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The Robustness Evaluation of Advanced Models in CT Image Segmentation: From Multi-organ to Multi-organ

Year: 2025

Type: article

Abstract: Medical image segmentation models are often tested on the same dataset used for training, which limits real-world applicability. This paper evaluates the robustness of several 3D and 2D models by comparing their performance on BTCV and AbdomenCT-1K datasets. The study explores the effects of model architecture, dimensionality, organ characteristics, and dataset differences on robustness through visualizations and various metrics, providing insights and recommendations for improving robustness and generalization.

Source: Applied and Computational Engineering

DOI: 10.54254/2755-2721/2024.21123

Authors: Chuhan Liu, Zicheng Lin, Yang Zhao, Jingkun Shi, Ruoyi Li

Institutions: Beihang University, Beijing University of Posts and Telecommunications, Xidian University, Beijing Institute of Technology, Shanghai Jiao Tong University

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W4407828167

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