A 2D dilated residual U-net for multi-organ segmentation in thoracic CT

Conference contribution


Publication Details

Author(s): Vesal S, Ravikumar N, Maier A
Editor(s): Su Ruan, Caroline Petitjean, Bernard Dubray, Bernard Dubray, Zoe Lambert
Publisher: CEUR-WS
Publication year: 2019
Volume: 2349
Conference Proceedings Title: CEUR Workshop Proceedings
ISSN: 16130073


Abstract

Automatic segmentation of organs-at-risk (OAR) in computed tomography (CT) is an essential part of planning effective treatment strategies to combat lung and esophageal cancer. Accurate segmentation of organs surrounding tumours helps account for the variation in position and morphology inherent across patients, thereby facilitating adaptive and computer-assisted radiotherapy. Although manual delineation of OARs is still highly prevalent, it is prone to errors due to complex variations in the shape and position of organs across patients, and low soft tissue contrast between neigh-bouring organs in CT images. Recently, deep convolutional neural networks (CNNs) have gained tremendous traction and achieved state-of-the-art results in medical image segmentation. In this paper, we propose a deep learning framework to segment OARs in thoracic CT images, specifically for the: heart, esophagus, trachea and aorta. Our approach employs dilated convolutions and aggregated residual connections in the bottleneck of a U-Net styled network, which incorporates global context and dense information. Our method achieved an overall Dice score of 91.57% on 20 unseen test samples from the ISBI 2019 SegTHOR challenge.


FAU Authors / FAU Editors

Maier, Andreas Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)
Ravikumar, Nishant
Lehrstuhl für Informatik 5 (Mustererkennung)
Vesal, Sulaiman
Lehrstuhl für Informatik 5 (Mustererkennung)


How to cite

APA:
Vesal, S., Ravikumar, N., & Maier, A. (2019). A 2D dilated residual U-net for multi-organ segmentation in thoracic CT. In Su Ruan, Caroline Petitjean, Bernard Dubray, Bernard Dubray, Zoe Lambert (Eds.), CEUR Workshop Proceedings. Venice, IT: CEUR-WS.

MLA:
Vesal, Sulaiman, Nishant Ravikumar, and Andreas Maier. "A 2D dilated residual U-net for multi-organ segmentation in thoracic CT." Proceedings of the 2019 SegTHOR Challenge: Segmentation of THoracic Organs at Risk in CT Images, SegTHOR 2019, Venice Ed. Su Ruan, Caroline Petitjean, Bernard Dubray, Bernard Dubray, Zoe Lambert, CEUR-WS, 2019.

BibTeX: 

Last updated on 2019-03-05 at 14:23