ArticleIEEE transactions on medical imaging2020
Multi-Needle Detection in 3D Ultrasound Images Using Unsupervised Order-Graph Regularized Sparse Dictionary Learning.
Article in IEEE transactions on medical imaging, 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, 49 citations in OpenAlex.
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- An MRI Study on Effects of Math Education on Brain Development Using Multi-Instance Contrastive Learning.Frontiers in psychology · 2021Article
- Deep learning-based digitization of prostate brachytherapy needles in ultrasound images.Medical physics · 2020Article
- Automatic multi-needle localization in ultrasound images using large margin mask RCNN for ultrasound-guided prostate brachytherapy.Physics in medicine and biology · 2020Article
- Automatic multi-catheter detection using deeply supervised convolutional neural network in MRI-guided HDR prostate brachytherapy.Medical physics · 2020Article
- Multi-needle Localization with Attention U-Net in US-guided HDR Prostate Brachytherapy.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
12 authors at 1 institution in 1 country.
Funding
Abstract
Accurate and automatic multi-needle detection in three-dimensional (3D) ultrasound (US) is a key step of treatment planning for US-guided brachytherapy. However, most current studies are concentrated on single-needle detection by only using a small number of images with a needle, regardless of the massive database of US images without needles. In this paper, we propose a workflow for multi-needle detection by considering the images without needles as auxiliary. Concretely, we train position-specific dictionaries on 3D overlapping patches of auxiliary images, where we develop an enhanced sparse dictionary learning method by integrating spatial continuity of 3D US, dubbed order-graph regularized dictionary learning. Using the learned dictionaries, target images are reconstructed to obtain residual pixels which are then clustered in every slice to yield centers. With the obtained centers, regions of interest (ROIs) are constructed via seeking cylinders. Finally, we detect needles by using the random sample consensus algorithm per ROI and then locate the tips by finding the sharp intensity drops along the detected axis for every needle. Extensive experiments were conducted on a phantom dataset and a prostate dataset of 70/21 patients without/with needles. Visualization and quantitative results show the effectiveness of our proposed workflow. Specifically, our method can correctly detect 95% of needles with a tip location error of 1.01 mm on the prostate dataset. This technique provides accurate multi-needle detection for US-guided HDR prostate brachytherapy, facilitating the clinical workflow.
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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.