Prostate and dominant intraprostatic lesion segmentation on PET/CT using cascaded regional-net.

Autor:	Matkovic LA; Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America.; School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America., Wang T; Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America.; Winship Cancer Institute, Emory University, Atlanta, GA, United States of America., Lei Y; Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America., Akin-Akintayo OO; Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, United States of America., Abiodun Ojo OA; Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, United States of America., Akintayo AA; Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, United States of America., Roper J; Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America.; School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America.; Winship Cancer Institute, Emory University, Atlanta, GA, United States of America., Bradley JD; Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America.; Winship Cancer Institute, Emory University, Atlanta, GA, United States of America., Liu T; Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America.; Winship Cancer Institute, Emory University, Atlanta, GA, United States of America., Schuster DM; Winship Cancer Institute, Emory University, Atlanta, GA, United States of America.; Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, United States of America., Yang X; Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America.; School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America.; Winship Cancer Institute, Emory University, Atlanta, GA, United States of America.
Jazyk:	angličtina
Zdroj:	Physics in medicine and biology [Phys Med Biol] 2021 Dec 07; Vol. 66 (24). Date of Electronic Publication: 2021 Dec 07.
DOI:	10.1088/1361-6560/ac3c13
Abstrakt:	Focal boost to dominant intraprostatic lesions (DILs) has recently been proposed for prostate radiation therapy. Accurate and fast delineation of the prostate and DILs is thus required during treatment planning. In this paper, we develop a learning-based method using positron emission tomography (PET)/computed tomography (CT) images to automatically segment the prostate and its DILs. To enable end-to-end segmentation, a deep learning-based method, called cascaded regional-Net, is utilized. The first network, referred to as dual attention network, is used to segment the prostate via extracting comprehensive features from both PET and CT images. A second network, referred to as mask scoring regional convolutional neural network (MSR-CNN), is used to segment the DILs from the PET and CT within the prostate region. Scoring strategy is used to diminish the misclassification of the DILs. For DIL segmentation, the proposed cascaded regional-Net uses two steps to remove normal tissue regions, with the first step cropping images based on prostate segmentation and the second step using MSR-CNN to further locate the DILs. The binary masks of DILs and prostates of testing patients are generated on the PET/CT images by the trained model. For evaluation, we retrospectively investigated 49 prostate cancer patients with PET/CT images acquired. The prostate and DILs of each patient were contoured by radiation oncologists and set as the ground truths and targets. We used five-fold cross-validation and a hold-out test to train and evaluate our method. The mean surface distance and DSC values were 0.666 ± 0.696 mm and 0.932 ± 0.059 for the prostate and 0.814 ± 1.002 mm and 0.801 ± 0.178 for the DILs among all 49 patients. The proposed method has shown promise for facilitating prostate and DIL delineation for DIL focal boost prostate radiation therapy. (© 2021 Institute of Physics and Engineering in Medicine.)
Databáze:	MEDLINE
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