Clinical evaluation of atlas- and deep learning-based automatic segmentation of multiple organs and clinical target volumes for breast cancer. (December 2020)

• Record Type: Journal Article
• Authors: Choi, Min Seo; Choi, Byeong Su; Chung, Seung Yeun; Kim, Nalee; Chun, Jaehee; Kim, Yong Bae; Chang, Jee Suk; Kim, Jin Sung
• Is Part Of: Radiotherapy and oncology. Volume 153(2020)
• Page Start: 139
• Page End: 145
• Publication Date: 2020-12
• Subjects: Deep learning-based autosegmentation -- Commercial atlas-based autosegmentation -- CTV segmentation -- Radiation therapy -- Artificial intelligence -- Breast cancer; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2020.09.045
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: The DLBAS model performed better for the majority of CTVs and OARs in comparison to ABAS solutions. ABAS performed relatively well for structures with distinct boundaries. DLBAS showed more robustness and less dependency on input data. DLBAS has great potential to help optimise the radiation therapy planning workflow. Abstract: Manual segmentation is the gold standard method for radiation therapy planning; however, it is time-consuming and prone to inter- and intra-observer variation, giving rise to interests in auto-segmentation methods. We evaluated the feasibility of deep learning-based auto-segmentation (DLBAS) in comparison to commercially available atlas-based segmentation solutions (ABAS) for breast cancer radiation therapy. This study used contrast-enhanced planning computed tomography scans from 62 patients with breast cancer who underwent breast-conservation surgery. Contours of target volumes (CTVs), organs, and heart substructures were generated using two commercial ABAS solutions and DLBAS using fully convolutional DenseNet. The accuracy of the segmentation was assessed using 14 test patients using the Dice Similarity Coefficient and Hausdorff Distance referencing the expert contours. A sensitivity analysis was performed using non-contrast planning CT from 14 additional patients. Compared to ABAS, the proposed DLBAS model yielded more consistent results and the highest average Dice Similarity Coefficient values and lowest Hausdorff Distances, especially for CTVs and the substructures of the heart. ABAS showed limited performance in soft-tissue-based regions, such as the esophagus, cardiac arteries, and smaller CTVs. The results of sensitivity analysis between contrast and non-contrast CT test sets showed little difference in the performance of DLBAS and conversely, a large discrepancy for ABAS. The proposed DLBAS algorithm was more consistent and robust in its performance than ABAS across the majority of structures when examining both CTVs and normal organs. DLBAS has great potential to aid a key process in the radiation therapy workflow, helping optimise and reduce the clinical workload.