textquoteright}bioimage informatics{\textquoteright}. This article reviews the advances of this field from several aspects, including applications, key techniques, available tools and resources. Application examples such as high-throughput/high-content phenotyping and atlas building for model organisms demonstrate the importance of bioimage informatics. The essential techniques to the success of these applications, such as bioimage feature identification, segmentation and tracking, registration, annotation, mining, image data management and visualization, are further summarized, along with a brief overview of the available bioimage databases, analysis tools and other resources.

}, keywords = {Biological, Biomedical Engineering, Computational Biology, Computer Graphics, Computer Simulation, Diagnostic Imaging, Models, User-Computer Interface}, issn = {1367-4811}, doi = {10.1007/s12021-010-9090-x}, author = {Peng, Hanchuan} } @article {5349, title = {Data-driven decomposition for multi-class classification.}, journal = {Pattern Recognition}, volume = {41}, year = {2008}, month = {2007 Jun 19}, pages = {67-76}, abstract = {

This paper presents a new study on a method of designing a multi-class classifier: Data-driven Error Correcting Output Coding (DECOC). DECOC is based on the principle of Error Correcting Output Coding (ECOC), which uses a code matrix to decompose a multi-class problem into multiple binary problems. ECOC for multi-class classification hinges on the design of the code matrix. We propose to explore the distribution of data classes and optimize both the composition and the number of base learners to design an effective and compact code matrix. Two real world applications are studied: (1) the holistic recognition (i.e., recognition without segmentation) of touching handwritten numeral pairs and (2) the classification of cancer tissue types based on microarray gene expression data. The results show that the proposed DECOC is able to deliver competitive accuracy compared with other ECOC methods, using parsimonious base learners than the pairwise coupling (one-vs-one) decomposition scheme. With a rejection scheme defined by a simple robustness measure, high reliabilities of around 98\% are achieved in both applications.

}, doi = {10.1016/j.patcog.2007.05.020}, author = {Zhou, Jie and Peng, Hanchuan and Suen, Ching Y.} } @article {2695, title = {Straightening caenorhabditis elegans images.}, journal = {Bioinformatics}, volume = {24}, year = {2008}, month = {2008 Jan 15}, pages = {234-42}, abstract = {

MOTIVATION: Caenorhabditis elegans, a roundworm found in soil, is a widely studied model organism with about 1000 cells in the adult. Producing high-resolution fluorescence images of C.elegans to reveal biological insights is becoming routine, motivating the development of advanced computational tools for analyzing the resulting image stacks. For example, worm bodies usually curve significantly in images. Thus one must {\textquoteright}straighten{\textquoteright} the worms if they are to be compared under a canonical coordinate system. RESULTS: We develop a worm straightening algorithm (WSA) that restacks cutting planes orthogonal to a {\textquoteright}backbone{\textquoteright} that models the anterior-posterior axis of the worm. We formulate the backbone as a parametric cubic spline defined by a series of control points. We develop two methods for automatically determining the locations of the control points. Our experimental methods show that our approaches effectively straighten both 2D and 3D worm images.

}, keywords = {Algorithms, Animals, Caenorhabditis elegans, Computer-Assisted, Fluorescence, Image Enhancement, Image Interpretation, Imaging, Microscopy, Three-Dimensional}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btm569}, author = {Peng, Hanchuan and Long, Fuhui and Liu, Xiao and Kim, Stuart K and Myers, Eugene W} } @article {2154, title = {Automatic image analysis for gene expression patterns of fly embryos.}, journal = {BMC Cell Biology}, volume = {8}, year = {2007}, month = {2007 Jul 10}, pages = {S7}, abstract = {

Staining the mRNA of a gene via in situ hybridization (ISH) during the development of a D. melanogaster embryo delivers the detailed spatio-temporal pattern of expression of the gene. Many biological problems such as the detection of co-expressed genes, co-regulated genes, and transcription factor binding motifs rely heavily on the analyses of these image patterns. The increasing availability of ISH image data motivates the development of automated computational approaches to the analysis of gene expression patterns.

}, keywords = {Algorithms, Animals, Computational Biology, Computer-Assisted, Drosophila, Embryo, Gene Expression Profiling, Genes, Image Processing, In Situ Hybridization, Insect, Nonmammalian, Reproducibility of Results, Software Design}, issn = {1471-2121}, doi = {10.1007/s12021-010-9090-x}, author = {Peng, Hanchuan and Long, Fuhui and Zhou, Jie and Leung, Garmay and Eisen, Michael B and Myers, Eugene W} } @article {2156, title = {Automatic recognition and annotation of gene expression patterns of fly embryos.}, journal = {Bioinformatics}, volume = {23}, year = {2007}, month = {2007 Mar 1}, pages = {589-96}, abstract = {

Gene expression patterns obtained by in situ mRNA hybridization provide important information about different genes during Drosophila embryogenesis. So far, annotations of these images are done by manually assigning a subset of anatomy ontology terms to an image. This time-consuming process depends heavily on the consistency of experts.

}, keywords = {Algorithms, Animals, Computational Biology, Drosophila, Embryo, Gene Expression, In Situ Hybridization, Messenger, Nonmammalian, RNA}, issn = {1367-4811}, doi = {10.1007/s12021-010-9090-x}, author = {Zhou, Jie and Peng, Hanchuan} } @article {2718, title = {Automatic segmentation of nuclei in 3D microscopy images of C. elegans.}, journal = {2007 4TH IEEE International Symposium on Biomedical Imaging: Macro to Nano, VOLS 1-3}, year = {2007}, month = {2007 Apr 12-15}, pages = {536-9}, abstract = {

Automatic segmentation of nuclei in 3D microscopy images is essential for many biological studies including high throughput analysis of gene expression level, morphology, and phenotypes in single cell level. The complexity and variability of the microscopy images present many difficulties to the traditional image segmentation methods. In this paper, we present a new method based on 3D watershed algorithm to segment such images. By using both the intensity information of the image and the geometry information of the appropriately detected foreground mask, our method is robust to intensity fluctuation within nuclei and at the same time sensitive to the intensity and geometrical cues between nuclei. Besides, the method can automatically correct potential segmentation errors by using several post-processing steps. We tested this algorithm on the 3D confocal images of C.elegans, an organism that has been widely used in biological studies. Our results show that the algorithm can segment nuclei in high accuracy despite the non-uniform background, tightly clustered nuclei with different sizes and shapes, fluctuated intensities, and hollow-shaped staining patterns in the images.

}, author = {Long, Fuhui and Peng, Hanchuan and Myers, Eugene} } @article {2155, title = {Phenotype clustering of breast epithelial cells in confocal images based on nuclear protein distribution analysis.}, journal = {BMC Cell Biology}, volume = {8 }, year = {2007}, month = {2007 Jul 10}, pages = {S3}, abstract = {

The distribution of chromatin-associated proteins plays a key role in directing nuclear function. Previously, we developed an image-based method to quantify the nuclear distributions of proteins and showed that these distributions depended on the phenotype of human mammary epithelial cells. Here we describe a method that creates a hierarchical tree of the given cell phenotypes and calculates the statistical significance between them, based on the clustering analysis of nuclear protein distributions.

}, keywords = {Breast, Breast Neoplasms, Cell Line, Cluster Analysis, Computer-Assisted, Confocal, Epithelial Cells, Female, Humans, Image Processing, Imaging, Microscopy, Models, Nuclear Proteins, Phenotype, Statistical, Three-Dimensional}, issn = {1471-2121}, doi = {10.1007/s12021-010-9090-x}, author = {Long, Fuhui and Peng, Hanchuan and Sudar, Damir and Leli{\`e}vre, Sophie A and Knowles, David W} } @article {5344, title = {Straightening worm images.}, journal = {2007 4TH IEEE International Symposium on Biomedical Imagin : Macro to Nano, VOLS 1-3}, year = {2007}, month = {2007 Apr 12-15}, pages = {292-5}, abstract = {

C. elegans, a roundworm in soil is widely used in studying animal development and aging, cell differentiation, etc. Recentlv, high-resolution fluorescence images of C. elegans have become available, introducing several new image analysis applications. One problem is that worm bodies usually curve greatly in images, thus it is highly desired to straighten worms so that they can be compared easily under the same canonical coordinate system. We develop a worm straightening algorithm (WSA) using a cutting-plane restacking method, which aggregates the linear rotation transforms of a continuous sequence of cutting lines/planes orthogonal to the "backbone" of a worm to best approximate the nonlinearly bended worm body. We formulate the backbone as a parametric form of cubic spline of a series of control points. We develop two minimum-spanning-tree based methods to automatically determine the locations of control points. Our experimental methods show that our approach can effectively straighten both 2D and 3D worm images.

}, doi = {10.1109/ISBI.2007.356846}, author = {Peng, Hanchuan and Long, Fuhui and Myers, Eugene W} } @article {2152, title = {Transitive closure and metric inequality of weighted graphs: detecting protein interaction modules using cliques.}, journal = {International Journal of Data Mining and Bioinformatics}, volume = {1}, year = {2006}, month = {2006 Jun 2}, pages = {162-77}, abstract = {

We study transitivity properties of edge weights in complex networks. We show that enforcing transitivity leads to a transitivity inequality which is equivalent to ultra-metric inequality. This can be used to define transitive closure on weighted undirected graphs, which can be computed using a modified Floyd-Warshall algorithm. These new concepts are extended to dissimilarity graphs and triangle inequalities. From this, we extend the clique concept from unweighted graph to weighted graph. We outline several applications and present results of detecting protein functional modules in a protein interaction network.

}, keywords = {Algorithms, Computational Biology, Fungal Proteins, Models, Protein Binding, Protein Interaction Mapping, Systems Biology, Theoretical}, issn = {1748-5673}, doi = {10.1007/s12021-010-9090-x}, author = {Ding, Chris and He, Xiaofeng and Xiong, Hui and Peng, Hanchuan and Holbrook, Stephen R} } @article {2158, title = {Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy.}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {27}, year = {2005}, month = {2005 Aug}, pages = {1226-38}, abstract = {

Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the difficulty in directly implementing the maximal dependency condition, we first derive an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection. Then, we present a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers). This allows us to select a compact set of superior features at very low cost. We perform extensive experimental comparison of our algorithm and other methods using three different classifiers (naive Bayes, support vector machine, and linear discriminate analysis) and four different data sets (handwritten digits, arrhythmia, NCI cancer cell lines, and lymphoma tissues). The results confirm that mRMR leads to promising improvement on feature selection and classification accuracy.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Cluster Analysis, Computer Simulation, Computer-Assisted, Diagnosis, Humans, Information Storage and Retrieval, Models, Neoplasms, Numerical Analysis, Pattern Recognition, Statistical}, issn = {0162-8828}, doi = {10.1007/s12021-010-9090-x}, author = {Peng, Hanchuan and Long, Fuhui and Ding, Chris} } @article {2159, title = {Minimum redundancy feature selection from microarray gene expression data.}, journal = {Journal of Bioinformatics and Computational Biology}, volume = {3}, year = {2005}, month = {2005 Apr}, pages = {185-205}, abstract = {

How to selecting a small subset out of the thousands of genes in microarray data is important for accurate classification of phenotypes. Widely used methods typically rank genes according to their differential expressions among phenotypes and pick the top-ranked genes. We observe that feature sets so obtained have certain redundancy and study methods to minimize it. We propose a minimum redundancy - maximum relevance (MRMR) feature selection framework. Genes selected via MRMR provide a more balanced coverage of the space and capture broader characteristics of phenotypes. They lead to significantly improved class predictions in extensive experiments on 6 gene expression data sets: NCI, Lymphoma, Lung, Child Leukemia, Leukemia, and Colon. Improvements are observed consistently among 4 classification methods: Naive Bayes, Linear discriminant analysis, Logistic regression, and Support vector machines. SUPPLIMENTARY: The top 60 MRMR genes for each of the datasets are listed in http://crd.lbl.gov/\ cding/MRMR/. More information related to MRMR methods can be found at http://www.hpeng.net/.

}, keywords = {Algorithms, Automated, Discriminant Analysis, Gene Expression Profiling, Oligonucleotide Array Sequence Analysis, Pattern Recognition}, issn = {0219-7200}, doi = {10.1007/s12021-010-9090-x}, author = {Ding, Chris and Peng, Hanchuan} } @article {2160, title = {A Bayesian morphometry algorithm.}, journal = {IEEE Transactions on Medical Imaging}, volume = {23}, year = {2004}, month = {2004 Jun}, pages = {723-37}, abstract = {

Most methods for structure-function analysis of the brain in medical images are usually based on voxel-wise statistical tests performed on registered magnetic resonance (MR) images across subjects. A major drawback of such methods is the inability to accurately locate regions that manifest nonlinear associations with clinical variables. In this paper, we propose Bayesian morphological analysis methods, based on a Bayesian-network representation, for the analysis of MR brain images. First, we describe how Bayesian networks (BNs) can represent probabilistic associations among voxels and clinical (function) variables. Second, we present a model-selection framework, which generates a BN that captures structure-function relationships from MR brain images and function variables. We demonstrate our methods in the context of determining associations between regional brain atrophy (as demonstrated on MR images of the brain), and functional deficits. We employ two data sets for this evaluation: the first contains MR images of 11 subjects, where associations between regional atrophy and a functional deficit are almost linear; the second data set contains MR images of the ventricles of 84 subjects, where the structure-function association is nonlinear. Our methods successfully identify voxel-wise morphological changes that are associated with functional deficits in both data sets, whereas standard statistical analysis (i.e., t-test and paired t-test) fails in the nonlinear-association case.

}, keywords = {Aged, Algorithms, Atrophy, Automated, Bayes Theorem, Brain, Brain Diseases, Cluster Analysis, Computer-Assisted, Humans, Image Interpretation, Magnetic Resonance Imaging, Pattern Recognition, Reproducibility of Results, Sensitivity and Specificity, Signal Processing, Subtraction Technique}, issn = {0278-0062}, doi = {10.1007/s12021-010-9090-x}, author = {Herskovits, Edward H and Peng, Hanchuan and Davatzikos, Christos} } @article {2157, title = {Mapping of microbial pathways through constrained mapping of orthologous genes.}, journal = {Proceedings / IEEE Computational Systems Bioinformatics Conference, CSB. IEEE Computational Systems Bioinformatics Conference}, year = {2004}, month = {2004}, pages = {363-70}, abstract = {

We present a novel computer algorithm for mapping biological pathways from one prokaryotic genome to another. The algorithm maps genes in a known pathway to their homologous genes (if any) in a target genome that is most consistent with (a) predicted orthologous gene relationship, (b) predicted operon structures, and (c) predicted co-regulation relationship of operons. Mathematically, we have formulated this problem as a constrained minimum spanning tree problem (called a Steiner network problem), and demonstrated that this formulation has the desired property through applications. We have solved this mapping problem using a combinatorial optimization algorithm, with guaranteed global optimality. We have implemented this algorithm as a computer program, called PMAP. Our test results on pathway mapping are highly encouraging {\textendash} we have mapped a number of pathways of H. influenzae, B. subtilis, H. pylori, and M. tuberculosis to E. coli using P-MAP, whose homologous pathways in E coli. are known and hence the mapping accuracy could be checked. We have then mapped known E. coli pathways in the EcoCyc database to the newly sequenced organism Synechococcus sp WH8102, and predicted 158 Synechococcus pathways. Detailed analyses on the predicted pathways indicate that P-MAP{\textquoteright}s mapping results are consistent with our general knowledge about (local) pathways. We believe that P-MAP will be a useful tool for microbial genome annotation projects and inference of individual microbial pathways.

}, keywords = {Algorithms, Amino Acid, Bacterial, Bacterial Physiological Phenomena, Bacterial Proteins, Biological, Chromosome Mapping, Computer Simulation, Conserved Sequence, Genome, Models, Sequence Homology, Signal Transduction}, issn = {1551-7497}, doi = {10.1007/s12021-010-9090-x}, author = {Olman, Victor and Peng, Hanchuan and Su, Zhengchang and Xu, Ying} }