ArticleJournal of pathology informatics2023
Removing non-nuclei information from histopathological images: A preprocessing step towards improving nuclei segmentation methods.
Article in Journal of pathology informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Smart neural network and cognitive computing process for multi task nuclei detection segmentation and classification in breast cancer histopathology images.Scientific reports · 2025Article
- Lightweight Evolving U-Net for Next-Generation Biomedical Imaging.Diagnostics (Basel, Switzerland) · 2025Article
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3 authors.
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Abstract
Disease interpretation by computer-aided diagnosis systems in digital pathology depends on reliable detection and segmentation of nuclei in hematoxylin and eosin (HE) images. These 2 tasks are challenging since appearance of both cell nuclei and background structures are very variable. This paper presents a method to improve nuclei detection and segmentation in HE images by removing tiles that only contain background information. The method divides each image into smaller patches and uses their projection to the noiselet space to capture different spatial features from non-nuclei background and nuclei structures. The noiselet features are clustered by a
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Registered trials
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