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A Weight-Selection Strategy on Training Deep Neural Networks for Imbalanced Classification Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Fully Deep Convolutional Neural Networks for Segmentation of the Prostate Gland in Diffusion-Weighted MR Images

Authors : Tyler Clark, Alexander Wong, Masoom A. Haider, Farzad Khalvati

Published in: Image Analysis and Recognition

Publisher: Springer International Publishing

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Abstract

Prostate cancer is a leading cause of mortality among men. Diffusion-weighted magnetic resonance imaging (DW-MRI) has shown to be successful at monitoring and detecting prostate tumors. The clinical guidelines to interpret DW-MRI for prostate cancer requires the segmentation of the prostate gland into different zones. Moreover, computer-aided detection tools which are designed to detect prostate cancer automatically, usually require the segmentation of prostate gland as a preprocessing step. In this paper, we present a segmentation algorithm for delineation of the prostate gland in DW-MRI via fully convolutional neural network. The segmentation algorithm was applied to images of 30 (testing) and 104 (training) patients and a median Dice Similarity Coefficient of 0.89 was achieved. This method is faster and returns similar results compared to registration based methods; although it has the potential to produce improved results given a larger training set.

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previous chapter Ejection Fraction Estimation Using a Wide Convolutional Neural Network
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Metadata
Title
Fully Deep Convolutional Neural Networks for Segmentation of the Prostate Gland in Diffusion-Weighted MR Images
Authors
Tyler Clark
Alexander Wong
Masoom A. Haider
Farzad Khalvati
Copyright Year
2017
Book
Image Analysis and Recognition

Print ISBN: 978-3-319-59875-8

Electronic ISBN: 978-3-319-59876-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59876-5_12

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