@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 = {<p>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.</p>
},
	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 {6491,
	title = {A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale.},
	journal = {PLoS Computational Biology},
	volume = {5},
	year = {2009},
	month = {2009 Mar},
	pages = {e1000334},
	abstract = {<p>In this era of complete genomes, our knowledge of neuroanatomical circuitry remains surprisingly sparse. Such knowledge is critical, however, for both basic and clinical research into brain function. Here we advocate for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brainwide coverage, using injections of tracers or viral vectors. We detail the scientific and medical rationale and briefly review existing knowledge and experimental techniques. We define a set of desiderata, including brainwide coverage; validated and extensible experimental techniques suitable for standardization and automation; centralized, open-access data repository; compatibility with existing resources; and tractability with current informatics technology. We discuss a hypothetical but tractable plan for mouse, additional efforts for the macaque, and technique development for human. We estimate that the mouse connectivity project could be completed within five years with a comparatively modest budget.</p>
},
	keywords = {Animals, Brain, Databases, Factual, Humans, Macaca, Mice, Models, Nerve Net, Neuroanatomy, Neurological, Research Design},
	issn = {1553-7358},
	doi = {10.1371/journal.pcbi.1000334},
	author = {Bohland, Jason W and Wu, Caizhi and Barbas, Helen and Bokil, Hemant and Bota, Mihail and Breiter, Hans C and Cline, Hollis T and Doyle, John C and Freed, Peter J and Greenspan, Ralph J and Haber, Suzanne N and Hawrylycz, Michael and Herrera, Daniel G and Hilgetag, Claus C and Huang, Z Josh and Jones, Allan and Jones, Edward G and Karten, Harvey J and Kleinfeld, David and K{\"o}tter, Rolf and Lester, Henry A and Lin, John M and Mensh, Brett D and Mikula, Shawn and Panksepp, Jaak and Price, Joseph L and Safdieh, Joseph and Saper, Clifford B and Schiff, Nicholas D and Schmahmann, Jeremy D and Stillman, Bruce W and Svoboda, Karel and Swanson, Larry W and Toga, Arthur W and Van Essen, David C and Watson, James D and Mitra, Partha P}
}
