Abdominal hemorrhage is a critical medical condition characterized by a dangerous clinical course，making rapid and accurate diagnosis imperative. This study addresses the challenges in ultrasound image segmentation for abdominal hemorrhage，including low contrast，high noise，and blurred boundaries，which often hinder the precise delineation of bleeding regions. To overcome these limitations，we propose a novel dual-domain and multiscale attention U-Net （DMA-UNet） that integrates spatial-and frequency-domain features for enhanced segmentation of abdominal hemorrhage in ultrasound images. This life-threatening condition typically results from trauma，ruptured aneurysms，or postoperative complications. Its abrupt onset and rapid progression can swiftly lead to hemorrhagic shock and multiple organ failure，rendering time a decisive factor in determining patient outcomes. In such critical scenarios，ultrasonography has become an indispensable first-line diagnostic tool because of its portability，absence of radiation exposure，and capability for rapid bedside assessment. Accurately identifying and quantifying the extent of hemorrhage on ultrasound scans directly influence clinical decision-making，such as choosing between conservative management or urgent surgical intervention，and provides crucial prognostic information. However，achieving precise automated segmentation of hemorrhage regions presents multiple inherent challenges. Ultrasound images are inherently characterized by low signal-to-noise ratios，pervasive speckle noise，poor tissue contrast，and various artifact interferences. The heterogeneity of pathological manifestations further compounds this difficulty： hemorrhage areas may present as anechoic，hyperechoic，or mixed echogenicity patterns，often with irregular and poorly defined boundaries that blend with surrounding parenchymal or adipose tissues. This combination of technical limitations and pathological variability makes traditional manual segmentation methods time consuming and operator dependent and subject to substantial interobserver variability. Moreover，complex factors in the clinical examination environment pose additional challenges. Ultrasound examinations are frequently conducted under suboptimal conditions，including patient movement，limited acoustic windows，uneven probe pressure，and intestinal gas interference，all of which considerably degrade image quality and consistency. In emergency and intraoperative settings，the inherent tension between diagnostic speed requirements and accuracy demands creates substantial pressure on medical personnel. Although deep learning techniques，particularly convolutional neural network architectures represented by U-Net and its variants，have achieved remarkable progress in medical image analysis for modalities，such as CT and MRI，their direct application to abdominal ultrasound image segmentation continues to face fundamental challenges. These models are typically designed for images with high contrast and standardized spatial characteristics. When processing ultrasound data，they often exhibit limited receptive fields，insufficient global contextual relationship modeling，and inefficient multiscale feature fusion. This phenomenon results in suboptimal performance when segmenting diffuse，low-contrast hemorrhage regions and frequently leads to under-segmentation of scattered bleeding areas or over-segmentation of anatomically similar soft tissues.

To bridge this technological gap，we propose a sophisticated deep learning architecture called dual-domain multiscale attention network （DMA-Net）. This framework is meticulously designed around three synergistic and innovative core modules，each specifically optimized to address the distinct limitations of conventional segmentation models when processing complex ultrasound data. The core architecture incorporates the following key innovative modules. 1） Multiscale atrous attention module in the encoder： This module employs a parallel multibranch architecture that systematically expands the receptive field through atrous convolutional layers with varying dilation rates while adaptively calibrating feature weights via dual channel-spatial attention mechanisms. Each branch integrates contextual information at different scales，thereby enhancing the sensitivity to subtle hemorrhage regions while preserving feature map resolution. A residual attention pathway is specifically designed to retain original texture details. The module dynamically suppresses speckle noise interference through learnable spatial masks，and its hierarchical dilation design captures local vascular structures and global bleeding distribution patterns. This multiscale fusion strategy substantially improves the detection accuracy of early-stage and scattered hemorrhage foci under challenging acoustic conditions. 2） Frequency-domain global modeling module in the bottleneck layer： This module implements a frequency-spatial dual-stream architecture that transforms feature maps into the spectral domain via fast Fourier transform，enabling holistic analysis of hemorrhage distribution patterns. In the frequency pathway，learnable spectral filters suppress ultrasound-specific artifacts while amplifying hemorrhage-related frequency components. The spatial pathway maintains convolutional operations to preserve anatomical details. Through residual connections and frequency-domain attention gates，the module selectively enhances cross-region dependencies that are critical for identifying discontinuous bleeding patterns. This approach effectively bridges the gap between localized feature extraction and global contextual understanding and is particularly valuable for segmenting diffuse hemorrhage regions with irregular boundaries. The integrated design achieves computational efficiency while remarkably improving the model’s ability to interpret complex acoustic signatures in abdominal ultrasound imaging. 3） Cross-level joint attention fusion module： a dynamic feature selection mechanism is introduced in the encoder-decoder skip connections. Through multiscale feature cross-computation，it generates adaptive attention weights that intelligently filter semantic features across different levels，suppress noise transmission，and promote the optimized fusion of deep semantic information with shallow spatial details for accurate boundary reconstruction.

The experimental framework was established using a clinically heterogeneous dataset comprising 363 intra-abdominal hemorrhage ultrasound images sourced from two independent medical centers to ensure anatomical and acquisition variability. A comprehensive data augmentation pipeline was implemented to enhance model robustness and generalizability； this pipeline incorporates spatial transformations （rotation，scaling，and elastic deformation），intensity variations，and speckle noise simulation. Quantitative evaluation on hold-out test sets from both institutions demonstrated the model’s strong segmentation performance and cross-center adaptability. For Center A，the model achieved a Dice similarity coefficient of 0.879 7 and an intersection over onion （IoU） of 0.796 1. For Center B，the Dice similarity coefficient and IoU reached 0.933 9 and 0.876 2，respectively，reflecting consistent reliability across different imaging protocols and patient populations.

DMA-UNet introduces an ultrasound-adapted segmentation framework that effectively integrates spatial- and frequency-domain representations through coordinated attention mechanisms. By jointly enhancing global context modeling，multiscale feature fusion，and imaging artifact resilience，the network offers a clinically viable solution for rapid and accurate hemorrhage assessment in critical care. Its strong performance across independent datasets underscores its translational potential for use in bedside decision support systems.