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A Deep Learning-based Framework for Segmenting Invisible Clinical Target Volumes with Estimated Uncertainties for Post-operative Prostate Cancer Radiotherapy by Balagopal, Anjali; Nguyen, Dan; Morgan, Howard; Weng, Yaochung; Dohopolski, Michael; Lin, Mu-Han; Barkousaraie, Azar Sadeghnejad; Gonzalez, Yesenia; Garant, Aurelie; Desai, Neil; Hannan, Raquibul; Jiang, Steve is a scholarly article available to read on EtoBox.

What is A Deep Learning-based Framework for Segmenting Invisible Clinical Target Volumes with Estimated Uncertainties for Post-operative Prostate Cancer Radiotherapy about?

In post-operative radiotherapy for prostate cancer, the cancerous prostate gland has been surgically removed, so the clinical target volume (CTV) to be irradiated encompasses the microscopic spread of tumor cells, which cannot be visualized in typical clinical images such as computed tomography or magnetic resonance imaging. In current clinical practice, physicians segment CTVs manually based on their relationship with nearby organs and other clinical information, per clinical guidelines. Automating post-operative prostate CTV segmentation with traditional image segmentation methods has been a major challenge. Here, we propose a deep learning model to overcome this problem by segmenting nearby organs first, then using their relationship with the CTV to assist CTV segmentation. The model proposed is trained using labels clinically approved and used for patient treatment, which are subject to relatively large inter-physician variations due to the absence of a visual ground truth. The model achieves an average Dice similarity coefficient (DSC) of 0.87 on a holdout dataset of 50 patients, much better than established methods, such as atlas-based methods (DSC<0.7). The uncertainties ass

Author

Balagopal, Anjali; Nguyen, Dan; Morgan, Howard; Weng, Yaochung; Dohopolski, Michael; Lin, Mu-Han; Barkousaraie, Azar Sadeghnejad; Gonzalez, Yesenia; Garant, Aurelie; Desai, Neil; Hannan, Raquibul; Jiang, Steve

Published

2020

Language

EN