mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops

Frontiers in Neural Circuits

Neuroscience

Paul S. Katz¹  Brandon Drescher¹  Mukesh Bangalore Renuka²  Fuming Yang²  Nagaraju Dhanyasi²  Jeff W. Lichtman²  Yaron Meirovitch²  Emma Yang²  Elisa C. Pavarino²  Xiaotang Lu²  Flavie Bidel³  Binyamin Hochner³  Aravinthan D. T. Samuel⁴  Core Francisco Park⁴  Mei Zhen⁵  Mona D. Wang⁶ 

[1] Department of Biology, University of Massachusetts Amherst, Amherst, MA, United States;Department of Cellular and Molecular Biology, Harvard University, Cambridge, MA, United States;Department of Neurobiology, Silberman Institute of Life Sciences, The Hebrew University, Jerusalem, Israel;Department of Physics, Harvard University, Cambridge, MA, United States;Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada;Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada;Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada;

关键词: affordable connectomics;    volume electron microscopy;    semi-automatic neural circuit reconstruction;    segmentation;    deep learning;    VAST;    lightweight software;    MATLAB;   

DOI  :  10.3389/fncir.2023.952921

 received in 2022-05-25, accepted in 2023-04-17,  发布年份 2023

来源: Frontiers

【 摘 要 】

Connectomics is fundamental in propelling our understanding of the nervous system's organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited from ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures and advanced machine learning algorithms. On the other hand, the field of neuroscience at large, and of image processing in particular, has manifested a need for user-friendly and open source tools which enable the community to carry out advanced analyses. In line with this second vein, here we propose mEMbrain, an interactive MATLAB-based software which wraps algorithms and functions that enable labeling and segmentation of electron microscopy datasets in a user-friendly user interface compatible with Linux and Windows. Through its integration as an API to the volume annotation and segmentation tool VAST, mEMbrain encompasses functions for ground truth generation, image preprocessing, training of deep neural networks, and on-the-fly predictions for proofreading and evaluation. The final goals of our tool are to expedite manual labeling efforts and to harness MATLAB users with an array of semi-automatic approaches for instance segmentation. We tested our tool on a variety of datasets that span different species at various scales, regions of the nervous system and developmental stages. To further expedite research in connectomics, we provide an EM resource of ground truth annotation from four different animals and five datasets, amounting to around 180 h of expert annotations, yielding more than 1.2 GB of annotated EM images. In addition, we provide a set of four pre-trained networks for said datasets. All tools are available from https://lichtman.rc.fas.harvard.edu/mEMbrain/. With our software, our hope is to provide a solution for lab-based neural reconstructions which does not require coding by the user, thus paving the way to affordable connectomics.

【 授权许可】

Unknown   
Copyright © 2023 Pavarino, Yang, Dhanyasi, Wang, Bidel, Lu, Yang, Francisco Park, Bangalore Renuka, Drescher, Samuel, Hochner, Katz, Zhen, Lichtman and Meirovitch.