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

Research Article
Open access

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

Chuhan Liu 1* , Zicheng Lin 2 , Yang Zhao 3 , Jingkun Shi 4 , Ruoyi Li 5
  • 1 Beihang University    
  • 2 Beijing University of Posts and Telecommunications    
  • 3 Xidian University    
  • 4 Beijing Institute of Technology    
  • 5 Shanghai Jiao Tong University    
  • *corresponding author 21371266@buaa.edu.cn
Published on 21 February 2025 | https://doi.org/10.54254/2755-2721/2024.21123
ACE Vol.132
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-941-0
ISBN (Online): 978-1-83558-942-7

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.

Keywords:

Robustness, Medical Image Segmentation, nnU-Net, nnFormer

Liu,C.;Lin,Z.;Zhao,Y.;Shi,J.;Li,R. (2025). The Robustness Evaluation of Advanced Models in CT Image Segmentation: From Multi-organ to Multi-organ. Applied and Computational Engineering,132,306-313.
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References

[1]. Clarivate. Web of science platform. https://clarivate. com/products/scientific-and-academic-research/ research-discovery-and-workflow-solutions/webofscience-platform/ #resources. Accessed: 2024-08-08.

[2]. Synapse. Multi-atlas labeling beyond the cranial vault - workshop and challenge, 2015.

[3]. Jun Ma, Yao Zhang, Song Gu, Cheng Zhu, Cheng Ge, Yichi Zhang, Xingle An, Congcong Wang, Qiyuan Wang, Xin Liu, Shucheng Cao, Qi Zhang, Shangqing Liu, Yunpeng Wang, Yuhui Li, Jian He, and Xiaoping Yang. Abdomenct-1k: Is abdominal organ segmentation a solved problem? IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10):6695–6714, 2022.

[4]. Hong-Yu Zhou, Jiansen Guo, Yinghao Zhang, Xiaoguang Han, Lequan Yu, Liansheng Wang, and Yizhou Yu. nnformer: Volumetric medical image segmentation via a 3d transformer. IEEE Transactions on Image Processing, 32:4036–4045, 2023.

[5]. Fabian Isensee, Paul F Jaeger, Simon AA Kohl, Jens Petersen, and Klaus H Maier-Hein. nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2):203–211, 2021.

[6]. Niccolo` McConnell, Nchongmaje Ndipenoch, Yu Cao, Alina Miron, and Yongmin Li. Exploring advanced architectural variations of nnunet. Neurocomputing, 560:126837, 2023.

[7]. Jieneng Chen, Jieru Mei, Xianhang Li, Yongyi Lu, Qihang Yu, Qingyue Wei, Xiangde Luo, Yutong Xie, Ehsan Adeli, Yan Wang, et al. Transunet: Rethinking the u-net architecture design for medical image segmentation through the lens of transformers. Medical Image Analysis, page 103280, 2024.

[8]. Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang. Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Transactions on Medical Imaging, 2019.

[9]. Adam K. Wolf. 3d nii visualizer. https://github.com/adamkwolf/ 3d-nii-visualizer, 2024. Accessed: 2024-09-04.

[10]. Dominik Mu ̈ller, In ̃aki Soto-Rey, and Frank Kramer. Towards a guideline for evaluation metrics in medical image segmentation. BMC Research Notes, 15(1):210, 2022.

[11]. Abdel Aziz Taha and Allan Hanbury. Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool. BMC medical imaging, 15:1–28, 2015.

Cite this article

Liu,C.;Lin,Z.;Zhao,Y.;Shi,J.;Li,R. (2025). The Robustness Evaluation of Advanced Models in CT Image Segmentation: From Multi-organ to Multi-organ. Applied and Computational Engineering,132,306-313.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation

ISBN：978-1-83558-941-0(Print) / 978-1-83558-942-7(Online)
Editor：Mustafa ISTANBULLU
Conference website: https://2024.confmla.org/
Conference date: 21 November 2024
Series: Applied and Computational Engineering
Volume number: Vol.132
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Clarivate. Web of science platform. https://clarivate. com/products/scientific-and-academic-research/ research-discovery-and-workflow-solutions/webofscience-platform/ #resources. Accessed: 2024-08-08.

[2]. Synapse. Multi-atlas labeling beyond the cranial vault - workshop and challenge, 2015.

[3]. Jun Ma, Yao Zhang, Song Gu, Cheng Zhu, Cheng Ge, Yichi Zhang, Xingle An, Congcong Wang, Qiyuan Wang, Xin Liu, Shucheng Cao, Qi Zhang, Shangqing Liu, Yunpeng Wang, Yuhui Li, Jian He, and Xiaoping Yang. Abdomenct-1k: Is abdominal organ segmentation a solved problem? IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10):6695–6714, 2022.

[4]. Hong-Yu Zhou, Jiansen Guo, Yinghao Zhang, Xiaoguang Han, Lequan Yu, Liansheng Wang, and Yizhou Yu. nnformer: Volumetric medical image segmentation via a 3d transformer. IEEE Transactions on Image Processing, 32:4036–4045, 2023.

[5]. Fabian Isensee, Paul F Jaeger, Simon AA Kohl, Jens Petersen, and Klaus H Maier-Hein. nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2):203–211, 2021.

[6]. Niccolo` McConnell, Nchongmaje Ndipenoch, Yu Cao, Alina Miron, and Yongmin Li. Exploring advanced architectural variations of nnunet. Neurocomputing, 560:126837, 2023.

[7]. Jieneng Chen, Jieru Mei, Xianhang Li, Yongyi Lu, Qihang Yu, Qingyue Wei, Xiangde Luo, Yutong Xie, Ehsan Adeli, Yan Wang, et al. Transunet: Rethinking the u-net architecture design for medical image segmentation through the lens of transformers. Medical Image Analysis, page 103280, 2024.

[8]. Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang. Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Transactions on Medical Imaging, 2019.

[9]. Adam K. Wolf. 3d nii visualizer. https://github.com/adamkwolf/ 3d-nii-visualizer, 2024. Accessed: 2024-09-04.

[10]. Dominik Mu ̈ller, In ̃aki Soto-Rey, and Frank Kramer. Towards a guideline for evaluation metrics in medical image segmentation. BMC Research Notes, 15(1):210, 2022.

[11]. Abdel Aziz Taha and Allan Hanbury. Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool. BMC medical imaging, 15:1–28, 2015.

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