Accurate segmentation of the fetal head (FH) and pubic symphysis (PS) in intrapartum ultrasound (IU) images is a crucial step for automatic angle of progression (AoP) measurement, which plays an essential role in predicting delivery outcomes and reducing maternal and fetal complications. Existing CNN–Transformer hybrid models often suffer from attention collapse due to limited medical data and tend to overlook boundary details that are critical yet highly degraded by artifacts and noise in IU images. To address these issues, we propose a CNN-stylized dual-path CNN–Transformer hybrid network tailored for IU segmentation. The encoder integrates a parallel CNN branch and a CNN-stylized Transformer branch to balance local feature extraction and global dependency modeling while mitigating attention collapse. A Transformer-to-CNN fusion module further enhances cross-branch information interaction. In the decoder, a Boundary Attention Residual Module captures subtle foreground-background transitions and progressively refines boundary features. In addition, an Adaptive Boundary Enhancement strategy is designed to emphasize challenging boundary regions during CutMix augmentation. Experiments on three datasets demonstrate that our method outperforms state-of-the-art approaches in both overall accuracy and boundary precision. The automatic AoP measurement analysis further validates its potential for clinical translation. Code is available at https://github.com/SakuraKong/Dual-Path-CNN-Stylized-Hybrid-Network.

Accurate computer-aided interpretation of fetal heart rate (FHR) recordings depends on detecting the baseline and transient accelerations (Acc) and decelerations (Dec) that deviate from it. Most deep learning models treat FHR as a simple 1-D time sequence, overlooking the spectral separation between the low-frequency baseline and high-frequency Acc/Dec patterns. Neglecting this clinically important time-frequency structure can result in missed detections of Acc and Dec events and increased susceptibility to noise. To overcome these limitations, we present WaveFHR-VNet-a U-Net-style, multi-scale, multi-basis wavelet-voting network that analyzes FHR signals in the joint time-frequency domain. WaveFHR-VNet embeds a discrete wavelet transform (DWT) in every encoder block. Each DWT splits the features into approximation (low-pass) coefficients, which preserve the low-frequency baseline trends, and detail (high-pass) coefficients, which preserve the high-frequency Acc/Dec edges. Cascading these decompositions through successive layers yields a hierarchical, multi-scale representation. The decoder uses inverse DWT for full-resolution reconstruction. Skip connections are equipped with an Interactive Coefficient Selection (ICS) module that learns attention masks to suppress Doppler noise and motion artefacts in the detail stream while amplifying diagnostically salient transients. To enhance spectral diversity, five complementary wavelet bases (db4, db6, sym4, sym5, bior3.5) operate in parallel; a simple voting layer fuses their outputs, eliminating manual basis tuning. Evaluated on four FHR datasets, WaveFHR-VNet achieved state-of-the-art performance, with improvements of up to 5.3995% Dice, 5.4758% IoU, and 4.6263% accuracy over the best baselines on LCU-DB, the most widely used public benchmark. It also demonstrates strong cross-dataset generalization, consistently outperforming all comparison models. These results suggest that WaveFHR-VNet can serve as a reliable tool for intrapartum monitoring.

The right atrium (RA) is critical for cardiac hemodynamics but is often overlooked in clinical diagnostics. This study presents a benchmark framework for RA cavity segmentation from late gadolinium-enhanced magnetic resonance imaging (LGE-MRIs), leveraging a two-stage strategy and a novel 3D deep learning network, RASnet. The architecture addresses challenges in class imbalance and anatomical variability by incorporating multi-path input, multi-scale feature fusion modules, Vision Transformers, context interaction mechanisms, and deep supervision. Evaluated on datasets comprising 354 LGE-MRIs, RASnet achieves SOTA performance with a Dice score of 92.19% on a primary dataset and demonstrates robust generalizability on an independent dataset. The proposed framework establishes a benchmark for RA cavity segmentation, enabling accurate and efficient analysis for cardiac imaging applications. Open-source code (https://github.com/zjinw/RAS) and data (https://zenodo.org/records/15524472) are provided to facilitate further research and clinical adoption.

Accurate segmentation of the fetal head and pubic symphysis in intrapartum ultrasound images and measurement of fetal angle of progression (AoP) are critical to both outcome prediction and complication prevention in delivery. However, due to poor quality of perinatal ultrasound imaging with blurred target boundaries and the relatively small target of the public symphysis, fully automated and accurate segmentation remains challenging. In this paper, we propse a dual-path boundary-guided residual network (DBRN), which is a novel approach to tackle these challenges. The model contains a multi-scale weighted module (MWM) to gather global context information, and enhance the feature response within the target region by weighting the feature map. The model also incorporates an enhanced boundary module (EBM) to obtain more precise boundary information. Furthermore, the model introduces a boundary-guided dual-attention residual module (BDRM) for residual learning. BDRM leverages boundary information as prior knowledge and employs spatial attention to simultaneously focus on background and foreground information, in order to capture concealed details and improve segmentation accuracy. Extensive comparative experiments have been conducted on three datasets. The proposed method achieves average Dice score of 0.908$\pm$0.05 and average Hausdorff distance of 3.396$\pm$0.66 mm. Compared with state-of-the-art competitors, the proposed DBRN achieves better results. In addition, the average difference between the automatic measurement of AoPs based on this model and the manual measurement results is 6.157$^{\circ }$, which has good consistency and has broad application prospects in clinical practice.