@article {69680, title = {Global Neuron Shape Reasoning with Point Affinity Transformers}, journal = {bioRxiv}, year = {2024}, month = {2024 Nov 24}, abstract = {

Connectomics is a subfield of neuroscience that aims to map the brain{\textquoteright}s intricate wiring diagram. Accurate neuron segmentation from microscopy volumes is essential for automating connectome reconstruction. However, current state-of-the-art algorithms use image-based convolutional neural networks that are limited to local neuron shape context. Thus, we introduce a new framework that reasons over global neuron shape with a novel point affinity transformer. Our framework embeds a (multi-)neuron point cloud into a fixed-length feature set from which we can decode any point pair affinities, enabling clustering neuron point clouds for automatic proofreading. We also show that the learned feature set can easily be mapped to a contrastive embedding space that enables neuron type classification using a simple KNN classifier. Our approach excels in two demanding connectomics tasks: proofreading segmentation errors and classifying neuron types. Evaluated on three benchmark datasets derived from state-of-the-art connectomes, our method outperforms point transformers, graph neural networks, and unsupervised clustering baselines.

}, doi = {10.1101/2024.11.24.625067}, url = {https://doi.org/10.1101/2024.11.24.625067}, author = {Troidl, Jakob and Knittel, Johannes and Li, Wanhua and Zhan, Fangneng and Pfister, Hanspeter and Turaga, Srinivas} } @article {65832, title = {Automatic Detection of Synaptic Partners in a Whole-Brain Drosophila EM Dataset}, journal = {Nature Methods}, volume = {18}, year = {2021}, month = {2021 Jul 1}, pages = {771-4}, abstract = {

The study of neural circuits requires the reconstruction of neurons and the identification of synaptic connections between them. To scale the reconstruction to the size of whole-brain datasets, semi-automatic methods are needed to solve those tasks. Here, we present an automatic method for synaptic partner identification in insect brains, which uses convolutional neural networks to identify post-synaptic sites and their pre-synaptic partners. The networks can be trained from human generated point annotations alone and requires only simple post-processing to obtain final predictions. We used our method to extract 244 million putative synaptic partners in the fifty-teravoxel full adult fly brain (FAFB) electron microscopy (EM) dataset and evaluated its accuracy on 146,643 synapses from 702 neurons with a total cable length of 312 mm in four different brain regions. The predicted synaptic connections can be used together with a neuron segmentation to infer a connectivity graph with high accuracy: 96\% of edges between connected neurons are correctly classified as weakly connected (less than five synapses) and strongly connected (at least five synapses). Our synaptic partner predictions for the FAFB dataset are publicly available, together with a query library allowing automatic retrieval of up- and downstream neurons.

}, doi = {10.1038/s41592-021-01183-7}, author = {Buhmann, Julia and Sheridan, Arlo and Gerhard, Stephan and Krause, Renate and Nguyen, Tri and Heinrich, Larissa and Schlegel, Philipp and Lee, Wei-Chung Allen and Wilson, Rachel and Saalfeld, Stephan and Jefferis, Gregory and Bock, Davi and Turaga, Srinivas and Cook, Matthew and Funke, Jan} } @article {3846, title = {Learning to agglomerate superpixel hierarchies.}, journal = {Neural Information Processing Systems}, volume = {24}, year = {2011}, month = {2011}, pages = {648-56}, abstract = {

An agglomerative clustering algorithm merges the most similar pair of clusters at every iteration. The function that evaluates similarity is traditionally handdesigned, but there has been recent interest in supervised or semisupervised settings in which ground-truth clustered data is available for training. Here we show how to train a similarity function by regarding it as the action-value function of a reinforcement learning problem. We apply this general method to segment images by clustering superpixels, an application that we call Learning to Agglomerate Superpixel Hierarchies (LASH). When applied to a challenging dataset of brain images from serial electron microscopy, LASH dramatically improved segmentation accuracy when clustering supervoxels generated by state of the boundary detection algorithms. The naive strategy of directly training only supervoxel similarities and applying single linkage clustering produced less improvement.

}, author = {Jain, Viren and Turaga, Srinivas and Briggman, Kevin and Helmstaedter, Moritz N. and Denk, Winfried and Seung, Sebastian} } @proceedings {4528, title = {Supervised learning of image restoration with convolutional networks.}, volume = {2}, year = {2007}, month = {2007 Oct 14}, pages = {1-8}, publisher = {IEEE}, edition = {ICCV}, author = {Jain, Viren and Murray, Joseph F and Roth, Fabian and Turaga, Srinivas and Zhigulin, Valentin and Briggman, Kevin L and Helmstaedter, Moritz N. and Denk, Winfried and Seung, H Sebastian} }