ArticleIEEE transactions on bio-medical engineering2019
Three-Dimensional Whole Breast Segmentation in Sagittal and Axial Breast MRI With Dense Depth Field Modeling and Localized Self-Adaptation for Chest-Wall Line Detection.
Article in IEEE transactions on bio-medical engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 26 citations in OpenAlex.
- Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers-Based Model.Cancer medicine · 2024Article
- MRI-based Deep Learning Models for Preoperative Breast Volume and Density Assessment Assisting Breast Reconstruction.Aesthetic plastic surgery · 2024Article
- Fully automated breast segmentation on spiral breast computed tomography images.Journal of applied clinical medical physics · 2022Article
- Fully automatic quantification of fibroglandular tissue and background parenchymal enhancement with accurate implementation for axial and sagittal breast MRI protocols.Medical physics · 2021Article
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5 authors at 1 institution in 1 country.
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Abstract
objectiveWhole breast segmentation is an essential task in quantitative analysis of breast MRI for cancer risk assessment. It is challenging, mainly, because the chest-wall line (CWL) can be very difficult to locate due to its spatially varying appearance-caused by both nature and imaging artifacts-and neighboring distracting structures. This paper proposes an automatic three-dimensional (3-D) segmentation method, termed DeepSeA, of whole breast for breast MRI.
methodsDeepSeA distinguishes itself from previous methods in three aspects. First, it reformulates the challenging problem of CWL localization as an equivalent problem that optimizes a smooth depth field and so fully utilizes the CWL's 3-D continuity. Second, it employs a localized self-adapting algorithm to adjust to the CWL's spatial variation. Third, it applies to breast MRI data in both sagittal and axial orientations equally well without training.
resultsA representative set of 99 breast MRI scans with varying imaging protocols is used for evaluation. Experimental results with expert-outlined reference standard show that DeepSeA can segment breasts accurately: the average Dice similarity coefficients, sensitivity, specificity, and CWL deviation error are 96.04%, 97.27%, 98.77%, and 1.63 mm, respectively. In addition, the configuration of DeepSeA is generalized based on experimental findings, for application to broad prospective data.
conclusionA fully automatic method-DeepSeA-for whole breast segmentation in sagittal and axial breast MRI is reported. SIGNIFICANCE: DeepSeA can facilitate cancer risk assessment with breast MRI.
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