ArticleProceedings of SPIE--the International Society for Optical Engineering2017
Automatic segmentation of the prostate on CT images using deep learning and multi-atlas fusion.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 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
- Deep Learning Techniques for Prostate Cancer Analysis and Detection: Survey of the State of the Art.Journal of imaging · 2025Review
- Prior guided deep difference meta-learner for fast adaptation to stylized segmentation.Machine learning: science and technology · 2025Article
- Automated contouring of CTV and OARs in planning CT scans using novel hybrid convolution-transformer networks for prostate cancer radiotherapy.Discover oncology · 2024Article
- Deep learning based automatic segmentation of the Internal Pudendal Artery in definitive radiotherapy treatment planning of localized prostate cancer.Physics and imaging in radiation oncology · 2024Article
- Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI.European radiology · 2023Article
- Role of Deep Learning in Prostate Cancer Management: Past, Present and Future Based on a Comprehensive Literature Review.Journal of clinical medicine · 2022Review
- Clinical validation of an automatic atlas-based segmentation tool for male pelvis CT images.Journal of applied clinical medical physics · 2022Article
- Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges.Diagnostics (Basel, Switzerland) · 2021Review
- Computer-assisted real-time automatic prostate segmentation during TaTME: a single-center feasibility study.Surgical endoscopy · 2021Article
- Automatic Segmentation of the Prostate on MR Images based on Anatomy and Deep Learning.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Robustness study of noisy annotation in deep learning based medical image segmentation.Physics in medicine and biology · 2020Article
- High-Resolution Encoder-Decoder Networks for Low-Contrast Medical Image Segmentation.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2019Article
- Deep learning-based three-dimensional segmentation of the prostate on computed tomography images.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- A semiautomatic segmentation method for prostate in CT images using local texture classification and statistical shape modeling.Medical physics · 2018Article
- A semiautomatic algorithm for three-dimensional segmentation of the prostate on CT images using shape and local texture characteristics.Proceedings of SPIE--the International Society for Optical Engineering · 2018Article
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Authors and funding
8 authors.
Funding
Abstract
Automatic segmentation of the prostate on CT images has many applications in prostate cancer diagnosis and therapy. However, prostate CT image segmentation is challenging because of the low contrast of soft tissue on CT images. In this paper, we propose an automatic segmentation method by combining a deep learning method and multi-atlas refinement. First, instead of segmenting the whole image, we extract the region of interesting (ROI) to delete irrelevant regions. Then, we use the convolutional neural networks (CNN) to learn the deep features for distinguishing the prostate pixels from the non-prostate pixels in order to obtain the preliminary segmentation results. CNN can automatically learn the deep features adapting to the data, which are different from some handcrafted features. Finally, we select some similar atlases to refine the initial segmentation results. The proposed method has been evaluated on a dataset of 92 prostate CT images. Experimental results show that our method achieved a Dice similarity coefficient of 86.80% as compared to the manual segmentation. The deep learning based method can provide a useful tool for automatic segmentation of the prostate on CT images and thus can have a variety of clinical applications.
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Registered trials
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