Abstract
Medical image segmentation often requires maintaining anatomical topology and structural consistency, which can be challenging when dealing with complex or noisy data. Traditional segmentation methods may produce topologically incorrect results that violate anatomical constraints. In this work, we propose a novel approach for robust segmentation that combines topology violation detection with feature synthesis.
Our method introduces a topology-aware framework that detects and corrects topological violations during segmentation. The framework consists of three key components: (1) a topology violation detection module that identifies structural inconsistencies, (2) a feature synthesis module that generates corrective features, and (3) a robust segmentation network that maintains anatomical topology.
We evaluate our approach on multiple medical imaging datasets, including brain MRI and fetal ultrasound. Experimental results demonstrate that our topology-aware approach significantly improves segmentation robustness compared to traditional methods. The method shows excellent performance in maintaining anatomical consistency and reducing topological errors.
The proposed framework represents a significant advancement in medical image segmentation, providing more anatomically accurate results that could improve clinical applications and research studies.
BibTeX
@inproceedings{li2023ro,
title={Robust Segmentation via Topology Violation Detection and Feature Synthesis},
author={Li, Liu and Ma, Qiang and Ouyang, Cheng and Li, Zeju and Meng, Qingjie and Zhang, Weitong and Qiao, Mengyun and Kyriakopoulou, Vanessa and Hajnal, Joseph and Rueckert, Daniel and Kainz, Bernhard},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2023)},
year={2023},
doi={10.1007/978-3-031-43901-8_7}
}