Cardiac MRI segmentation using block-partitioned transformer with global-local information integration

Elham Abouei; Shaoyan Pan; Mingzhe Hu; Aparna H. Kesarwala; Jun Zhou; Justin Roper; Tian Liu; Xiaofeng Yang

doi:10.1117/12.3006929

2 April 2024 Cardiac MRI segmentation using block-partitioned transformer with global-local information integration

Elham Abouei, Shaoyan Pan, Mingzhe Hu, Aparna H. Kesarwala, Jun Zhou, Justin Roper, Tian Liu, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 12930, Medical Imaging 2024: Clinical and Biomedical Imaging; 1293021 (2024) https://doi.org/10.1117/12.3006929
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

In this work, we aimed to develop a deep-learning algorithm for segmentation of cardiac Magnetic Resonance Image (MRI) to facilitate contouring of Left Ventricle (LV), Right Ventricle (RV), and Myocardium (Myo). We proposed a Shifting Block Partition Multilayer Perceptron (SBP-MLP) network built upon a symmetric U-shaped encoder-decoder network. We evaluated this proposed network on a public cardiac MRI dataset, ACDC training dataset. The network performance was quantitatively evaluated using Hausdorff Distance (HD), Mean Surface Distance (MSD) and Residual Mean Square distance (RMS) as well as Dice score coefficient, sensitivity, and precision. The performance of the proposed network was compared with two other state-of-the-art networks known as dynamic UNet and Swin-UNetr. Our proposed network achieved the following quantitative metrics as HD = 1.521±0.090 mm, MSD = 0.287±0.080 mm, RMSD = 0.738±0.315 mm. as well as Dice = 0.948±0.020, precision = 0.946±0.017, sensitivity = 0.951±0.027. The proposed network showed statistically significant improvement compared to the Swin-UNetr and dynamic UNet algorithms across most metrics for the three segments. The SBP-MLP showed superior segmentation performance, as evidenced by higher Dice score and lower HD relative to competing methods. Overall, the proposed SBP-MLP demonstrates comparable or superior performance to competing methods. This robust method has the potential for implementation in clinical workflows for cardiac segmentation and analysis.

Citation Download Citation

Elham Abouei, Shaoyan Pan, Mingzhe Hu, Aparna H. Kesarwala, Jun Zhou, Justin Roper, Tian Liu, and Xiaofeng Yang "Cardiac MRI segmentation using block-partitioned transformer with global-local information integration", Proc. SPIE 12930, Medical Imaging 2024: Clinical and Biomedical Imaging, 1293021 (2 April 2024); https://doi.org/10.1117/12.3006929

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