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2016 | OriginalPaper | Chapter

Gland Instance Segmentation by Deep Multichannel Side Supervision

Authors : Yan Xu, Yang Li, Mingyuan Liu, Yipei Wang, Maode Lai, Eric I-Chao Chang

Published in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

Publisher: Springer International Publishing

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Abstract

In this paper, we propose a new image instance segmentation method that segments individual glands (instances) in colon histology images. This is a task called instance segmentation that has recently become increasingly important. The problem is challenging since not only do the glands need to be segmented from the complex background, they are also required to be individually identified. Here we leverage the idea of image-to-image prediction in recent deep learning by building a framework that automatically exploits and fuses complex multichannel information, regional and boundary patterns, with side supervision (deep supervision on side responses) in gland histology images. Our proposed system, deep multichannel side supervision (DMCS), alleviates heavy feature design due to the use of convolutional neural networks guided by side supervision. Compared to methods reported in the 2015 MICCAI Gland Segmentation Challenge, we observe state-of-the-art results based on a number of evaluation metrics.

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previous chapter Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images
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Metadata
Title
Gland Instance Segmentation by Deep Multichannel Side Supervision
Authors
Yan Xu
Yang Li
Mingyuan Liu
Yipei Wang
Maode Lai
Eric I-Chao Chang
Copyright Year
2016
Book
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

Print ISBN: 978-3-319-46722-1

Electronic ISBN: 978-3-319-46723-8

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46723-8_57

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