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"Esophageal Gross Tumor Volume Segmentation using a 3D Convolutional Neural Network"

Sahar Yousefi, Hessam Sokooti, Mohamed S. Elmahdy, Femke P. Peters, Mohammad T. Manzuri Shalmani, Roel T. Zinkstok and Marius Staring

Abstract

Accurate gross tumor volume (GTV) segmentation in esophagus CT images is a critical task in computer aided diagnosis (CAD) systems. However, because of the difficulties raised by the contrast similarity between esophageal GTV and its neighbouring tissues in CT scans, this problem has been addressed weakly. In this paper we present a 3D end-to-end method based on a convolutional neural network (CNN) for this purpose. We leverages design elements from DenseNet in a typical U-shape. The proposed architecture consists of a contractile path and an extending path that includes dense blocks for extracting contextual features and retrieves the lost resolution respectively. Using dense blocks leads to deep supervision, feature re-usability, and parameter reduction while aiding the network to be more accurate. The proposed architecture was trained and tested on a dataset containing 553 scans from 49 distinct patients. The proposed network achieved a Dice value of 0:73 ± 0:20, and a 95% mean surface distance of 3:07 ± 1:86 mm for 85 test scans. The experimental results indicates the effectiveness of the proposed method for clinical diagnosis and treatment systems.

 

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Copyright © 2018 by the authors. Published version © 2018 by Springer Lecture Notes in Computer Science. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.

 

BibTeX entry

@inproceedings{Yousefi:2018,
author = "{Sahar Yousefi and Hessam Sokooti and Mohamed S. Elmahdy and Femke P. Peters and Mohammad T. Manzuri Shalmani and Roel T. Zinkstok and Marius Staring}",
title = "{Esophageal Gross Tumor Volume Segmentation using a 3D Convolutional Neural Network}",
booktitle = "{Medical Image Computing and Computer-Assisted Intervention}",
editor = "{A.F. Frangi and J.A. Schnabel and C. Davatzikos and C. Alberola-López and G. Fichtinger}",
address = "{Granada,Spain}",
series = "{Lecture Notes in Computer Science}",
volume = "{11073}",
pages = "{343 - 351}",
month = "{September}",
year = "{2018}",
}

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