Non-nuclei characterization in histopathological images: a processing step to improve nuclei segmentation methods

Christian Arias; Ricardo Moncayo; Eduardo Romero M.D.

doi:10.1117/12.2579613

3 November 2020 Non-nuclei characterization in histopathological images: a processing step to improve nuclei segmentation methods

Christian Arias, Ricardo Moncayo, Eduardo Romero M.D.

Author Affiliations +

Proceedings Volume 11583, 16th International Symposium on Medical Information Processing and Analysis; 115830K (2020) https://doi.org/10.1117/12.2579613
Event: The 16th International Symposium on Medical Information Processing and Analysis, 2020, Lima, Peru

Abstract

This study presents a novel strategy to characterize and remove non-nuclei signal (noise) in histopathological images stained with hematoxylin and eosin (H and E), a preprocessing step to improve traditional nuclei segmentation methods. Any non nuclei structure is mapped to a noiselet space at different resolution levels where a classic classifier is trained to recognize the noiselet coefficients of this projection. The proposed approach was evaluated with two multi-organ datasets manually annotated, comparing the nuclei segmentation obtained by a watershed algorithm plus the presented approach against the watershed method alone. An average Dice Score in these datasets (MICCAI Challenge and TCIA) of 70.2 and 59.6 was obtained by applying the herein introduced method, while the obtained Dice Score with only the watershed method was of 66.7 and 55.5.

Citation Download Citation

Christian Arias, Ricardo Moncayo, and Eduardo Romero M.D. "Non-nuclei characterization in histopathological images: a processing step to improve nuclei segmentation methods", Proc. SPIE 11583, 16th International Symposium on Medical Information Processing and Analysis, 115830K (3 November 2020); https://doi.org/10.1117/12.2579613

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