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Tissue Classification to Support Local Active Delineation of Brain Tumors Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Polyp Segmentation with Fully Convolutional Deep Dilation Neural Network

Evaluation Study

Authors : Yunbo Guo, Bogdan J. Matuszewski

Published in: Medical Image Understanding and Analysis

Publisher: Springer International Publishing

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Abstract

Analyses of polyp images play an important role in an early detection of colorectal cancer. An automated polyp segmentation is seen as one of the methods that could improve the accuracy of the colonoscopic examination. The paper describes evaluation study of a segmentation method developed for the Endoscopic Vision Gastrointestinal Image ANAlysis – (GIANA) polyp segmentation sub-challenges. The proposed polyp segmentation algorithm is based on a fully convolutional network (FCN) model. The paper describes cross-validation results on the training GIANA dataset. Various tests have been evaluated, including network configuration, effects of data augmentation, and performance of the method as a function of polyp characteristics. The proposed method delivers state-of-the-art results. It secured the first place for the image segmentation tasks at the 2017 GIANA challenge and the second place for the SD images at the 2018 GIANA challenge.

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previous chapter A General Framework for Localizing and Locally Segmenting Correlated Objects: A Case Study on Intervertebral Discs in Multi-modality MR Images
next chapter Convolutional Attention on Images for Locating Macular Edema
Footnotes
1
It is envisaged that supplementary results of the ongoing research on polyp segmentation will be gradually made available at: https://​github.​com/​ybguo1/​Polyp-Segmentation.
 
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Metadata
Title
Polyp Segmentation with Fully Convolutional Deep Dilation Neural Network
Authors
Yunbo Guo
Bogdan J. Matuszewski
Copyright Year
2020
Book
Medical Image Understanding and Analysis

Print ISBN: 978-3-030-39342-7

Electronic ISBN: 978-3-030-39343-4

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-39343-4_32

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