# US-ATHC: Unsupervised Multi-Class Glioma Segmentation via Adaptive Thresholding and Clustering

## TL;DR

This paper introduces US-ATHC, an unsupervised method for segmenting gliomas in MRI scans without needing expert annotations.

## Contribution

The novel contribution is a fully unsupervised two-step pipeline for accurate multi-class glioma segmentation.

## Key findings

- US-ATHC outperformed classical clustering and deep learning models on the BraTS 2021 dataset.
- The method showed robustness and practical applicability in real-world clinical settings using the Gliobiopsy dataset.

## Abstract

Background/Objectives: Accurate segmentation of gliomas in 3D volumetric MRI is critical for diagnosis, treatment planning, and surgical navigation. However, the scarcity of expert annotations limits the applicability of supervised learning approaches, motivating the development of unsupervised methods. This study presents US-ATHC (Unsupervised Segmentation using Adaptive Thresholding and Hierarchical Clustering), a fully unsupervised two-step pipeline for both global tumor detection and multi-class subregion segmentation. Methods: In the first step, a global tumor mask is extracted by combining adaptive thresholding (Sauvola) with morphological processing on individual MRI slices. The resulting candidates are fused across axial, coronal, and sagittal views using a strict 3D consistency criterion. In the second step, the global mask is refined into a three-class segmentation (active tumor, edema, and necrosis) using optimized affinity propagation clustering. Results: The method was evaluated on the BraTS 2021 dataset, demonstrating accurate tumor and subregion segmentation that outperformed both classical clustering techniques and state-of-the-art deep learning models. External validation on the Gliobiopsy dataset from the University Hospital of Poitiers confirmed robustness and practical applicability in real-world clinical settings. Conclusions: US-ATHC establishes an unsupervised paradigm for glioma segmentation that balances accuracy with computational efficiency. Its annotation-independent nature makes it suitable for scenarios with scarce labeled data, supporting integration into clinical workflows and large-scale neuroimaging studies.