@conference {3858, title = {Boundary learning by optimization with topological constraints.}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition}, year = {2010}, month = {2010}, abstract = {

Recent studies have shown that machine learning can improve the accuracy of detecting object boundaries in images. In the standard approach, a boundary detector is trained by minimizing its pixel-level disagreement with human boundary tracings. This naive metric is problematic because it is overly sensitive to boundary locations. This problem is solved by metrics provided with the Berkeley Segmentation Dataset, but these can be insensitive to topological differences, such as gaps in boundaries. Furthermore, the Berkeley metrics have not been useful as cost functions for supervised learning. Using concepts from digital topology, we propose a new metric called the warping error that tolerates disagreements over boundary location, penalizes topological disagreements, and can be used directly as a cost function for learning boundary detection, in a method that we call Boundary Learning by Optimization with Topological Constraints (BLOTC). We trained boundary detectors on electron microscopic images of neurons, using both BLOTC and standard training. BLOTC produced substantially better performance on a 1.2 million pixel test set, as measured by both the warping error and the Rand index evaluated on segmentations generated from the boundary labelings. We also find our approach yields significantly better segmentation performance than either gPb-OWT-UCM or multiscale normalized cut, as well as Boosted Edge Learning trained directly on our data.

}, author = {Jain, Viren and Bollmann, Benjamin and Richardson, Mark and Berger, Daniel R. and Helmstaedter, Moritz N. and Briggman, Kevin L and Denk, Winfried and Bowden, Jared B. and Mendenhall, John M. and Abraham, Wickliffe C. and Harris, Kristen M and Kasthuri, Narayanan and Hayworth, Ken J. and Schalek, Richard and Tapia, Juan Carlos and Lichtman, Jeff W. and Seung, Hyunjune Sebastian} } @article {3844, title = {Convolutional networks can learn to generate affinity graphs for image segmentation.}, journal = {Neural Computation}, volume = {22}, year = {2010}, month = {2010 Feb}, pages = {511-38}, abstract = {

Many image segmentation algorithms first generate an affinity graph and then partition it. We present a machine learning approach to computing an affinity graph using a convolutional network (CN) trained using ground truth provided by human experts. The CN affinity graph can be paired with any standard partitioning algorithm and improves segmentation accuracy significantly compared to standard hand-designed affinity functions. We apply our algorithm to the challenging 3D segmentation problem of reconstructing neuronal processes from volumetric electron microscopy (EM) and show that we are able to learn a good affinity graph directly from the raw EM images. Further, we show that our affinity graph improves the segmentation accuracy of both simple and sophisticated graph partitioning algorithms. In contrast to previous work, we do not rely on prior knowledge in the form of hand-designed image features or image preprocessing. Thus, we expect our algorithm to generalize effectively to arbitrary image types.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Computer-Assisted, Electron, Image Processing, Mathematical Computing, Mathematical Concepts, Mathematics, Microscopy, Neural Networks (Computer), Pattern Recognition}, issn = {1530-888X}, doi = {10.1162/neco.2009.10-08-881}, author = {Turaga, Srinivas C and Murray, Joseph F and Jain, Viren and Roth, Fabian and Helmstaedter, Moritz and Briggman, Kevin and Denk, Winfried and Seung, Hyunjune Sebastian} } @article {3843, title = {Machines that learn to segment images: a crucial technology for connectomics.}, journal = {Current Opinion in Neurobiology}, volume = {20}, year = {2010}, month = {2010 Oct}, pages = {653-66}, abstract = {

Connections between neurons can be found by checking whether synapses exist at points of contact, which in turn are determined by neural shapes. Finding these shapes is a special case of image segmentation, which is laborious for humans and would ideally be performed by computers. New metrics properly quantify the performance of a computer algorithm using its disagreement with {\textquoteright}true{\textquoteright} segmentations of example images. New machine learning methods search for segmentation algorithms that minimize such metrics. These advances have reduced computer errors dramatically. It should now be faster for a human to correct the remaining errors than to segment an image manually. Further reductions in human effort are expected, and crucial for finding connectomes more complex than that of Caenorhabditis elegans.

}, keywords = {Algorithms, Animals, Artificial Intelligence, Computer-Assisted, Electron, Humans, Image Processing, Microscopy, Nanotechnology, Neurobiology}, issn = {1873-6882}, doi = {10.1016/j.conb.2010.07.004}, author = {Jain, Viren and Seung, Hyunjune Sebastian and Turaga, Srinivas C} } @proceedings {4532, title = {Representing part-whole relationships in recurrent neural networks.}, volume = {18}, year = {2005}, month = {2005}, pages = {563-70}, address = {Whistler, B.C.}, abstract = {

There is little consensus about the computational function of top-down synaptic connections in the visual system. Here we explore the hypothesis that top-down connections, like bottom-up connections, reflect partwhole relationships. We analyze a recurrent network with bidirectional synaptic interactions between a layer of neurons representing parts and a layer of neurons representing wholes. Within each layer, there is lateral inhibition. When the network detects a whole, it can rigorously enforce part-whole relationships by ignoring parts that do not belong. The network can complete the whole by filling in missing parts. The network can refuse to recognize a whole, if the activated parts do not conform to a stored part-whole relationship. Parameter regimes in which these behaviors happen are identified using the theory of permitted and forbidden sets [3, 4]. The network behaviors are illustrated by recreating Rumelhart and McClelland{\textquoteright}s {\textquotedblleft}interactive activation{\textquotedblright} model [7].

}, author = {Jain, Viren and Zhigulin, Valentin and Seung, Hyunjune Sebastian} }