RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy

TL;DR

RootPainter3D introduces an interactive machine learning approach that significantly accelerates and improves the accuracy of organ-at-risk contouring in radiotherapy, making it accessible for routine clinical use.

Contribution

The paper presents a novel interactive machine learning method with corrective annotation that achieves high accuracy and reduces contouring time in radiotherapy planning.

Findings

Dice score of 0.95 with manual delineations

Time to delineate hearts reduced to 2 minutes 2 seconds after 923 images

Substantial time savings compared to manual contouring

Abstract

Organ-at-risk contouring is still a bottleneck in radiotherapy, with many deep learning methods falling short of promised results when evaluated on clinical data. We investigate the accuracy and time-savings resulting from the use of an interactive-machine-learning method for an organ-at-risk contouring task. We compare the method to the Eclipse contouring software and find strong agreement with manual delineations, with a dice score of 0.95. The annotations created using corrective-annotation also take less time to create as more images are annotated, resulting in substantial time savings compared to manual methods, with hearts that take 2 minutes and 2 seconds to delineate on average, after 923 images have been delineated, compared to 7 minutes and 1 seconds when delineating manually. Our experiment demonstrates that interactive-machine-learning with corrective-annotation provides a fast and accessible way for non computer-scientists to train deep-learning models to segment their own structures of interest as part of routine clinical workflows. Source code is available at \href{https://github.com/Abe404/RootPainter3D}{this HTTPS URL}.