SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions--An EndoVis' 24 Challenge

Thu, 01 Jan 2026 00:00:00 +0000

Robust surgical tool segmentation with state-space vision models

Mon, 01 Jan 2024 00:00:00 +0000

This exploratory collaboration investigated state-space vision models for robust surgical tool segmentation under realistic visual corruptions. In collaboration with Kyung Hee University, we participated in the MICCAI EndoVis 2024 SegSTRONG-C challenge, benchmarking robustness to non-adversarial corruptions such as smoke, bleeding, and illumination changes. The proposed Vision Mamba–UNet approach achieved competitive performance across corruption types and ranked 7th overall among international submissions.

Perspective

The project supported early exploration of state-space models in medical image analysis and international doctoral mentorship. A jointly submitted DAAD exchange proposal, although not funded, laid the groundwork for continued collaboration and future joint research activities.

Samra Irshad |

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions--An EndoVis' 24 Challenge

Robust surgical tool segmentation with state-space vision models

Perspective