A sparse representation of the pathologist's interaction with whole slide images to improve the assigned relevance of regions of interest

Daniel Santiago; Germán Corredor; Eduardo Romero

doi:10.1117/12.2285906

17 November 2017 A sparse representation of the pathologist's interaction with whole slide images to improve the assigned relevance of regions of interest

Daniel Santiago, Germán Corredor, Eduardo Romero

Author Affiliations +

Proceedings Volume 10572, 13th International Conference on Medical Information Processing and Analysis; 1057204 (2017) https://doi.org/10.1117/12.2285906
Event: 13th International Symposium on Medical Information Processing and Analysis, 2017, San Andres Island, Colombia

Abstract

During a diagnosis task, a Pathologist looks over a Whole Slide Image (WSI), aiming to find out relevant pathological patterns. Nonetheless, a virtual microscope captures these structures, but also other cellular patterns with different or none diagnostic meaning. Annotation of these images depends on manual delineation, which in practice becomes a hard task. This article contributes a new method for detecting relevant regions in WSI using the routine navigations in a virtual microscope. This method constructs a sparse representation or dictionary of each navigation path and determines the hidden relevance by maximizing the incoherence between several paths. The resulting dictionaries are then projected onto each other and relevant information is set to the dictionary atoms whose similarity is higher than a custom threshold. Evaluation was performed with 6 pathological images segmented from a skin biopsy already diagnosed with basal cell carcinoma (BCC). Results show that our proposal outperforms the baseline by more than 20%.

Citation Download Citation

Daniel Santiago, Germán Corredor, and Eduardo Romero "A sparse representation of the pathologist's interaction with whole slide images to improve the assigned relevance of regions of interest", Proc. SPIE 10572, 13th International Conference on Medical Information Processing and Analysis, 1057204 (17 November 2017); https://doi.org/10.1117/12.2285906

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