ArticleProceedings of SPIE--the International Society for Optical Engineering2017
Evaluation of nucleus segmentation in digital pathology images through large scale image synthesis.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Image-based analysis of lymphoma cell heterogeneity: Comparative study of CRYO and FFPE techniques.Journal of pathology informatics · 2026Article
- ENHANCED SHARP-GAN FOR HISTOPATHOLOGY IMAGE SYNTHESIS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- SHARP-GAN: SHARPNESS LOSS REGULARIZED GAN FOR HISTOPATHOLOGY IMAGE SYNTHESIS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2022Article
- Robust Histopathology Image Analysis: to Label or to Synthesize?Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2019Article
- Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images.Pattern recognition · 2019Article
- The bumps under the hippocampus.Human brain mapping · 2018Article
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10 authors.
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Abstract
Digital histopathology images with more than 1 Gigapixel are drawing more and more attention in clinical, biomedical research, and computer vision fields. Among the multiple observable features spanning multiple scales in the pathology images, the nuclear morphology is one of the central criteria for diagnosis and grading. As a result it is also the mostly studied target in image computing. Large amount of research papers have devoted to the problem of extracting nuclei from digital pathology images, which is the foundation of any further correlation study. However, the validation and evaluation of nucleus extraction have yet been formulated rigorously and systematically. Some researches report a human verified segmentation with thousands of nuclei, whereas a single whole slide image may contain up to million. The main obstacle lies in the difficulty of obtaining such a large number of validated nuclei, which is essentially an impossible task for pathologist. We propose a systematic validation and evaluation approach based on large scale image synthesis. This could facilitate a more quantitatively validated study for current and future histopathology image analysis field.
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