A hierarchical deep learning approach for segmentation of glioblastoma tumor niches on digital histopathology

Alvaro Sandino Garzon; Ruchika Verma; Yijang Chen; David Becerra Tovar; Eduardo Romero Castro; Pallavi Tiwari

doi:10.1117/12.2611855

4 April 2022 A hierarchical deep learning approach for segmentation of glioblastoma tumor niches on digital histopathology

Alvaro Sandino Garzon, Ruchika Verma, Yijang Chen, David Becerra Tovar, Eduardo Romero Castro, Pallavi Tiwari

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Proceedings Volume 12039, Medical Imaging 2022: Digital and Computational Pathology; 1203911 (2022) https://doi.org/10.1117/12.2611855
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Glioblastoma (GBM) is a highly aggressive brain tumor and is notoriously known for its intra-tumoral heterogeneity. Diagnosis of GBM is based on histopathology confirmation via tissue samples obtained from intra-cranial biopsies. After surgical intervention, histopathology tissue slides are visually analyzed by neuro-pathologists to identify distinct GBM histological hallmarks. GBMs may be histologically undergraded based on the amount of viable tissue due to sampling errors associated with small tissue samples obtained. Consequently, there is a need for automatic identification of histopathological GBM hallmarks. In this work, we present a hierarchical deep learning strategy to automatically segment distinct GBM niches including necrosis, cellular tumor, and hyperplastic blood-vessels, on H&E digitized histopathology slides. Our approach includes first segmenting necrosis and cellular tumor regions, then identifying hyperplastic blood-vessels within cellular tumor regions.

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Alvaro Sandino Garzon, Ruchika Verma, Yijang Chen, David Becerra Tovar, Eduardo Romero Castro, and Pallavi Tiwari "A hierarchical deep learning approach for segmentation of glioblastoma tumor niches on digital histopathology", Proc. SPIE 12039, Medical Imaging 2022: Digital and Computational Pathology, 1203911 (4 April 2022); https://doi.org/10.1117/12.2611855

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KEYWORDS

Tumors

Tissues

Image segmentation

Biopsy

Blood vessels

Brain

Error analysis

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