ArticleJournal of medical imaging (Bellingham, Wash.)2014
Segmentation of breast masses on dedicated breast computed tomography and three-dimensional breast ultrasound images.
Article in Journal of medical imaging (Bellingham, Wash.), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 21 citations in OpenAlex.
- Article
- BUSIS: A Benchmark for Breast Ultrasound Image Segmentation.Healthcare (Basel, Switzerland) · 2022Review
- Study on automatic detection and classification of breast nodule using deep convolutional neural network system.Journal of thoracic disease · 2020Article
- Combinatorial active contour bilateral filter for ultrasound image segmentation.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Signal dropout correction-based ultrasound segmentation for diastolic mitral valve modeling.Journal of medical imaging (Bellingham, Wash.) · 2018Article
- Neutrosophic segmentation of breast lesions for dedicated breast computed tomography.Journal of medical imaging (Bellingham, Wash.) · 2018Article
- Automatic extraction of cell nuclei from H&E-stained histopathological images.Journal of medical imaging (Bellingham, Wash.) · 2017Article
- Impact of lesion segmentation metrics on computer-aided diagnosis/detection in breast computed tomography.Journal of medical imaging (Bellingham, Wash.) · 2014Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
We present and evaluate a method for the three-dimensional (3-D) segmentation of breast masses on dedicated breast computed tomography (bCT) and automated 3-D breast ultrasound images. The segmentation method, refined from our previous segmentation method for masses on contrast-enhanced bCT, includes two steps: (1) initial contour estimation and (2) active contour-based segmentation to further evolve and refine the initial contour by adding a local energy term to the level-set equation. Segmentation performance was assessed in terms of Dice coefficients (DICE) for 129 lesions on noncontrast bCT, 38 lesions on contrast-enhanced bCT, and 98 lesions on 3-D breast ultrasound (US) images. For bCT, DICE values of 0.82 and 0.80 were obtained on contrast-enhanced and noncontrast images, respectively. The improvement in segmentation performance with respect to that of our previous method was statistically significant (
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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.