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Unifying Local and Global Shape Descriptors to Grade Soft-Tissue Sarcomas Using Graph Convolutional Networks

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Julia Schnabel

Prof. Dr.

Principal Investigator

Computational Imaging and AI in Medicine

Abstract

The tumor grading of patients suffering from soft-tissue sarcomas is a critical task, as an accurate classification of this high-mortality cancer entity constitutes a decisive factor in devising optimal treatment strategies. In this work, we focus on distinguishing soft-tissue sarcoma subtypes solely based on their 3D morphological characteristics, derived from tumor segmentation masks. Notably, we direct attention to overcoming the limitations of texture-based methodologies, which often fall short of providing adequate shape delineation. To this end, we propose a novel yet elegant modular geometric deep learning framework coined Global Local Graph Convolutional Network (GloLo-GCN) that integrates local and global shape characteristics into a meaningful unified shape descriptor. Evaluated on a multi-center dataset, our proposed model performs better in soft-tissue sarcoma grading than GCNs based on state-of-the-art graph convolutions and a volumetric 3D convolutional neural network, also evaluated on binary segmentation masks exclusively.

inproceedings

ISBI 2024

IEEE 21st International Symposium on Biomedical Imaging. Athens, Greece, May 27-30, 2024.

Authors

J. Kiechle • S. M. Fischer • D. M. Lang • M. Folco • S. C. Foreman • V. K. N. Rösner • A.-K. Lohse • C. Mogler • C. Knebel • M. R. Makowski • K. Woertler • S. E. Combs • A. S. Gersing • J. C. Peeken • J. A. Schnabel

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DOI

Research Area

 C1 | Medicine

BibTeXKey: KFL+24

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