ArticleInternational journal of computer assisted radiology and surgery2020
A deep learning method for real-time intraoperative US image segmentation in prostate brachytherapy.
Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Deep learning auto-contouring of target and organs-at-risk on post-catheter implant CT images for prostate HDR brachytherapy.Journal of applied clinical medical physics · 2026Article
- Dose control in robot-assisted seed brachytherapy: challenges, current status, and future directions.Journal of robotic surgery · 2026Review
- Systematic review of artificial intelligence in brachytherapy.Journal of contemporary brachytherapy · 2026Review
- A review of artificial intelligence in brachytherapy.Journal of applied clinical medical physics · 2025Review
- Automatic medical imaging segmentation via self-supervising large-scale convolutional neural networks.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2025Article
- Artificial intelligence in interventional radiotherapy (brachytherapy): Enhancing patient-centered care and addressing patients' needs.Clinical and translational radiation oncology · 2024Review
- Article
- 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
- A Robust and Explainable Structure-Based Algorithm for Detecting the Organ Boundary From Ultrasound Multi-Datasets.Journal of digital imaging · 2023Article
- Ultrasound Prostate Segmentation Using Adaptive Selection Principal Curve and Smooth Mathematical Model.Journal of digital imaging · 2023Article
- Intelligent contour extraction approach for accurate segmentation of medical ultrasound images.Frontiers in physiology · 2023Article
- A Web-Based Automated Image Processing Research Platform for Cochlear Implantation-Related Studies.Journal of clinical medicine · 2022Article
- Domain generalization for prostate segmentation in transrectal ultrasound images: A multi-center study.Medical image analysis · 2022Article
- Review
- Automatic segmentation of inner ear on CT-scan using auto-context convolutional neural network.Scientific reports · 2021Article
- Instant plan quality prediction on transrectal ultrasound for high-dose-rate prostate brachytherapy.BrachytherapyArticle
- A review of artificial intelligence in prostate cancer detection on imaging.Therapeutic advances in urologyReview
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
purposeThis paper addresses the detection of the clinical target volume (CTV) in transrectal ultrasound (TRUS) image-guided intraoperative for permanent prostate brachytherapy. Developing a robust and automatic method to detect the CTV on intraoperative TRUS images is clinically important to have faster and reproducible interventions that can benefit both the clinical workflow and patient health.
methodsWe present a multi-task deep learning method for an automatic prostate CTV boundary detection in intraoperative TRUS images by leveraging both the low-level and high-level (prior shape) information. Our method includes a channel-wise feature calibration strategy for low-level feature extraction and learning-based prior knowledge modeling for prostate CTV shape reconstruction. It employs CTV shape reconstruction from automatically sampled boundary surface coordinates (pseudo-landmarks) to detect the low-contrast and noisy regions across the prostate boundary, while being less biased from shadowing, inherent speckles, and artifact signals from the needle and implanted radioactive seeds.
resultsThe proposed method was evaluated on a clinical database of 145 patients who underwent permanent prostate brachytherapy under TRUS guidance. Our method achieved a mean accuracy of [Formula: see text] and a mean surface distance error of [Formula: see text]. Extensive ablation and comparison studies show that our method outperformed previous deep learning-based methods by more than 7% for the Dice similarity coefficient and 6.9 mm reduced 3D Hausdorff distance error.
conclusionOur study demonstrates the potential of shape model-based deep learning methods for an efficient and accurate CTV segmentation in an ultrasound-guided intervention. Moreover, learning both low-level features and prior shape knowledge with channel-wise feature calibration can significantly improve the performance of deep learning methods in medical image segmentation.
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