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

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/)