PURPOSE: Accurate segmentation of the prostate on computed tomography (CT) for treatment planning is challenging due to CT's poor soft tissue contrast. Magnetic resonance imaging (MRI) has been used to aid prostate delineation, but its final accuracy is limited by MRI-CT registration errors. We developed a deep attention-based segmentation strategy on CT-based synthetic MRI (sMRI) to deal with the CT prostate delineation challenge without MRI acquisition. METHODS AND MATERIALS: We developed a prostate segmentation strategy which employs an sMRI-aided deep attention network to accurately segment the prostate on CT. Our method consists of three major steps. First, a cycle generative adversarial network was used to estimate an sMRI from CT images. Second, a deep attention fully convolution network was trained based on sMRI and the prostate contours deformed from MRIs. Attention models were introduced to pay more attention to prostate boundary. The prostate contour for a query patient was obtained by feeding the patient's CT images into the trained sMRI generation model and segmentation model. RESULTS: The segmentation technique was validated with a clinical study of 49 patients by leave-one-out experiments and validated with an additional 50 patients by hold-out test. The Dice similarity coefficient, Hausdorff distance, and mean surface distance indices between our segmented and deformed MRI-defined prostate manual contours were 0.92 ± 0.09, 4.38 ± 4.66, and 0.62 ± 0.89 mm, respectively, with leave-one-out experiments, and were 0.91 ± 0.07, 4.57 ± 3.03, and 0.62 ± 0.65 mm, respectively, with hold-out test. CONCLUSIONS: We have proposed a novel CT-only prostate segmentation strategy using CT-based sMRI, and validated its accuracy against the prostate contours that were manually drawn on MRI images and deformed to CT images. This technique could provide accurate prostate volume for treatment planning without requiring MRI acquisition, greatly facilitating the routine clinical workflow.
PURPOSE: Accurate segmentation of the prostate on computed tomography (CT) for treatment planning is challenging due to CT's poor soft tissue contrast. Magnetic resonance imaging (MRI) has been used to aid prostate delineation, but its final accuracy is limited by MRI-CT registration errors. We developed a deep attention-based segmentation strategy on CT-based synthetic MRI (sMRI) to deal with the CT prostate delineation challenge without MRI acquisition. METHODS AND MATERIALS: We developed a prostate segmentation strategy which employs an sMRI-aided deep attention network to accurately segment the prostate on CT. Our method consists of three major steps. First, a cycle generative adversarial network was used to estimate an sMRI from CT images. Second, a deep attention fully convolution network was trained based on sMRI and the prostate contours deformed from MRIs. Attention models were introduced to pay more attention to prostate boundary. The prostate contour for a query patient was obtained by feeding the patient's CT images into the trained sMRI generation model and segmentation model. RESULTS: The segmentation technique was validated with a clinical study of 49 patients by leave-one-out experiments and validated with an additional 50 patients by hold-out test. The Dice similarity coefficient, Hausdorff distance, and mean surface distance indices between our segmented and deformed MRI-defined prostate manual contours were 0.92 ± 0.09, 4.38 ± 4.66, and 0.62 ± 0.89 mm, respectively, with leave-one-out experiments, and were 0.91 ± 0.07, 4.57 ± 3.03, and 0.62 ± 0.65 mm, respectively, with hold-out test. CONCLUSIONS: We have proposed a novel CT-only prostate segmentation strategy using CT-based sMRI, and validated its accuracy against the prostate contours that were manually drawn on MRI images and deformed to CT images. This technique could provide accurate prostate volume for treatment planning without requiring MRI acquisition, greatly facilitating the routine clinical workflow.
Authors: Yaozong Gao; Yeqin Shao; Jun Lian; Andrew Z Wang; Ronald C Chen; Dinggang Shen Journal: IEEE Trans Med Imaging Date: 2016-01-18 Impact factor: 10.048
Authors: Søren M Bentzen; Louis S Constine; Joseph O Deasy; Avi Eisbruch; Andrew Jackson; Lawrence B Marks; Randall K Ten Haken; Ellen D Yorke Journal: Int J Radiat Oncol Biol Phys Date: 2010-03-01 Impact factor: 7.038
Authors: Kimberly D Miller; Rebecca L Siegel; Chun Chieh Lin; Angela B Mariotto; Joan L Kramer; Julia H Rowland; Kevin D Stein; Rick Alteri; Ahmedin Jemal Journal: CA Cancer J Clin Date: 2016-06-02 Impact factor: 508.702
Authors: Luke A Matkovic; Tonghe Wang; Yang Lei; Oladunni O Akin-Akintayo; Olayinka A Abiodun Ojo; Akinyemi A Akintayo; Justin Roper; Jeffery D Bradley; Tian Liu; David M Schuster; Xiaofeng Yang Journal: Phys Med Biol Date: 2021-12-07 Impact factor: 3.609
Authors: Serdar Charyyev; Tonghe Wang; Yang Lei; Beth Ghavidel; Jonathan J Beitler; Mark McDonald; Walter J Curran; Tian Liu; Jun Zhou; Xiaofeng Yang Journal: Br J Radiol Date: 2021-10-28 Impact factor: 3.039
Authors: Tonghe Wang; Yang Lei; Yabo Fu; Walter J Curran; Tian Liu; Jonathon A Nye; Xiaofeng Yang Journal: Phys Med Date: 2020-07-29 Impact factor: 2.685
Authors: Xianjin Dai; Yang Lei; Yupei Zhang; Richard L J Qiu; Tonghe Wang; Sean A Dresser; Walter J Curran; Pretesh Patel; Tian Liu; Xiaofeng Yang Journal: Med Phys Date: 2020-06-15 Impact factor: 4.071
Authors: Tonghe Wang; Yang Lei; Yabo Fu; Jacob F Wynne; Walter J Curran; Tian Liu; Xiaofeng Yang Journal: J Appl Clin Med Phys Date: 2020-12-11 Impact factor: 2.102