MonoUNet: A Robust Tiny Neural Network for Automated Knee Cartilage Segmentation on Point-of-Care Ultrasound Devices
Authors: Alvin Kimbowa, Arjun Parmar, Ibrahim Mujtaba, Will Wei, Maziar Badii, Matthew Harkey, David Liu, Ilker Hacihaliloglu
Organizations: School of Biomedical Engineering, The University of British Columbia, Canada · Department of Kinesiology, Michigan State University, USA · Department of Rheumatology, The University of British Columbia, Canada · Department of Radiology, The University of British Columbia, Canada · Department of Medicine, The University of British Columbia, Canada
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, a novel, highly compact segmentation model consisting of (i) an aggressively reduced U-Net backbone, (ii) a trainable monogenic block that extracts multi-scale local phase features from the input, and (iii) a gating mechanism that injects these features into the encoder stages to reduce sensitivity to variations in ultrasound image appearance. MonoUNet segmentation performance was evaluated on a multi-site, multi-device knee cartilage ultrasound dataset using Dice score and mean average surface distance (MASD). Agreement between MonoUNet and manual cartilage outcomes (thickness and echo intensity) was assessed using Bland-Altman analysis with 95% limits of agreement, and reliability was assessed using intraclass correlation coefficient (ICC2,k). Results: Overall, MonoUNet outperformed existing lightweight segmentation models, with average Dice scores ranging from 92.62% to 94.82% and 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 (ICC2,k)=0.96 and bias=2.00% (0.047 mm) for average thickness, and ICC2,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.
Figures & tables
Figure 1: Overview of MonoUNet architecture : Trainable multi-scale local phase features are extracted from the input image using the Mono block and injected into the high-resolution encoder stages via Mono gates, where they are fused with the encoder features using learnable channel-wise weights. The decoder is half the size of the encoder.
Figure 2: Cross-dataset Dice score distributions for the three training scenarios. Top row shows models trained on D0, middle row shows models trained on D1, and bottom row shows models trained on D2 and tested on D0, D1, and D3.
Figure 3: Bland-Altman plots showing the agreement between cartilage thickness and intensity measured from manual and automated segmentations.
Figure 4: Representative qualitative results on unseen test images. Manual annotations are shown in green and model predictions in red. Rows 1–2 show models trained on D0 and tested on D2 and D3; rows 3–4 show models trained on D1 and tested on D2 and D3; rows 5–6 show models trained on D2 and tested on D1 and D3.
Medical image segmentation delineates organs, lesions, and other structures in scans to support measurement, diagnosis, treatment planning, and research.
Ultrasound imaging is the most widely adopted medical modality globally due to its low cost and portability, yet artificial intelligence (AI) deployment remains constrained by reliance on GPU-accelerated models, creating a structural paradox where the cost of "intelligence" exceeds that of the imaging device itself. Here, we present the systematic adaptation and extensive evaluation of UltraSeg, an ultra-lightweight architecture originally developed for colonoscopic polyp segmentation, now engineered for point-of-care ultrasound (POCUS) across ten public datasets spanning six anatomical sites (breast, thyroid, kidney, carotid, fetal, and small-animal tumor). We systematically validate both variants in ultrasound domains: UltraSeg-130K (0.13M parameters) achieves 89.7 FPS on single-core CPUs and 34.8 FPS on a refurbished mobile device, while UltraSeg-500K (0.5M parameters) delivers 44.6 FPS on CPU and 16.1 FPS on mobile device. UltraSeg-500K matches or exceeds the Dice performance of the 31M-parameter UNet and approaches 105M-parameter TransUNet in average performance, with superior zero-shot cross-dataset generalization on external validation sets (UDIAT, DDTI). By enabling clinical-grade segmentation without GPU dependency, this work brings AI costs in line with ultrasound accessibility, making advanced diagnostics available in resource-limited settings.
Weihao Gao
School of Computer Science and Artificial Intelligence, Guangdong University of Education
Purpose: To develop and evaluate cross-sequence transfer learning for automatic femoral cartilage segmentation, testing bidirectional transfer between dual-echo steady-state (DESS) and sagittal proton density-weighted 3D fast spin-echo (Cube) sequences. Materials and Methods: We optimized a modified 2D U-Net on 507 DESS images from the Osteoarthritis Initiative (OAI). We then established same-sequence baselines using subject-level cross-validation on a subset of 44 OAI DESS images and 44 Cube images acquired at the Istituto Ortopedico Rizzoli, Bologna, Italy. Each subset included 22 non-lesioned and 22 lesioned subjects. Finally, we performed transfer learning across sequences by fine-tuning the pretrained models on the target sequence with increasing training set sizes to study convergence, while keeping validation and test sets fixed. Segmentations were evaluated using Dice similarity coefficient (DSC) and average surface distance (ASD). Lesion effects were assessed with two-sided Mann-Whitney U tests with Bonferroni correction. Results: Same-sequence training yielded higher accuracy on DESS than Cube (DSC, 0.900 vs 0.830; P<.001). Cube-to-DESS transfer matched DESS performance (DSC, 0.903±0.032 vs 0.900±0.027), reaching a performance plateau at 9 training subjects. DESS-to-Cube yielded a lower combined DSC (0.802±0.049 vs 0.830±0.042), reaching a plateau at 24 training subjects. Lesions did not affect DESS (P≥.39) but reduced Cube accuracy (DSC, 0.805 vs 0.856; P<.001). Conclusion: Transfer learning across sequences can substantially reduce target-sequence annotation requirements for femoral cartilage segmentation, but performance is direction- and sequence-dependent, and the effects of lesions on segmentation may vary across MRI sequences.
Francesco Chiumento, Gianluigi Crimi, Elisa Moretta +7
School of Electronic Engineering, Dublin City University, Dublin, Ireland · Bioengineering and Computing Laboratory, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy · Dipartimento di Scienze Mediche e Chirurgiche (DIMEC), Alma Mater Studiorum – Università di Bologna, Bologna, Italy +4
Accurate lesion segmentation in ultrasound images is essential for preventive screening and clinical diagnosis, yet remains challenging due to low contrast, blurry boundaries, and significant scale variations. Although existing deep learning-based methods have achieved remarkable performance, these methods still struggle with scale variations and indistinct tumor boundaries. To address these challenges, we propose a progressive boundary enhanced U-Net (PBE-UNet). Specially, we first introduce a scale-aware aggregation module (SAAM) that dynamically adjusts its receptive field to capture robust multi-scale contextual information. Then, we propose a boundary-guided feature enhancement (BGFE) module to enhance the feature representations. We find that there are large gaps between the narrow boundary and the wide segmentation error areas. Unlike existing methods that treat boundaries as static masks, the BGFE module progressively expands the narrow boundary prediction into broader spatial attention maps. Thus, broader spatial attention maps could effectively cover the wider segmentation error regions and enhance the model's focus on these challenging areas. We conduct expensive experiments on four benchmark ultrasound datasets, BUSI, Dataset B, TN3K, and BP. The experimental results how that our proposed PBE-UNet outperforms state-of-the-art ultrasound image segmentation methods. The code is at https://github.com/cruelMouth/PBE-UNet.
Chen Wang, Yixin Zhu, Yongbin Zhu +5
Department of Computer Science, Shaoxing University, Shaoxing, China · National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University, China · College of Computer Science, Chongqing University, China +3