ArticleIEEE transactions on medical imaging2019
Pelvic Organ Segmentation Using Distinctive Curve Guided Fully Convolutional Networks.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 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
- Semi-supervised deep learning for uterus and bladder segmentation on female pelvic floor magnetic resonance imaging with limited labeled data.American journal of obstetrics and gynecology · 2026Article
- Prior guided deep difference meta-learner for fast adaptation to stylized segmentation.Machine learning: science and technology · 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
- Automated contouring of CTV and OARs in planning CT scans using novel hybrid convolution-transformer networks for prostate cancer radiotherapy.Discover oncology · 2024Article
- 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
- 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
- Semi-Supervised Medical Image Segmentation Guided by Bi-Directional Constrained Dual-Task Consistency.Bioengineering (Basel, Switzerland) · 2023Article
- COVID-19 Screening in Chest X-Ray Images Using Lung Region Priors.IEEE journal of biomedical and health informatics · 2021Article
- Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges.Diagnostics (Basel, Switzerland) · 2021Review
- Asymmetric multi-task attention network for prostate bed segmentation in computed tomography images.Medical image analysis · 2021Article
- Attention-RefNet: Interactive Attention Refinement Network for Infected Area Segmentation of COVID-19.IEEE journal of biomedical and health informatics · 2021Article
- Research on obtaining pseudo CT images based on stacked generative adversarial network.Quantitative imaging in medicine and surgery · 2021Article
- Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of COVID-19 in CT images.Pattern recognition · 2021Article
- A review of deep learning based methods for medical image multi-organ segmentation.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2021Review
- Semi-automatic sigmoid colon segmentation in CT for radiation therapy treatment planning via an iterative 2.5-D deep learning approach.Medical image analysis · 2021Article
- Boundary Coding Representation for Organ Segmentation in Prostate Cancer Radiotherapy.IEEE transactions on medical imaging · 2021Article
- Imaging Study of Pseudo-CT Synthesized From Cone-Beam CT Based on 3D CycleGAN in Radiotherapy.Frontiers in oncology · 2021Article
- A Deep Learning-Based Automated CT Segmentation of Prostate Cancer Anatomy for Radiation Therapy Planning-A Retrospective Multicenter Study.Diagnostics (Basel, Switzerland) · 2020Article
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Authors and funding
6 authors.
Funding
Abstract
Accurate segmentation of pelvic organs (i.e., prostate, bladder, and rectum) from CT image is crucial for effective prostate cancer radiotherapy. However, it is a challenging task due to: 1) low soft tissue contrast in CT images and 2) large shape and appearance variations of pelvic organs. In this paper, we employ a two-stage deep learning-based method, with a novel distinctive curve-guided fully convolutional network (FCN), to solve the aforementioned challenges. Specifically, the first stage is for fast and robust organ detection in the raw CT images. It is designed as a coarse segmentation network to provide region proposals for three pelvic organs. The second stage is for fine segmentation of each organ, based on the region proposal results. To better identify those indistinguishable pelvic organ boundaries, a novel morphological representation, namely, distinctive curve, is also introduced to help better conduct the precise segmentation. To implement this, in this second stage, a multi-task FCN is initially utilized to learn the distinctive curve and the segmentation map separately and then combine these two tasks to produce accurate segmentation map. The final segmentation results of all three pelvic organs are generated by a weighted max-voting strategy. We have conducted exhaustive experiments on a large and diverse pelvic CT data set for evaluating our proposed method. The experimental results demonstrate that our proposed method is accurate and robust for this challenging segmentation task, by also outperforming the state-of-the-art segmentation methods.
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