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

A non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, comprising:

• forming an imaging data set comprising a digitized biopsy image of a patient having a glioma;
• segmenting the digitized biopsy image using a first deep learning algorithm to identify one or more necrotic regions and one or more non-necrotic regions within the digitized biopsy image;
• and
• segmenting the non-necrotic regions using a second deep learning algorithm to identify a first glioblastoma multiforme (GBM) histopathological indicator.

A prognostic apparatus, comprising:

• a memory configured to store an imaging data set comprising a digitized whole slide image (WSI) of a patient having a glioma;
• one or more deep learning classifier circuits configured to:
• segment the digitized WSI using a first deep learning algorithm to identify one or more necrotic regions and one or more non-necrotic regions;
• and
• segment the one or more non-necrotic regions using a second deep learning algorithm to identify glioblastoma multiforme (GBM) histopathological indicators.