Physics for the IB Diploma: Biomedical physics

Deep Learning-based Automated Segmentation of Ultra-Widefield Retinal Vasculature for Cardiometabolic Disease Association Analysis.

Retinal vessel analysis in ultra-widefield (UWF) images provides a unique opportunity for large-scale assessment of systemic microvascular health. However, accurate segmentation in true-color UWF images remains challenging due to the large field of view, complex background, and reduced vessel contrast. To address these challenges, we develop ECS-Net, a dedicated deep learning (DL) framework for retinal vessel segmentation in true-color UWF images. Our ECS-Net adopts an enhanced encoder decoder architecture that integrates a dual-domain context enhancement module (DCEM), consisting of an anisotropic spatial focus unit (ASFU) and a feature correlation calibration unit (FCCU), together with atrous spatial pyramid pooling (ASPP) for multi-scale context modeling. The proposed ECS Net achieved a higher Dice coefficient of 0.8349 than other state-of-the-art algorithms (0.7001-0.8136), demonstrating strong generalization under real-world imaging conditions. Building upon accurate vessel extraction, region-specific vascular parameters, including vessel density (VD), fractal dimension (FD), tortuosity (TC), and mean curvature (MC), were quantified separately for central (45°), peripheral (45° 133°), and global (133°) zones. Multivariable logistic regression was used to evaluate associations with metabolic diseases in 4,618 participants. Hypertension showed significant inverse associations with VD and FD across all zones (all P < 0.001). Diabetes exhibited striking regional specificity, characterized by increased TC and MC and decreased FD were confined to the peripheral and global zones (all P< 0.01), with inverse associations for TC and MC detectable only in the global zone (P < 0.05). This study establishes the first DL framework specifically designed for retinal vessel segmentation in true color UWF images and reveals disease-specific regional vascular patterns that would be missed by conventional fundus photography, highlighting the value of UWF imaging for comprehensive systemic disease assessment and providing a biological prior for the development of interpretable and region-aware models. The code will be released on GitHub: https://github.com/yxyXinyue/UWF Retinal-Vasculature-Segmentation-Cardiometabolic.

Domain-Adaptive Retinal Vessel Segmentation for Unannotated Fundus Images

Accurate retinal vessel segmentation supports quantitative vascular analysis in assessing ocular and systemic diseases. Yet, its clinical scalability is constrained by limited pixel-level annotations and domain shift across heterogeneous fundus datasets. Thus, this study proposes a domain-adaptive retinal vessel segmentation model (DA-VesselNet), a weakly supervised approach that transfers vessel-segmentation knowledge from annotated source datasets to the unannotated Retinal Fundus Multi-Disease Image Dataset (RFMiD). The model was trained on DRIVE, CHASE_DB1, and FIVES, and adapted to RFMiD. The ResNet50 encoder with an attention-gated U-Net decoder, confidence-aware pseudo-label supervision, and perturbation consistency regularisation were used for the training process. Results on the held-out CHASE_DB1 indicated that DA-VesselNet achieved a Dice score of 0.5778, an Intersection over Union (IoU) of 0.4082, and an Area Under the Curve (AUC) of 0.9518. On 200 held-out FIVES test images with different pathological features, it achieved a Dice score of 0.7447 and an AUC of 0.9733, outperforming the source-only baseline model. To assess adaptation independently of source-adjacent data, the model was further tested on STARE and HRF, two domains excluded entirely from source training. Adaptation improved Dice by 0.0469 on STARE and 0.0056 on HRF with AUC gains of 0.0140 and 0.0121, respectively. Ablation analysis identified source-anchored supervision as the dominant contributor to performance. The adapted model was subsequently applied to generate vessel pseudo-labels for the RFMiD target domain, providing a structural resource for future vessel-informed analysis. These findings demonstrate that DA-VesselNet offers a scalable solution for creating clinically relevant pseudo-labelled vessels in fundus imaging with limited annotations.

Semantic Segmentation of Optical Coherence Tomography Images Based on TUnet+ Modeling

Accurate segmentation of Optical Coherence Tomography images is critical for assisting clinicians in identifying lesion regions and evaluating disease progression in retinal and cardiovascular disorders. As OCT is an optical electromagnetic-wave imaging modality, segmentation performance is closely related to the interpretation of wave scattering, tissue-layer boundaries, and propagation-induced image features. Existing OCT segmentation models are often limited by high computational complexity and insufficient accuracy, which restrict efficient and reliable clinical application. To address these challenges, this study proposes a lightweight TUnet+ network incorporating an innovatively designed PSE_C2fCIB module. Extensive experiments were conducted on the publicly available GOALS2022 dataset, including 200 deidentified OCT images augmented for training and validation. The results show that TUnet+ achieves state-of-the-art performance, with an accuracy of 99.3%, an F1-score of 95.65%, and a mean Intersection over Union of 91.87%. Through multi-scale feature fusion, channel attention, and efficient contextual modeling, the proposed framework improves the recognition of fine structural boundaries in OCT images. The method provides a robust technical foundation for automated diagnostic support and quantitative disease assessment, and it also demonstrates the value of electromagnetic-wave imaging analysis in biomedical engineering.

ADF-Net: Adaptive Directional Feature Fusion Network for OCTA Vessel Segmentation.

GA-Gabor guided swin-UNet for retinal vessel segmentation

Retinal Blood Vessel Segmentation Using Attention V-Net with Scale-Aware Evaluation

Retinal blood vessel segmentation remains a significant challenge, especially for small blood vessels with diameters less than 3 pixels in the DRIVE dataset and less than 4 pixels in the STARE dataset, owing to their low contrast and narrow structures. The aim of this study is to improve small retinal blood vessel segmentation performance through an Attention V-Net architecture that integrates attention-gating mechanisms into the skip connections of a V-Net backbone to strengthen the feature representation of thin vascular structures. The research method involves training and evaluating the proposed model on the DRIVE and STARE datasets using a scale-aware evaluation framework based on pixel-pitch calibration, classifying blood vessels into small and large categories, and measuring performance using accuracy, sensitivity, specificity, precision, Dice coefficient, and IoU. The results show that for small vessel segmentation, the method achieves sensitivities of 0.7033 and 0.6984, Dice scores of 0.4720 and 0.4699, and IoUs of 0.3096 and 0.3079 on the DRIVE and STARE datasets, respectively. For large vessels, sensitivities of 0.9219 and 0.8851, Dice scores of 0.8031 and 0.8179, and IoUs of 0.6719 and 0.6933 are obtained. Global evaluation yields accuracies of 0.9475 and 0.9602, sensitivities of 0.8727 and 0.8719, and Dice scores of 0.8080 and 0.8268. In conclusion, Attention V-Net demonstrates consistent segmentation performance across vessel scales, and the scale-aware evaluation framework effectively reveals the performance gap between small and large vessel segmentation, providing a more clinically relevant assessment than conventional global evaluation for early diagnosis of retinal diseases.