Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session A01: Predicting Nonlinear and Complex Systems with Machine Learning
8:00 AM–10:48 AM,
Monday, March 6, 2023
Room: Room 124
Sponsoring
Units:
GSNP DSOFT
Chair: Yuhai Tu, IBM T. J. Watson Research Center
Abstract: A01.00014 : Automated neuron tracking using deep learning and targeted augmentation allows fast collection of C. elegans whole brain calcium activity during behavior*
10:36 AM–10:48 AM
Presenter:
Core Francisco Park
(Harvard University)
Authors:
Core Francisco Park
(Harvard University)
Sahand Rahi
(Ecole Polytechnique Federale de Lausanne)
Aravinthan Samuel
(Harvard University)
Mahsa Barzegar Keshteli
(Ecole Polytechnique Federale de Lausanne)
Kseniia Korchagina
(Ecole Polytechnique Federale de Lausanne)
Ariane Delrocq
(Ecole Polytechnique Federale de Lausanne)
Vladislav Susoy
(Harvard University)
Corinne Jones
(Ecole Polytechnique Federale de Lausanne)
To tackle this problem, we present a method to harness the robustness, adaptability and accuracy of CNNs while requiring only 5~10 frames of annotations. We designed a new network, 3D Compact Network (3DCN) with atrous convolutions and resolution-aware pooling layers. Our network is faster, more memory efficient, more accurate, and generalizes better than a conventional U-Net. After an initial training phase with rigid augmentations, the neural network annotates frames close to the training examples in posture space. From this coarse prediction, it then deforms the closest grounth truth frame to imitate this posture. The CNN is then retrained using these targeted augmented frames.
Using this method, we have successfully tracked 80+ neurons in the C. elegans male tail with large deformations and segmented very dim neurites in the hermaphrodite head from a completely different imaging setup. We have also tracked 50+ neurons of interest in the hermaphrodite head with <13 training frames per dataset.
We briefly discuss preliminary findings drawn from neural activity and behavior under a thermal stimulus.
*C.F.P acknowledges the support of NIH R01.
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