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2021 | OriginalPaper | Chapter

A 2D ResU-Net Powered Segmentation of Thoracic Organs at Risk Using Computed Tomography Images

Authors : Mohit Asudani, Alarsh Tiwari, Harsh Kataria, Vipul Kumar Mishra, Anurag Goswami

Published in: Advanced Computing

Publisher: Springer Singapore

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Abstract

The recent advances in the field of computer vision have led to the wide use of Convolutional Neural Networks (CNNs) in organ segmentation of computed tomography (CT) images. Image-guided radiation therapy requires the accurate segmentation of organs at risk (OARs). In this paper, the proposed model is a 2D ResU-Net network to automatically segment thoracic organs at risk in computed tomography (CT) images. The architecture consists of a downsampling path for capturing features and a symmetric upsampling path for obtaining precise localization. The proposed approach achieves a 0.93 dice metric (DSC) and 0.26 hausdorff distance (HD) after using ImageNet stats for normalizing and using pre-trained weights.

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Metadata
Title
A 2D ResU-Net Powered Segmentation of Thoracic Organs at Risk Using Computed Tomography Images
Authors
Mohit Asudani
Alarsh Tiwari
Harsh Kataria
Vipul Kumar Mishra
Anurag Goswami
Copyright Year
2021
Publisher
Springer Singapore
Book
Advanced Computing

Print ISBN: 978-981-16-0400-3

Electronic ISBN: 978-981-16-0401-0

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-16-0401-0_4

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