ArticleRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology2019
Synthetic MRI-aided multi-organ segmentation on male pelvic CT using cycle consistent deep attention network.
Article in Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 2 of them syntheses that pooled it.
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61 citing papers in PubMed, 2 syntheses or guidelines pooled it, 136 citations in OpenAlex.
- Deep learning in MRI-guided radiation therapy: A systematic review.Journal of applied clinical medical physics · 2024Pooled it
- Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review.Journal of medical systems · 2020Pooled it
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- Generative Models for Medical Image Creation and Translation: A Scoping Review.Sensors (Basel, Switzerland) · 2026Article
- Automatic segmentation of cone beam CT images using treatment planning CT images in patients with prostate cancer.Radiological physics and technology · 2025Article
- A review of artificial intelligence in brachytherapy.Journal of applied clinical medical physics · 2025Review
- Domain-Adaptive and Per-Fraction Guided Deep Learning Framework for Magnetic Resonance Imaging-Based Segmentation of Organs at Risk in Gynecologic Cancers.Advances in radiation oncology · 2025Article
- Deep learning with attention modules and residual transformations improves hepatocellular carcinoma (HCC) differentiation using multiphase CT.Precision radiation oncology · 2025Article
- tUbeNet: a generalizable deep learning tool for 3D vessel segmentation.Biology methods & protocols · 2025Article
- 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
- Article
- Cascaded cross-attention transformers and convolutional neural networks for multi-organ segmentation in male pelvic computed tomography.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Attention-Gated Deep-Learning-Based Automatic Digitization of Interstitial Needles in High-Dose-Rate Brachytherapy for Cervical Cancer.Advances in radiation oncology · 2024Article
- Shortening image registration time using a deep neural network for patient positional verification in radiotherapy.Physical and engineering sciences in medicine · 2023Article
- Machine Learning for Medical Image Translation: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- TISS-net: Brain tumor image synthesis and segmentation using cascaded dual-task networks and error-prediction consistency.Neurocomputing · 2023Article
- Radiograph-comparable image synthesis for spine alignment analysis using deep learning with prospective clinical validation.EClinicalMedicine · 2023Article
- Abdomen CT multi-organ segmentation using token-based MLP-Mixer.Medical physics · 2023Article
- Article
1 more citing papers are in PubMed but not listed here.
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9 authors at 1 institution in 1 country.
Funding
Abstract
background and purposeManual contouring is labor intensive, and subject to variations in operator knowledge, experience and technique. This work aims to develop an automated computed tomography (CT) multi-organ segmentation method for prostate cancer treatment planning. METHODS AND MATERIALS: The proposed method exploits the superior soft-tissue information provided by synthetic MRI (sMRI) to aid the multi-organ segmentation on pelvic CT images. A cycle generative adversarial network (CycleGAN) was used to estimate sMRIs from CT images. A deep attention U-Net (DAUnet) was trained on sMRI and corresponding multi-organ contours for auto-segmentation. The deep attention strategy was introduced to identify the most relevant features to differentiate different organs. Deep supervision was incorporated into the DAUnet to enhance the features' discriminative ability. Segmented contours of a patient were obtained by feeding CT image into the trained CycleGAN to generate sMRI, which was then fed to the trained DAUnet to generate organ contours. We trained and evaluated our model with 140 datasets from prostate patients.
resultsThe Dice similarity coefficient and mean surface distance between our segmented and bladder, prostate, and rectum manual contours were 0.95 ± 0.03, 0.52 ± 0.22 mm; 0.87 ± 0.04, 0.93 ± 0.51 mm; and 0.89 ± 0.04, 0.92 ± 1.03 mm, respectively.
conclusionWe proposed a sMRI-aided multi-organ automatic segmentation method on pelvic CT images. By integrating deep attention and deep supervision strategy, the proposed network provides accurate and consistent prostate, bladder and rectum segmentation, and has the potential to facilitate routine prostate-cancer radiotherapy treatment planning.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.