ArticleMedical image analysis2019
CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation.
Article in Medical image analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review.Journal of medical systems · 2020Pooled it
- A Reproducible COCO-Polygon Quality-Control Pipeline Improves Segmentation Stability in Endoscopic Airway Imaging.Diagnostics (Basel, Switzerland) · 2026Article
- The Robust Vessel Segmentation and Centerline Extraction: One-Stage Deep Learning Approach.Journal of imaging · 2025Article
- Evaluating the dosimetric impact of deep-learning-based auto-segmentation in prostate cancer radiotherapy: Insights into real-world clinical implementation and inter-observer variability.Journal of applied clinical medical physics · 2025Article
- An efficient segment anything model for the segmentation of medical images.Scientific reports · 2024Article
- Automated contouring of CTV and OARs in planning CT scans using novel hybrid convolution-transformer networks for prostate cancer radiotherapy.Discover oncology · 2024Article
- An AS-OCT image dataset for deep learning-enabled segmentation and 3D reconstruction for keratitis.Scientific data · 2024Article
- Artificial Intelligence-Based Organ Delineation for Radiation Treatment Planning of Prostate Cancer on Computed Tomography.Advances in radiation oncology · 2024Article
- Cascaded cross-attention transformers and convolutional neural networks for multi-organ segmentation in male pelvic computed tomography.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Article
- A research on the improved rotational robustness for thoracic organ delineation by using joint learning of segmenting spatially-correlated organs: A U-net based comparison.Journal of applied clinical medical physics · 2023Article
- Adaptive Region-Specific Loss for Improved Medical Image Segmentation.IEEE transactions on pattern analysis and machine intelligence · 2023Article
- Self-configuring nnU-Net for automatic delineation of the organs at risk and target in high-dose rate cervical brachytherapy, a low/middle-income country's experience.Journal of applied clinical medical physics · 2023Article
- Automatic segmentation of the female pelvic floor muscles on MRI for pelvic floor function assessment.Quantitative imaging in medicine and surgery · 2023Article
- Deep learning-based segmentation of prostatic urethra on computed tomography scans for treatment planning.Physics and imaging in radiation oncology · 2023Article
- Dynamic Cross-Task Representation Adaptation for Clinical Targets Co-Segmentation in CT Image-Guided Post-Prostatectomy Radiotherapy.IEEE transactions on medical imaging · 2023Article
- A skeleton context-aware 3D fully convolutional network for abdominal artery segmentation.International journal of computer assisted radiology and surgery · 2023Article
- Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy.Nature communications · 2022Article
- Can Generative Adversarial Networks help to overcome the limited data problem in segmentation?Zeitschrift fur medizinische Physik · 2022Article
- Shadow-Consistent Semi-Supervised Learning for Prostate Ultrasound Segmentation.IEEE transactions on medical imaging · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
Accurate segmentation of the prostate and organs at risk (e.g., bladder and rectum) in CT images is a crucial step for radiation therapy in the treatment of prostate cancer. However, it is a very challenging task due to unclear boundaries, large intra- and inter-patient shape variability, and uncertain existence of bowel gases and fiducial markers. In this paper, we propose a novel automatic segmentation framework using fully convolutional networks with boundary sensitive representation to address this challenging problem. Our novel segmentation framework contains three modules. First, an organ localization model is designed to focus on the candidate segmentation region of each organ for better performance. Then, a boundary sensitive representation model based on multi-task learning is proposed to represent the semantic boundary information in a more robust and accurate manner. Finally, a multi-label cross-entropy loss function combining boundary sensitive representation is introduced to train a fully convolutional network for the organ segmentation. The proposed method is evaluated on a large and diverse planning CT dataset with 313 images from 313 prostate cancer patients. Experimental results show that the performance of our proposed method outperforms the baseline fully convolutional networks, as well as other state-of-the-art methods in CT male pelvic organ segmentation.
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Registered trials
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.