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A Multi-resolution Deep Learning Framework for Lung Adenocarcinoma Growth Pattern Classification Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Segmentating Nucleus Membranes in SBFSEM Volume Data with Deep Neural Networks

Authors : Yassar Almutairi, Tim Cootes, Karl Kadler

Published in: Medical Image Understanding and Analysis

Publisher: Springer International Publishing

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Abstract

We describe and evaluate a method for segmenting cell nuclei from serial block-face scanning electron microscope volumes. The nucleus is a roughly ellipsoidal structure near the centre of each cell, appearing as an irregular ellipse in each image slice. It is common to segment it manually, which is very time-consuming. We use a Convolutional Neural Network to locate the boundary of the nuclei in each image slice. Geometric constraints are used to discard false matches. The full 3D shape of each nucleus is reconstructed by linking the boundaries in neighbouring slices. We demonstrate and evaluate the system on several large image volumes.

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previous chapter Convolutional Neural Networks for Efficient Localization of Interstitial Lung Disease Patterns in HRCT Images
next chapter Novel Quantitative Magnetic Resonance Imaging Features with Liver Function Tests to Distinguish Parenchymal and Biliary Disease
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Metadata
Title
Segmentating Nucleus Membranes in SBFSEM Volume Data with Deep Neural Networks
Authors
Yassar Almutairi
Tim Cootes
Karl Kadler
Copyright Year
2018
Book
Medical Image Understanding and Analysis

Print ISBN: 978-3-319-95920-7

Electronic ISBN: 978-3-319-95921-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-95921-4_3

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