Contour-Guided Query-Based Feature Fusion for Boundary-Aware and Generalizable Cardiac Ultrasound Segmentation

Zahid Ullah; Sieun Choi; Jihie Kim

cs.CVMar 30, 2026

Contour-Guided Query-Based Feature Fusion for Boundary-Aware and Generalizable Cardiac Ultrasound Segmentation

Authors: Zahid Ullah, Sieun Choi, Jihie Kim

Organizations: Department of Computer Science and Artificial Intelligence, Dongguk University, Seoul 04620, Republic of Korea

Abstract

Accurate cardiac ultrasound segmentation is essential for reliable assessment of ventricular function in intelligent healthcare systems. However, echocardiographic images are challenging due to low contrast, speckle noise, irregular boundaries, and domain shifts across devices and patient populations. Existing methods, largely based on appearance-driven learning, often fail to preserve boundary precision and structural consistency under these conditions. To address these issues, we propose a Contour-Guided Query Refinement Network (CGQR-Net) for boundary-aware cardiac ultrasound segmentation. The framework integrates multi-resolution feature representations with contour-derived structural priors. An HRNet backbone preserves high-resolution spatial details while capturing multi-scale context. A coarse segmentation is first generated, from which anatomical contours are extracted and encoded into learnable query embeddings. These contour-guided queries interact with fused feature maps via cross-attention, enabling structure-aware refinement that improves boundary delineation and reduces noise artifacts. A dual-head supervision strategy jointly optimizes segmentation and boundary prediction to enforce structural consistency. The proposed method is evaluated on the CAMUS dataset and further validated on the CardiacNet dataset to assess cross-dataset generalization. Experimental results demonstrate improved segmentation accuracy, enhanced boundary precision, and robust performance across varying imaging conditions. These results highlight the effectiveness of integrating contour-level structural information with feature-level representations for reliable cardiac ultrasound segmentation.

Figures & tables

Fig. 1: Sample echocardiographic images from the CAMUS (top row) and CardiacNet (bottom row) datasets, illustrating differences in image quality, noise levels, and anatomical variability. The CAMUS dataset contains relatively clean echocardiographic images with well-defined cardiac structures, while the CardiacNet dataset exhibits significantly higher variability, including severe speckle noise, low contrast, and irregular anatomical boundaries.

Fig. 2: Architecture of the proposed CGQR-Net. HRNet extracts multi-resolution features from the input echocardiography image, and a coarse segmentation head provides an initial structural prediction. Contours extracted from the coarse mask are converted into query embeddings and used to refine fused multi-scale features through cross-attention. The refined representation is then passed to segmentation and boundary heads to produce the final boundary-aware multi-class segmentation.

Paper	Methodology	Drawbacks
U-Net [ 11 ]	Encoder–decoder CNN with skip connections for pixel-wise segmentation.	Relies on local features and often produces smooth or inaccurate boundaries in low-contrast ultrasound images.
Attention U-Net [ 7 ]	Introduces attention gates to focus on relevant regions.	Improves region localization but still lacks explicit structural modeling; boundary precision remains limited.
UNet++ [ 8 ]	Dense skip connections for improved multi-scale feature fusion.	Increases architectural complexity while remaining primarily driven by pixel-wise learning without explicit contour priors.
nnU-Net [ 26 ]	Self-configuring segmentation framework with adaptive design.	Provides a strong baseline but remains appearance-driven and struggles with domain shift and boundary ambiguity.
CANet [ 27 ]	Context-aware network leveraging multi-scale contextual information.	Produces smoother predictions but lacks precise boundary delineation in challenging regions.
Extended nnU-Net [ 28 ]	Improved nnU-Net with enhanced configurations and training strategies.	Offers improved optimization but still shows limited structural awareness and sensitivity to anatomical variability.

TABLE I: Summary of existing cardiac segmentation methods, their methodologies, and limitations.

Fig. 3: Overall workflow of the proposed framework. CAMUS data, including two-chamber (2CH) and four-chamber (4CH) echocardiographic views, are used for training and internal validation, while CardiacNet is used for external validation. After preprocessing, images are fed into the boundary-aware segmentation model, which integrates HRNet, multi-scale feature fusion, and contour-guided query refinement. The model is evaluated using Dice similarity coefficients and qualitative analysis, producing final multi-class segmentation outputs.

Fig. 4: Overview of the proposed contour-guided query refinement framework. A two-dimensional echocardiography image is first processed by a HRNet backbone to extract multi-resolution features, and a coarse segmentation map is then generated. Contour points extracted from the coarse prediction are converted into query embeddings. These contour-derived queries interact with fused multi-scale features through cross-attention, where the queries provide structural guidance and the fused features provide spatial context. The refined representation is finally passed to dual prediction heads to produce the segmentation output and boundary map. Here, 2D denotes two-dimensional, Conv convolution, BN batch normalization, and ReLU rectified linear unit.

Parameter	Value
Framework	PyTorch
Optimizer	AdamW
Initial Learning Rate	$1\times 10^{-4}$
Learning Rate Scheduler	Cosine Annealing
Batch Size	4
Number of Epochs	100

TABLE II: Training hyperparameters used for model training.

\mathcal{L}=\mathcal{L}_{seg}+\lambda\mathcal{L}_{boundary}

Algorithm 1 Phase-Specific Training of CGQR-Net

Model	Endocardium	Epicardium	Left Atrium	Avg.
U-Net [ 11 ]	93.69	86.25	85.34	88.43
Attention U-Net [ 7 ]	93.72	86.54	86.02	88.76
UNet++ [ 8 ]	93.81	86.88	86.47	89.05
nnU-Net [ 26 ]	93.33	87.06	85.13	88.51
CANet [ 27 ]	93.91	87.52	87.01	89.48
Extended nnU-Net [ 28 ]	92.90	85.81	86.54	88.42

TABLE III: Comparison of cardiac structure segmentation performance on the CAMUS dataset in terms of DSC (%). The best results are shown in bold.

Model	Left Atrium	Right Atrium	Left Ventricle	Right Ventricle	Avg.
U-Net [ 11 ]	88.61	87.98	91.51	88.19	89.07
Attention U-Net [ 7 ]	88.94	88.31	91.63	88.54	89.36
UNet++ [ 8 ]	89.05	88.42	91.68	88.71	89.47
nnU-Net [ 26 ]	88.66	88.47	91.40	88.50	89.26
CANet [ 27 ]	88.95	88.91	91.70	89.26	89.71
Extended nnU-Net [ 28 ]	88.21	88.23	91.03	88.28	88.94

TABLE IV: Comparison of segmentation performance on the CardiacNet dataset in terms of DSC (%). The best results are shown in bold.

Fig. 5: Qualitative segmentation results on the CAMUS dataset. From left to right: original image, ground truth, coarse prediction, contour points, boundary map, final prediction, and overlay visualization. The proposed method produces more accurate and sharper boundaries compared to the coarse segmentation.

Fig. 6: Qualitative segmentation results on the CardiacNet dataset. From left to right: original image, ground truth, coarse prediction, contour points, boundary map, final prediction, and overlay visualization. The proposed method shows improved robustness under challenging conditions, producing more accurate and consistent segmentations with better boundary delineation.

Fig. 7: Visual comparison of segmentation results with existing methods on the CAMUS dataset. From left to right: original image and predictions from different methods, including the proposed CGQR-Net. The proposed method produces more accurate segmentation with improved boundary delineation and better shape consistency compared to previous approaches.

Method	LV Endo.	LV Epi.	LA
Full CGQR-Net	93.95	88.09	89.15
w/o Boundary Head	93.21	87.34	88.42
w/o Coarse Head	93.48	87.76	88.63
w/o Contour Queries	92.87	86.95	87.88
w/o Feature Pyramid Fusion	92.65	86.71	87.54
w/o Teacher Forcing	93.02	87.11	88.06

TABLE V: Class-wise ablation study of CGQR-Net on the CAMUS dataset (% DSC)

Ultrasound Image Segmentation

Ultrasound image segmentation delineates organs, lesions, and other structures in ultrasound scans for measurement, diagnosis, and image-guided care.

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CardioSAM: Topology-Aware Decoder Design for High-Precision Cardiac MRI Segmentation

Ujjwal Jain

ABV-IIITM Gwalior, India

Medical Image Segmentation Medical Image Analysis Boundary-Aware Segmentation Topology-Preserving Image Segmentation Segment Anything Models

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Accurate segmentation of cardiac structures in cardiovascular magnetic resonance (CMR) images is essential for reliable diagnosis and treatment of cardiovascular diseases. However, manual segmentation remains time-consuming and suffers from significant inter-observer variability. Recent advances in deep learning, particularly foundation models such as the Segment Anything Model (SAM), demonstrate strong generalization but often lack the boundary precision required for clinical applications. To address this limitation, we propose CardioSAM, a hybrid architecture that combines the generalized feature extraction capability of a frozen SAM encoder with a lightweight, trainable cardiac-specific decoder. The proposed decoder introduces two key innovations: a Cardiac-Specific Attention module that incorporates anatomical topological priors, and a Boundary Refinement Module designed to improve tissue interface delineation. Experimental evaluation on the ACDC benchmark demonstrates that CardioSAM achieves a Dice coefficient of 93.39%, IoU of 87.61%, pixel accuracy of 99.20%, and HD95 of 4.2 mm. The proposed method surpasses strong baselines such as nnU-Net by +3.89% Dice and exceeds reported inter-expert agreement levels (91.2%), indicating its potential for reliable and clinically applicable cardiac segmentation.

Abstract

Ultrasound Image Segmentation

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