Deep learning tools for the cancer clinic: an open-source framework with head and neck contour validation

Abstract

Background With the rapid growth of deep learning research for medical applications comes the need for clinical personnel to be comfortable and familiar with these techniques. Taking a proven approach, we developed a straightforward open-source framework for producing automatic contours for head and neck planning computed tomography studies using a convolutional neural network (CNN). Methods Anonymized studies of 229 patients treated at our clinic for head and neck cancer from 2014 to 2018 were used to train and validate the network. We trained a separate CNN iteration for each of 11 common organs at risk, and then used data from 19 patients previously set aside as test cases for evaluation. We used a commercial atlas-based automatic contouring tool as a comparative benchmark on these test cases to ensure acceptable CNN performance. For the CNN contours and the atlas-based contours, performance was measured using three quantitative metrics and physician reviews using survey and quantifiable correction time for each contour. Results The CNN achieved statistically better scores than the atlas-based workflow on the quantitative metrics for 7 of the 11 organs at risk. In the physician review, the CNN contours were more likely to need minor corrections but less likely to need substantial corrections, and the cumulative correction time required was less than for the atlas-based contours for all but two test cases. Conclusions With this validation, we packaged the code framework and trained CNN parameters and a no-code, browser-based interface to facilitate reproducibility and expansion of the work. All scripts and files are available in a public GitHub repository and are ready for immediate use under the MIT license. Our work introduces a deep learning tool for automatic contouring that is easy for novice personnel to use.

Clinical validation of commercial deep-learning based auto-segmentation models for organs at risk in the head and neck region: a single institution study

“…( 33 ), who corroborate previous findings that surface DSC may better correlate with clinical applicability. Additionally, this study focused on the performance of commercially available auto-segmentation solutions; however, there is a growing trend of research teams developing open-source solutions, available on platforms like GitHub ( 34 , 35 ). These open-source solutions often yield competitive results, rivalling commercial software and may be of particularly beneficial for research studies with limited funding or in clinics with constrained resources.…”

Artificial intelligence in oncologic imaging

“…As in many other body regions, various deep learning algorithms have already achieved human-level performance in segmenting the organ or cancerous lesions, which may ultimately aid with focal therapies. This can dramatically reduce the time a radiation oncologist spends in manually contouring a patient study, increase contour consistency and improve accuracy [32] .…”