ArticleComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society2015
Selective invocation of shape priors for deformable segmentation and morphologic classification of prostate cancer tissue microarrays.
Article in Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Biological feature-based machine learning in histopathological images: a systematic review.Journal of pathology informatics · 2026Review
- Quantitative Histomorphometric Features of Prostate Cancer Predict Patients Who Biochemically Recur Following Prostatectomy.Laboratory investigation; a journal of technical methods and pathology · 2023Article
- A hitchhiker's guide to cancer models.Trends in biotechnology · 2022Review
- Prostate Cancer Risk Stratification via Nondestructive 3D Pathology with Deep Learning-Assisted Gland Analysis.Cancer research · 2022Article
- Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study.NPJ precision oncology · 2021Article
- Computerized tumor multinucleation index (MuNI) is prognostic in p16+ oropharyngeal carcinoma.The Journal of clinical investigation · 2021Article
- Automated gleason grading on prostate biopsy slides by statistical representations of homology profile.Computer methods and programs in biomedicine · 2020Article
- Computer-Aided Laser Dissection: A Microdissection Workflow Leveraging Image Analysis Tools.Journal of pathology informatics · 2018Article
- Article
- Development of a Reference Image Collection Library for Histopathology Image Processing, Analysis and Decision Support Systems Research.Journal of digital imaging · 2017Article
- Multi-Pass Adaptive Voting for Nuclei Detection in Histopathological Images.Scientific reports · 2016Article
- Evaluating stability of histomorphometric features across scanner and staining variations: prostate cancer diagnosis from whole slide images.Journal of medical imaging (Bellingham, Wash.) · 2016Article
- Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology.Annual review of biomedical engineering · 2016Review
- Detecting and segmenting cell nuclei in two-dimensional microscopy images.Journal of pathology informatics · 2016Article
- Robust automatic breast cancer staging using a combination of functional genomics and image-omics.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2015Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
Shape based active contours have emerged as a natural solution to overlap resolution. However, most of these shape-based methods are computationally expensive. There are instances in an image where no overlapping objects are present and applying these schemes results in significant computational overhead without any accompanying, additional benefit. In this paper we present a novel adaptive active contour scheme (AdACM) that combines boundary and region based energy terms with a shape prior in a multi level set formulation. To reduce the computational overhead, the shape prior term in the variational formulation is only invoked for those instances in the image where overlaps between objects are identified; these overlaps being identified via a contour concavity detection scheme. By not having to invoke all three terms (shape, boundary, region) for segmenting every object in the scene, the computational expense of the integrated active contour model is dramatically reduced, a particularly relevant consideration when multiple objects have to be segmented on very large histopathological images. The AdACM was employed for the task of segmenting nuclei on 80 prostate cancer tissue microarray images from 40 patient studies. Nuclear shape based, architectural and textural features extracted from these segmentations were extracted and found to able to discriminate different Gleason grade patterns with a classification accuracy of 86% via a quadratic discriminant analysis (QDA) classifier. On average the AdACM model provided 60% savings in computational times compared to a non-optimized hybrid active contour model involving a shape prior.
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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.