Comprehensive Pathological Image Segmentation via Teacher Aggregation for Tumor Microenvironment Analysis
Journal:
arXiv
Published Date:
Jan 6, 2025
Abstract
The tumor microenvironment (TME) plays a crucial role in cancer progression
and treatment response, yet current methods for its comprehensive analysis in
H&E-stained tissue slides face significant limitations in the diversity of
tissue cell types and accuracy. Here, we present PAGET (Pathological image
segmentation via AGgrEgated Teachers), a new knowledge distillation approach
that integrates multiple segmentation models while considering the hierarchical
nature of cell types in the TME. By leveraging a unique dataset created through
immunohistochemical restaining techniques and existing segmentation models,
PAGET enables simultaneous identification and classification of 14 key TME
components. We demonstrate PAGET's ability to perform rapid, comprehensive TME
segmentation across various tissue types and medical institutions, advancing
the quantitative analysis of tumor microenvironments. This method represents a
significant step forward in enhancing our understanding of cancer biology and
supporting precise clinical decision-making from large-scale histopathology
images.
