ArticleJournal of medical imaging (Bellingham, Wash.)2019
Effective nuclei segmentation with sparse shape prior and dynamic occlusion constraint for glioblastoma pathology images.
Article in Journal of medical imaging (Bellingham, Wash.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Spatial Omics Technologies in Glioblastoma Research: Principles, Applications, and Best Practices.Genes · 2026Review
- Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review.Npj imaging · 2024Review
- Foveal blur-boosted segmentation of nuclei in histopathology images with shape prior knowledge and probability map constraints.Bioinformatics (Oxford, England) · 2021Article
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7 authors.
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Abstract
We propose a segmentation method for nuclei in glioblastoma histopathologic images based on a sparse shape prior guided variational level set framework. By spectral clustering and sparse coding, a set of shape priors is exploited to accommodate complicated shape variations. We automate the object contour initialization by a seed detection algorithm and deform contours by minimizing an energy functional that incorporates a shape term in a sparse shape prior representation, an adaptive contour occlusion penalty term, and a boundary term encouraging contours to converge to strong edges. As a result, our approach is able to deal with mutual occlusions and detect contours of multiple intersected nuclei simultaneously. Our method is applied to several whole-slide histopathologic image datasets for nuclei segmentation. The proposed method is compared with other state-of-the-art methods and demonstrates good accuracy for nuclei detection and segmentation, suggesting its promise to support biomedical image-based investigations.
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Registered trials
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