ArticlePhysics in medicine and biology2020
Automatic multi-needle localization in ultrasound images using large margin mask RCNN for ultrasound-guided prostate brachytherapy.
Article in Physics in medicine and biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 35 citations in OpenAlex.
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- Systematic review of artificial intelligence in brachytherapy.Journal of contemporary brachytherapy · 2026Review
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- Artificial intelligence in interventional radiotherapy (brachytherapy): Enhancing patient-centered care and addressing patients' needs.Clinical and translational radiation oncology · 2024Review
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- 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
- Ultrasound-guided needle tracking with deep learning: A novel approach with photoacoustic ground truth.Photoacoustics · 2023Article
- Landmark tracking in liver US images using cascade convolutional neural networks with long short-term memory.Measurement science & technology · 2023Article
- MRI-based prostate and dominant lesion segmentation using cascaded scoring convolutional neural network.Medical physics · 2022Article
- Deep Learning-Based Chest CT Image Features in Diagnosis of Lung Cancer.Computational and mathematical methods in medicine · 2022Article
- Male pelvic multi-organ segmentation on transrectal ultrasound using anchor-free mask CNN.Medical physics · 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
- Action Recognition Using a Spatial-Temporal Network for Wild Felines.Animals : an open access journal from MDPI · 2021Article
- Deep learning-based digitization of prostate brachytherapy needles in ultrasound images.Medical physics · 2020Article
- Instant plan quality prediction on transrectal ultrasound for high-dose-rate prostate brachytherapy.BrachytherapyArticle
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
Multi-needle localization in ultrasound (US) images is a crucial step of treatment planning for US-guided prostate brachytherapy. However, current computer-aided technologies are mostly focused on single-needle digitization, while manual digitization is labor intensive and time consuming. In this paper, we proposed a deep learning-based workflow for fast automatic multi-needle digitization, including needle shaft detection and needle tip detection. The major workflow is composed of two components: a large margin mask R-CNN model (LMMask R-CNN), which adopts the lager margin loss to reformulate Mask R-CNN for needle shaft localization, and a needle based density-based spatial clustering of application with noise algorithm which integrates priors to model a needle in an iteration for a needle shaft refinement and tip detections. Besides, we use the skipping connection in neural network architecture to improve the supervision in hidden layers. Our workflow was evaluated on 23 patients who underwent US-guided high-dose-rate (HDR) prostrate brachytherapy with 339 needles being tested in total. Our method detected 98% of the needles with 0.091 ± 0.043 mm shaft error and 0.330 ± 0.363 mm tip error. Compared with only using Mask R-CNN and only using LMMask R-CNN, the proposed method gains a significant improvement on both shaft error and tip error. The proposed method automatically digitizes needles per patient with in a second. It streamlines the workflow of transrectal ultrasound-guided HDR prostate brachytherapy and paves the way for the development of real-time treatment planning system that is expected to further elevate the quality and outcome of HDR prostate brachytherapy.
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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.