Synthetic CT-based multi-organ segmentation in cone beam CT for adaptive pancreatic radiotherapy

Xianjin Dai; Yang Lei; James Janopaul-Naylor; Tonghe Wang; Justin Roper; Jun Zhou; Walter J. Curran; Tian Liu; Pretesh Patel; Xiaofeng Yang

doi:10.1117/12.2581132

15 February 2021 Synthetic CT-based multi-organ segmentation in cone beam CT for adaptive pancreatic radiotherapy

Xianjin Dai, Yang Lei, James Janopaul-Naylor, Tonghe Wang, Justin Roper, Jun Zhou, Walter J. Curran, Tian Liu, Pretesh Patel, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 1159623 (2021) https://doi.org/10.1117/12.2581132
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

The inter-fraction motion management of pancreatic radiotherapy remains a challenge in current clinical practice. CBCT-based adaptive radiotherapy is an emerging technique for either offline or online plan adaptations. Accurately delineating tumor targets and organs-at-risk (OARs) is an important step in adaptive re-planning process; however, manual delineation can be labor-intensive and time-consuming. Especially for online adaptation, rapid re-planning is generally required. In this study, we present a fully automated delineation method to expedite the contouring process of adaptive radiotherapy re-planning and dose-volume based plan evaluation and monitoring. In particular, to avoid scatter artifact from CBCT and improve the image quality, a cycle-consistent adversarial network was firstly used to generate synthetic CT given CBCT. Then, a mask scoring regional neural network (RCNN) has been developed to extract the features from synthetic CT for obtaining final segmentation. Metrics including Dice similarity coefficient (DSC), Hausdorff distance 95% (HD95), mean surface distance (MSD), and residual mean square distance (RMS) were used to evaluate our proposed method. Overall, DSC values ranging from 0.82 to 0.94 were achieved among 8 organs.

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Xianjin Dai, Yang Lei, James Janopaul-Naylor, Tonghe Wang, Justin Roper, Jun Zhou, Walter J. Curran, Tian Liu, Pretesh Patel, and Xiaofeng Yang "Synthetic CT-based multi-organ segmentation in cone beam CT for adaptive pancreatic radiotherapy", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 1159623 (15 February 2021); https://doi.org/10.1117/12.2581132

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