Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer

Tim Lustberg*, Johan van Soest, Mark Gooding, Devis Peressutti, Paul Aljabar, Judith van der Stoep, Wouter van Elmpt, Andre Dekker

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

161 Citations (Web of Science)

Abstract

Background and purpose: Contouring of organs at risk (OARS) is an important but time consuming part of radiotherapy treatment planning. The aim of this study was to investigate whether using institutional created software-generated contouring will save time if used as a starting point for manual OAR contouring for lung cancer patients. Material and methods: Twenty CT scans of stage I-Ill NSCLC patients were used to compare user adjusted contours after an atlas-based and deep learning contour, against manual delineation. The lungs, esophagus, spinal cord, heart and mediastinum were contoured for this study. The time to perform the manual tasks was recorded. Results: With a median time of 20 min for manual contouring, the total median time saved was 7.8 min when using atlas-based contouring and 10 min for deep learning contouring. Both atlas based and deep learning adjustment times were significantly lower than manual contouring time for all OARs except for the left lung and esophagus of the atlas based contouring. Conclusions: User adjustment of software generated contours is a viable strategy to reduce contouring time of OARs for lung radiotherapy while conforming to local clinical standards. In addition, deep learning contouring shows promising results compared to existing solutions. (C) 2017 The Authors. Published by Elsevier Ireland Ltd. Radiotherapy and Oncology 126 (2018) 312-317 This is an open access article under the CC BY-NC-ND license (http://creativecommons.orgilicensesiby-nc-nd/4.0/).
Original languageEnglish
Pages (from-to)312-317
Number of pages6
JournalRadiotherapy and Oncology
Volume126
Issue number2
DOIs
Publication statusPublished - 1 Feb 2018

Keywords

  • Lung cancer
  • Organs at risk
  • Radiotherapy
  • Atlas contouring
  • Deep learning contouring
  • VOLUME DELINEATION
  • NODAL VOLUMES
  • SEGMENTATION
  • IMAGES
  • ORGANS
  • RISK
  • NECK
  • HEAD

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