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

TLDR

Contouring organs at risk is a critical but time‑consuming step in radiotherapy planning. The study aimed to determine whether institutional software‑generated contours could reduce manual OAR contouring time in lung cancer patients. Twenty stage I–III NSCLC CT scans were used to contour lungs, esophagus, spinal cord, heart, and mediastinum with atlas‑based and deep‑learning methods, and manual adjustment times were recorded. Atlas‑based and deep‑learning contours shortened manual OAR contouring by 7.8 min and 10 min respectively, with both methods significantly faster than manual except for left lung and esophagus in the atlas case, and deep‑learning outperformed existing solutions.

Abstract

Background and purposeContouring 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 methodsTwenty CT scans of stage I–III 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.ResultsWith 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.ConclusionsUser 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.