Jintao Duan¹, Miao Zhou², Yuxiang Wang¹, Fangfang Chen¹, Liangqing Lin³, Qinghua Wu³, Haipo Cui¹

¹School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. ²Jiangsu Cancer Hospital, Nanjing 213164, Jiangsu Province, China. ³The First Hospital of Putian, Putian 351100, Fujian Province, China.

Address correspondence to: Haipo Cui, School of Health Science and Engineering, University of Shanghai for Science and Technology, No. 516 Jungong Road, Yangpu District, Shanghai 200093, China. E-mail: h_b_cui@163.com.

Received September 27, 2024; Accepted January 22, 2025; Published April 1, 2025

DOI: https://doi.org/10.61189/242224ubhmhy

Highlights

● A systematic review of deep learning (DL) approaches for femoral cartilage segmentation in knee joint ultrasound images.

● Evaluation of popular DL models (U-Net, U-Net++, Siam U-Net, Mask R-CNNs) using various metrics.

● Discussion on dataset scarcity, data preprocessing methods, and future directions for DL-based ultrasound segmentation.

Research Article |Published on: 01 April 2025

[Medical Artificial Intelligence] 2025; 1(1): 1-13

Yuxiang Wang¹, Miao Zhou², Fangfang Chen¹, Jintao Duan¹, Liangqing Lin³, Qinghua Wu³, Wenhui Guo⁴, Haipo Cui¹

¹School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. ²Jiangsu Cancer Hospital, Nanjing 213164, China. ³Anesthesiology, The First Hospital of Putian, Putian 351100, China. ⁴School of Anesthesiology, Second Military Medical University/Naval Medical University, Shanghai 200433, China.

Address correspondence to: Haipo Cui, School of Health Science and Engineering, University of Shanghai for Science and Technology, No. 516 Jungong Road, Yangpu District, Shanghai 200093, China. E-mail: h_b_cui@163.com.

Received September 27, 2024; Accepted January 22, 2025; Published April 1, 2025

DOI: https://doi.org/10.61189/861515qdddmg

Highlights

● This review provides a comprehensive overview of various U-Net architectures and their variants, including the original U-Net, U-Net++, Attention U-Net, and ResU-Net, along with a discussion on potential improvements to the

U- Net architecture.

● The differences between these network architectures are analyzed in terms of training complexity and computa-   tional requirements.

● The review delves into the application of U-Net and its variants in ultrasound imaging, discussing both the advan-   tages and limitations of each model in various ultrasound contexts. Relevant literature on the application of each network architecture in ultrasound is also summarized.

Review Article |Published on: 01 April 2025

[Medical Artificial Intelligence] 2025; 1(1): 27-38