ArticleMedical physics2020
CT prostate segmentation based on synthetic MRI-aided deep attention fully convolution network.
Article in Medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
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- A review of artificial intelligence in brachytherapy.Journal of applied clinical medical physics · 2025Review
- Achieving accurate prostate auto-segmentation on CT in the absence of MR imaging.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2025Article
- On-board synthetic 4D MRI generation from 4D CBCT for radiotherapy of abdominal tumors: A feasibility study.Medical physics · 2024Article
- Deep learning based clinical target volumes contouring for prostate cancer: Easy and efficient application.Journal of applied clinical medical physics · 2024Article
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- The use of deep learning in interventional radiotherapy (brachytherapy): A review with a focus on open source and open data.Zeitschrift fur medizinische Physik · 2024Review
- Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals.Korean journal of radiology · 2024Review
- Shortening image registration time using a deep neural network for patient positional verification in radiotherapy.Physical and engineering sciences in medicine · 2023Article
- Overcoming the Challenges in the Development and Implementation of Artificial Intelligence in Radiology: A Comprehensive Review of Solutions Beyond Supervised Learning.Korean journal of radiology · 2023Review
- Adaptive Region-Specific Loss for Improved Medical Image Segmentation.IEEE transactions on pattern analysis and machine intelligence · 2023Article
- Deep neural network-based synthetic image digital fluoroscopy using digitally reconstructed tomography.Physical and engineering sciences in medicine · 2023Article
- Automatic segmentation of neurovascular bundle on mri using deep learning based topological modulated network.Medical physics · 2023Article
- 2D medical image synthesis using transformer-based denoising diffusion probabilistic model.Physics in medicine and biology · 2023Article
- Abdomen CT multi-organ segmentation using token-based MLP-Mixer.Medical physics · 2023Article
- [Segmentation of prostate region in magnetic resonance images based on improved V-Net].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
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- Male pelvic multi-organ segmentation using token-based transformer Vnet.Physics in medicine and biology · 2022Article
- Patient-specific synthetic magnetic resonance imaging generation from cone beam computed tomography for image guidance in liver stereotactic body radiation therapy.Precision radiation oncology · 2022Article
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Authors and funding
13 authors.
Funding
Abstract
purposeAccurate 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.
resultsThe 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.
conclusionsWe 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.
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