@inproceedings{dab54b8a4d2a49d8825b26d59a37e5cb,
title = "Exploiting clinically available delineations for cnn-based segmentation in radiotherapy treatment planning",
abstract = "Convolutional neural networks (CNNs) have been widely and successfully used for medical image segmentation. However, CNNs are typically considered to require large numbers of dedicated expert-segmented training volumes, which may be limiting in practice. This work investigates whether clinically obtained segmentations which are readily available in picture archiving and communication systems (PACS) could provide a possible source of data to train a CNN for segmentation of organs-at-risk (OARs) in radiotherapy treatment planning. In such data, delineations of structures deemed irrelevant to the target clinical use may be lacking. To overcome this issue, we use multi-label instead of multi-class segmentation. We empirically assess how many clinical delineations would be sufficient to train a CNN for the segmentation of OARs and find that increasing the training set size beyond a limited number of images leads to sharply diminishing returns. Moreover, we find that by using multi-label segmentation, missing structures in the reference standard do not have a negative effect on overall segmentation accuracy. These results indicate that segmentations obtained in a clinical workflow can be used to train an accurate OAR segmentation model.",
keywords = "Convolutional neural network, Deep learning, Incomplete labels, MRI, Organ-at-risk segmentation, Radiotherapy",
author = "{van Harten}, {Louis D.} and Wolterink, {Jelmer M.} and Verhoeff, {Joost J.C.} and Ivana I{\v s}gum",
year = "2020",
doi = "10.1117/12.2549653",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Ivana Isgum and Landman, {Bennett A.}",
booktitle = "Medical Imaging 2020",
address = "United States",
note = "SPIE Medical Imaging 2020 : Image-Guided Procedures, Robotic Interventions, and Modeling ; Conference date: 15-02-2020 Through 20-02-2020",
}