Exploiting clinically available delineations for cnn-based segmentation in radiotherapy treatment planning

Louis D. van Harten*, Jelmer M. Wolterink, Joost J.C. Verhoeff, Ivana Išgum

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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.

Original languageEnglish
Title of host publicationMedical Imaging 2020
Subtitle of host publicationImage Processing
EditorsIvana Isgum, Bennett A. Landman
PublisherSPIE Press
ISBN (Electronic)9781510633933
DOIs
Publication statusPublished - 2020
Externally publishedYes
EventSPIE Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling - Marriott Marquis Houston, Houston, United States
Duration: 15 Feb 202020 Feb 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11313
ISSN (Print)1605-7422

Conference

ConferenceSPIE Medical Imaging 2020
CountryUnited States
CityHouston
Period15/02/2020/02/20

Keywords

  • Convolutional neural network
  • Deep learning
  • Incomplete labels
  • MRI
  • Organ-at-risk segmentation
  • Radiotherapy

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