Dense cellular segmentation for EM using 2D–3D neural network ensembles

Published in

Scientific Reports(2020)

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TL;DR

This work presents an algorithm using novel hybrid 2D–3D segmentation networks to produce dense cellular segmentations with accuracy levels that outperform baseline methods and approach those of human annotators, the first published approach to automating the creation of cell models with this level of structural detail.

Abstract

Biologists who use electron microscopy (EM) images to build nanoscale 3D models of whole cells and their organelles have historically been limited to small numbers of cells and cellular features due to constraints in imaging and analysis. This has been a major factor limiting insight into the complex variability of cellular environments. Modern EM can produce gigavoxel image volumes containing large numbers of cells, but accurate manual segmentation of image features is slow and limits the creation of cell models. Segmentation algorithms based on convolutional neural networks can process large volumes quickly, but achieving EM task accuracy goals often challenges current techniques. Here, we define dense cellular segmentation as a multiclass semantic segmentation task for modeling cells and large numbers of their organelles, and give an example in human blood platelets. We present an algorithm using novel hybrid 2D–3D segmentation networks to produce dense cellular segmentations with accuracy levels that outperform baseline methods and approach those of human annotators. To our knowledge, this work represents the first published approach to automating the creation of cell models with this level of structural detail.

Authors

M. Guay

1 papers

Z. Emam

1 papers

Adam Anderson

1 papers

References36 items

3D ultrastructural analysis of α‐granule, dense granule, mitochondria, and canalicular system arrangement in resting human platelets

Automated Reconstruction of a Serial-Section EM Drosophila Brain with Flood-Filling Networks and Local Realignment

Med3D: Transfer Learning for 3D Medical Image Analysis

Deep learning in medical imaging and radiation therapy.

CDeep3M—Plug-and-Play cloud-based deep learning for image segmentation

Dense cellular segmentation for EM using 2D–3D neural network ensembles

Published in

Scientific Reports(2020)

External Links:

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TL;DR

Abstract

Authors

M. Guay

1 papers

Z. Emam

1 papers

Adam Anderson

1 papers

References36 items

3D ultrastructural analysis of α‐granule, dense granule, mitochondria, and canalicular system arrangement in resting human platelets

Automated Reconstruction of a Serial-Section EM Drosophila Brain with Flood-Filling Networks and Local Realignment

Med3D: Transfer Learning for 3D Medical Image Analysis

Deep learning in medical imaging and radiation therapy.

CDeep3M—Plug-and-Play cloud-based deep learning for image segmentation

I. Pokrovskaya

1 papers

TL;DR

Abstract

Authors

References36 items

3D ultrastructural analysis of α‐granule, dense granule, mitochondria, and canalicular system arrangement in resting human platelets

Automated Reconstruction of a Serial-Section EM Drosophila Brain with Flood-Filling Networks and Local Realignment

Med3D: Transfer Learning for 3D Medical Image Analysis

Deep learning in medical imaging and radiation therapy.

CDeep3M—Plug-and-Play cloud-based deep learning for image segmentation

TL;DR

Abstract

Authors

References36 items

3D ultrastructural analysis of α‐granule, dense granule, mitochondria, and canalicular system arrangement in resting human platelets

Automated Reconstruction of a Serial-Section EM Drosophila Brain with Flood-Filling Networks and Local Realignment

Med3D: Transfer Learning for 3D Medical Image Analysis

Deep learning in medical imaging and radiation therapy.

CDeep3M—Plug-and-Play cloud-based deep learning for image segmentation

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STEM tomography reveals that the canalicular system and α‐granules remain separate compartments during early secretion stages in blood platelets

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