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Pancreas Segmentation in CT and MRI via Task-Specific Network Design and Recurrent Neural Contextual Learning Read chapter Read first chapter

2019 | OriginalPaper | Chapter

4. Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-Fine Framework and Its Adversarial Examples

Authors : Yingwei Li, Zhuotun Zhu, Yuyin Zhou, Yingda Xia, Wei Shen, Elliot K. Fishman, Alan L. Yuille

Published in: Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

Publisher: Springer International Publishing

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Abstract

Although deep neural networks have been a dominant method for many 2D vision tasks, it is still challenging to apply them to 3D tasks, such as medical image segmentation, due to the limited amount of annotated 3D data and limited computational resources. In this chapter, by rethinking the strategy to apply 3D Convolutional Neural Networks to segment medical images, we propose a novel 3D-based coarse-to-fine framework to efficiently tackle these challenges. The proposed 3D-based framework outperforms their 2D counterparts by a large margin since it can leverage the rich spatial information along all three axes. We further analyze the threat of adversarial attacks on the proposed framework and show how to defend against the attack. We conduct experiments on three datasets, the NIH pancreas dataset, the JHMI pancreas dataset and the JHMI pathological cyst dataset, where the first two and the last one contain healthy and pathological pancreases, respectively, and achieve the current state of the art in terms of Dice-Sørensen Coefficient (DSC) on all of them. Especially, on the NIH pancreas dataset, we outperform the previous best by an average of over \(2\%\), and the worst case is improved by \(7\%\) to reach almost \(70\%\), which indicates the reliability of our framework in clinical applications.

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previous chapter 2D-Based Coarse-to-Fine Approaches for Small Target Segmentation in Abdominal CT Scans
next chapter Unsupervised Domain Adaptation of ConvNets for Medical Image Segmentation via Adversarial Learning
Footnotes
1
The results are reported by our runs using the same cross-validation splits where the code is available from their GitHub: https://​github.​com/​yulequan/​HeartSeg.
 
2
The coarse model is used for comparison since it is the basis of our framework.
 
3
Since the raw intensity values are to be in \([-100, 240]\) during preprocessing (see Sect. 4.4.1.1), here we set \(\Lambda = 240 - (-100) = 340\) accordingly.
 
4
For implementation simplicity and efficiency, we ignored the sub-volumes only containing the background class when generating adversarial examples.
 
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Metadata
Title
Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-Fine Framework and Its Adversarial Examples
Authors
Yingwei Li
Zhuotun Zhu
Yuyin Zhou
Yingda Xia
Wei Shen
Elliot K. Fishman
Alan L. Yuille
Copyright Year
2019
Book
Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

Print ISBN: 978-3-030-13968-1

Electronic ISBN: 978-3-030-13969-8

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-13969-8_4

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