ArticleJournal of medical imaging (Bellingham, Wash.)2020
Automatic segmentation of brain tumor resections in intraoperative ultrasound images using U-Net.
Article in Journal of medical imaging (Bellingham, Wash.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 27 citations in OpenAlex.
- U-ConvNext: A Robust Approach to Glioma Segmentation in Intraoperative Ultrasound.Journal of imaging informatics in medicine · 2026Article
- Article
- Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasound.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Optimizing diffuse optical imaging for breast tissues with a dual-encoder neural network to preserve small structural information and fine features.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Intraoperative MR Imaging during Glioma Resection.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2022Review
- Fully automated segmentation in temporal bone CT with neural network: a preliminary assessment study.BMC medical imaging · 2021Article
- Enhanced registration of ultrasound volumes by segmentation of resection cavity in neurosurgical procedures.International journal of computer assisted radiology and surgery · 2020Article
- Automatic segmentation of brain tumor resections in intraoperative ultrasound images using U-Net.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- The Essential Role of Open Data and Software for the Future of Ultrasound-Based Neuronavigation.Frontiers in oncology · 2020Article
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To compensate for the intraoperative brain tissue deformation, computer-assisted intervention methods have been used to register preoperative magnetic resonance images with intraoperative images. In order to model the deformation due to tissue resection, the resection cavity needs to be segmented in intraoperative images. We present an automatic method to segment the resection cavity in intraoperative ultrasound (iUS) images. We trained and evaluated two-dimensional (2-D) and three-dimensional (3-D) U-Net networks on two datasets of 37 and 13 cases that contain images acquired from different ultrasound systems. The best overall performing method was the 3-D network, which resulted in a 0.72 mean and 0.88 median Dice score over the whole dataset. The 2-D network also had good results with less computation time, with a median Dice score over 0.8. We also evaluated the sensitivity of network performance to training and testing with images from different ultrasound systems and image field of view. In this application, we found specialized networks to be more accurate for processing similar images than a general network trained with all the data. Overall, promising results were obtained for both datasets using specialized networks. This motivates further studies with additional clinical data, to enable training and validation of a clinically viable deep-learning model for automated delineation of the tumor resection cavity in iUS images.
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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.