Intra- and Inter-Cellular Awareness for 3D Neuron Tracking and Segmentation in Large-Scale Connectomics

Hao Zhai, Jing Liu, Bei Hong, Jiazheng Liu, Qiwei Xie, Hua Han
Medical Imaging with Deep Learning, PMLR 227:1691-1712, 2024.

Abstract

Currently, most state-of-the-art pipelines for 3D micro-connectomic reconstruction deal with neuron over-segmentation, agglomeration and subcellular compartment (nuclei, mitochondria, synapses, etc.) detection separately. Inspired by the proofreading consensus of experts, we established a paradigm to acquire priori knowledge of cellular characteristics and ultrastructures, as well as determine the connectivity of neural circuits simultaneously. Following this novel paradigm, we were keen to bring the Intra- and Inter-Cellular Awareness back when Tracking and Segmenting neurons in connectomics. Our proposed method (II-CATS) utilizes few-shot learning techniques to encode the internal neurite representation and its learnable components, which could significantly impact neuron tracings. We further go beyond the original expected run length (ERL) metric by focusing on biological constraints (bERL) or spanning from the nucleus to spines (nERL). With the evaluation of these metrics, we perform typical experiments on multiple electron microscopy datasets on diverse animals and scales. In particular, our proposed method is naturally suitable for tracking neurons that have been identified by staining.

Cite this Paper

BibTeX
@InProceedings{pmlr-v227-zhai24a, title = {Intra- and Inter-Cellular Awareness for 3D Neuron Tracking and Segmentation in Large-Scale Connectomics}, author = {Zhai, Hao and Liu, Jing and Hong, Bei and Liu, Jiazheng and Xie, Qiwei and Han, Hua}, booktitle = {Medical Imaging with Deep Learning}, pages = {1691--1712}, year = {2024}, editor = {Oguz, Ipek and Noble, Jack and Li, Xiaoxiao and Styner, Martin and Baumgartner, Christian and Rusu, Mirabela and Heinmann, Tobias and Kontos, Despina and Landman, Bennett and Dawant, Benoit}, volume = {227}, series = {Proceedings of Machine Learning Research}, month = {10--12 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v227/zhai24a/zhai24a.pdf}, url = {https://proceedings.mlr.press/v227/zhai24a.html}, abstract = {Currently, most state-of-the-art pipelines for 3D micro-connectomic reconstruction deal with neuron over-segmentation, agglomeration and subcellular compartment (nuclei, mitochondria, synapses, etc.) detection separately. Inspired by the proofreading consensus of experts, we established a paradigm to acquire priori knowledge of cellular characteristics and ultrastructures, as well as determine the connectivity of neural circuits simultaneously. Following this novel paradigm, we were keen to bring the Intra- and Inter-Cellular Awareness back when Tracking and Segmenting neurons in connectomics. Our proposed method (II-CATS) utilizes few-shot learning techniques to encode the internal neurite representation and its learnable components, which could significantly impact neuron tracings. We further go beyond the original expected run length (ERL) metric by focusing on biological constraints (bERL) or spanning from the nucleus to spines (nERL). With the evaluation of these metrics, we perform typical experiments on multiple electron microscopy datasets on diverse animals and scales. In particular, our proposed method is naturally suitable for tracking neurons that have been identified by staining.} }
Endnote
%0 Conference Paper %T Intra- and Inter-Cellular Awareness for 3D Neuron Tracking and Segmentation in Large-Scale Connectomics %A Hao Zhai %A Jing Liu %A Bei Hong %A Jiazheng Liu %A Qiwei Xie %A Hua Han %B Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2024 %E Ipek Oguz %E Jack Noble %E Xiaoxiao Li %E Martin Styner %E Christian Baumgartner %E Mirabela Rusu %E Tobias Heinmann %E Despina Kontos %E Bennett Landman %E Benoit Dawant %F pmlr-v227-zhai24a %I PMLR %P 1691--1712 %U https://proceedings.mlr.press/v227/zhai24a.html %V 227 %X Currently, most state-of-the-art pipelines for 3D micro-connectomic reconstruction deal with neuron over-segmentation, agglomeration and subcellular compartment (nuclei, mitochondria, synapses, etc.) detection separately. Inspired by the proofreading consensus of experts, we established a paradigm to acquire priori knowledge of cellular characteristics and ultrastructures, as well as determine the connectivity of neural circuits simultaneously. Following this novel paradigm, we were keen to bring the Intra- and Inter-Cellular Awareness back when Tracking and Segmenting neurons in connectomics. Our proposed method (II-CATS) utilizes few-shot learning techniques to encode the internal neurite representation and its learnable components, which could significantly impact neuron tracings. We further go beyond the original expected run length (ERL) metric by focusing on biological constraints (bERL) or spanning from the nucleus to spines (nERL). With the evaluation of these metrics, we perform typical experiments on multiple electron microscopy datasets on diverse animals and scales. In particular, our proposed method is naturally suitable for tracking neurons that have been identified by staining.
APA
Zhai, H., Liu, J., Hong, B., Liu, J., Xie, Q. & Han, H.. (2024). Intra- and Inter-Cellular Awareness for 3D Neuron Tracking and Segmentation in Large-Scale Connectomics. Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 227:1691-1712 Available from https://proceedings.mlr.press/v227/zhai24a.html.

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