MonoUNet: A Robust Tiny Neural Network for Automated Knee Cartilage Segmentation on Point-of-Care Ultrasound Devices

• URL: http://arxiv.org/abs/2604.07780v1
• Date: Thu, 09 Apr 2026 04:14:16 GMT
• Title: MonoUNet: A Robust Tiny Neural Network for Automated Knee Cartilage Segmentation on Point-of-Care Ultrasound Devices
• Abstract summary: We develop an ultra-compact deep learning model for knee cartilage segmentation on point-of-care ultrasound (POCUS) devices.<n>MonoUNet consists of (i) an aggressively reduced backbone with an asymmetric decoder, (ii) a trainable monogenic block that extracts multi-scale local phase features, and (iii) a gated feature injection mechanism.<n>MonoUNet was evaluated on a multi-site, multi-device knee cartilage ultrasound dataset.
• Score: 3.64325868505768
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Objective: To develop a robust and compact deep learning model for automated knee cartilage segmentation on point-of-care ultrasound (POCUS) devices. Methods: We propose MonoUNet, an ultra-compact U-Net consisting of (i) an aggressively reduced backbone with an asymmetric decoder, (ii) a trainable monogenic block that extracts multi-scale local phase features, and (iii) a gated feature injection mechanism that integrates these features into the encoder stages to reduce sensitivity to variations in ultrasound image appearance and improve robustness across devices. MonoUNet was evaluated on a multi-site, multi-device knee cartilage ultrasound dataset acquired using cart-based, portable, and handheld POCUS devices. Results: Overall, MonoUNet outperformed existing lightweight segmentation models, with average Dice scores ranging from 92.62% to 94.82% and mean average surface distance (MASD) values between 0.133 mm and 0.254 mm. MonoUNet reduces the number of parameters by 10x--700x and computational cost by 14x--2000x relative to existing lightweight models. MonoUNet cartilage outcomes showed excellent reliability and agreement with the manual outcomes: intraclass correlation coefficients (ICC$_{2,k})$=0.96 and bias=2.00% (0.047 mm) for average thickness, and ICC$_{2,k}$=0.99 and bias=0.80% (0.328 a.u.) for echo intensity. Conclusion: Incorporating trainable local phase features improves the robustness of highly compact neural networks for knee cartilage segmentation across varying acquisition settings and could support scalable ultrasound-based assessment and monitoring of knee osteoarthritis using POCUS devices. The code is publicly available at https://github.com/alvinkimbowa/monounet.

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