ArticleBioMed research international2017
Automatic Segmentation of Ultrasound Tomography Image.
Article in BioMed research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Benchmarking YOLOs in breast ultrasound lesion segmentation.Quantitative imaging in medicine and surgery · 2026Article
- MAAR-Net: Multi-scale attention-assisted residual neural network for renal microvascular structure segmentation.PloS one · 2026Article
- Novel segmentation algorithm for high-throughput analysis of spectral domain-optical coherence tomography imaging of teleost retinas.Molecular vision · 2022Article
- Update on thyroid ultrasound: a narrative review from diagnostic criteria to artificial intelligence techniques.Chinese medical journal · 2019Article
- Erratum to "Automatic Segmentation of Ultrasound Tomography Image".BioMed research international · 2017Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ultrasound tomography (UST) image segmentation is fundamental in breast density estimation, medicine response analysis, and anatomical change quantification. Existing methods are time consuming and require massive manual interaction. To address these issues, an automatic algorithm based on GrabCut (AUGC) is proposed in this paper. The presented method designs automated GrabCut initialization for incomplete labeling and is sped up with multicore parallel programming. To verify performance, AUGC is applied to segment thirty-two in vivo UST volumetric images. The performance of AUGC is validated with breast overlapping metrics (Dice coefficient (
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