ArticleProceedings. IEEE International Symposium on Biomedical Imaging2015
NUCLEI SEGMENTATION VIA SPARSITY CONSTRAINED CONVOLUTIONAL REGRESSION.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2015. 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.
- Enhanced Nuclei Segmentation and Classification via Category Descriptors in the SAM Model.Bioengineering (Basel, Switzerland) · 2024Article
- Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications.Cancers · 2022Review
- Generative Adversarial Networks in Digital Pathology and Histopathological Image Processing: A Review.Journal of pathology informatics · 2021Review
- Deep learning in digital pathology image analysis: a survey.Frontiers of medicine · 2020Review
- Learning Based Segmentation of CT Brain Images: Application to Postoperative Hydrocephalic Scans.IEEE transactions on bio-medical engineering · 2018Article
- Integrative Analysis of Cellular Morphometric Context Reveals Clinically Relevant Signatures in Lower Grade Glioma.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2016Article
- Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review.IEEE reviews in biomedical engineering · 2016Review
- Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases.Journal of pathology informatics · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Automated profiling of nuclear architecture, in histology sections, can potentially help predict the clinical outcomes. However, the task is challenging as a result of nuclear pleomorphism and cellular states (e.g., cell fate, cell cycle), which are compounded by the batch effect (e.g., variations in fixation and staining). Present methods, for nuclear segmentation, are based on human-designed features that may not effectively capture intrinsic nuclear architecture. In this paper, we propose a novel approach, called sparsity constrained convolutional regression (SCCR), for nuclei segmentation. Specifically, given raw image patches and the corresponding annotated binary masks, our algorithm jointly learns a bank of convolutional filters and a sparse linear regressor, where the former is used for feature extraction, and the latter aims to produce a likelihood for each pixel being nuclear region or background. During classification, the pixel label is simply determined by a thresholding operation applied on the likelihood map. The method has been evaluated using the benchmark dataset collected from The Cancer Genome Atlas (TCGA). Experimental results demonstrate that our method outperforms traditional nuclei segmentation algorithms and is able to achieve competitive performance compared to the state-of-the-art algorithm built upon human-designed features with biological prior knowledge.
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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.