TY - JOUR
T1 - mEMbrain
T2 - an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops
AU - Pavarino, Elisa C.
AU - Yang, Emma
AU - Dhanyasi, Nagaraju
AU - Wang, Mona D.
AU - Bidel, Flavie
AU - Lu, Xiaotang
AU - Yang, Fuming
AU - Francisco Park, Core
AU - Bangalore Renuka, Mukesh
AU - Drescher, Brandon
AU - Samuel, Aravinthan D.T.
AU - Hochner, Binyamin
AU - Katz, Paul S.
AU - Zhen, Mei
AU - Lichtman, Jeff W.
AU - Meirovitch, Yaron
N1 - Publisher Copyright:
Copyright © 2023 Pavarino, Yang, Dhanyasi, Wang, Bidel, Lu, Yang, Francisco Park, Bangalore Renuka, Drescher, Samuel, Hochner, Katz, Zhen, Lichtman and Meirovitch.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - affordable connectomics
KW - deep learning
KW - lightweight software
KW - MATLAB
KW - segmentation
KW - semi-automatic neural circuit reconstruction
KW - VAST
KW - volume electron microscopy
UR - http://www.scopus.com/inward/record.url?scp=85164209199&partnerID=8YFLogxK
U2 - 10.3389/fncir.2023.952921
DO - 10.3389/fncir.2023.952921
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C2 - 37396399
AN - SCOPUS:85164209199
SN - 1662-5110
VL - 17
JO - Frontiers in Neural Circuits
JF - Frontiers in Neural Circuits
M1 - 952921
ER -