ArticleJournal of medical imaging (Bellingham, Wash.)2017
Automatic extraction of cell nuclei from H&E-stained histopathological images.
Article in Journal of medical imaging (Bellingham, Wash.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 41 citations in OpenAlex.
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- Nuclei instance segmentation from histopathology images using Bayesian dropout based deep learning.BMC medical imaging · 2023Article
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- Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning.NPJ precision oncology · 2020Article
- ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network.EBioMedicine · 2019Article
- Automated Computational Detection, Quantitation, and Mapping of Mitosis in Whole-Slide Images for Clinically Actionable Surgical Pathology Decision Support.Journal of pathology informatics · 2019Article
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
Extraction of cell nuclei from hematoxylin and eosin (H&E)-stained histopathological images is an essential preprocessing step in computerized image analysis for disease detection, diagnosis, and prognosis. We present an automated cell nuclei segmentation approach that works with H&E-stained images. A color deconvolution algorithm was first applied to the image to get the hematoxylin channel. Using a morphological operation and thresholding technique on the hematoxylin channel image, candidate target nuclei and background regions were detected, which were then used as markers for a marker-controlled watershed transform segmentation algorithm. Moreover, postprocessing was conducted to split the touching nuclei. For each segmented region from the previous steps, the regional maximum value positions were identified as potential nuclei centers. These maximum values were further grouped into [Formula: see text]-clusters, and the locations within each cluster were connected with the minimum spanning tree technique. Then, these connected positions were utilized as new markers for a watershed segmentation approach. The final number of nuclei at each region was determined by minimizing an objective function that iterated all of the possible [Formula: see text]-values. The proposed method was applied to the pathological images of the tumor tissues from The Cancer Genome Atlas study. Experimental results show that the proposed method can lead to promising results in terms of segmentation accuracy and separation of touching nuclei.
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