Coronary vessel segmentation in XCA is challenging due to poor image quality, including low contrast, noise, and artifacts, as well as complex visualization of vessel structures with curves and bifurc

The gap

Consensus across the literature

Supporting evidence — 3 representative gaps

• Angio-fusion net: dual-stream enhanced VGG16 attention U-Net for vessel morphology preservation in XCA segmentation (2026) · doi

  Coronary vessel segmentation in XCA is challenging due to poor image quality, including low contrast, noise, and artifacts, as well as complex visualization of vessel structures with curves and bifurcations. Traditional preprocessing methods, such as G-channel thresholding, have feature significant masking. Background interference from pulmonary tissues, bones, catheters, and cardiac motion artifacts further complicates accurate vessel delineation, particularly for low- (4–7). The contrast vessels with unclear boundaries advancement of deep learning has significantly enhanced medical vessel segmentation. Modern Convolutional Neural Network (CNN) architectures, such as U-Net (8) and DeepLabV3+ (9), have advanced feature information. Feature Pyramid Networks (FPNs) (10) have improved detail lesion retention, particularly segmentation. Hybrid models, such as SegFormer and Swin- U-Net, have feature representation and contextual understanding (11). Attention mechanisms have also contributed by optimizing feature selection and reducing background noise. However, U-Net in handling complex and multi-class segmentation, emphasizing the necessity for continued research in this domain. segmentation by using multi-scale faces difficulties further refined particularly coronary imaging, skin in in Several models have addressed challenges in coronary vessel segmentation. Notably, Fuzzy Attention (FA)-SegNet (12), TABLE 1 Comparison of segmentation methods and constraints for XCA images. (13), improve and Vascular Specific

  Keywords: segmentation vessel feature coronary particularly contrast noise artifacts complex signi background further models attention multi
• GDT-SwinKid: A hybrid model for precise renal lesion analysis (2026) · doi

  GDT-SwinKid is the latest advancement in the development of automated analytical capabilities for the automated analysis of kidney disease using deep learning. This is achieved by merging GDT, an extraction system based on Swin Transfor- mations (and the extraction of the primary and secondary structures) into a single high-quality backbone (the U-Net) to create a single effective model that not only achieves very high accuracy for segmentation (Dice: 0.95) and classification (AUC = 0.991), but also reduces parameter count and computational requirements compared to most current methods. When compared with other existing architectures, the current work consistently produced superior performance relative to the other transformer-based approaches or the current efficient CNN designs specifically, it provided enhanced accuracy in localizing lesions and showed greater consistency across a wide range of classes or categories of clinical diseases (cyst, tumor, stone) PLOS One | https://doi.org/10.1371/journal.pone.0349285 May 20, 2026 27 / 30 when performed across these multiple classes or categories. Furthermore, the results from our ablation studies showed that every one of the innovative design features contributes significantly to the overall success of the model. The visual evidence generated through the model’s interpretability (attention and Grad-CAM) supports the accuracy of predictions, leading to a high degree of confidence in the use of deep learning methods. Collectively, this paper provides a ground-breaking new high-performance model that surpasses all previous results in segmentation or classification of renal disease. 9.1. Future work will focus on Here are future works directions, presented in clear point wise, 1. Can expand the GDT-SwinKid framework to include multi-modal and multi-view imaging data, such as MRI and ultra- sound, aiming to support more comprehensive and realistic clinical scenarios. 2. Can integrate clinical metadata (e.g., patient demographics and lab results) alongside imaging to enhance diagnostic precision and boost clinical relevance. 3. Focus on rare lesion types and borderline cases by developing advanced augmentation methods or adopting few-shot and transfer learning strategies.

  Keywords: high model clinical learning accuracy current swinkid automated disease deep extraction based single segmentation classification
• Ultra-widefield optical coherence tomography angiography in diabetic retinopathy: from retinal lesions to choroidal metrics (2026) · doi

  Several future research areas for UWF-OCTA in DR require atten- tion. Although UWF-OCTA has shown certain application value in DR assessment, current research and clinical practice still have numer- ous unresolved limitations, such as non-unified imaging specifica- tions, insufficient automatic lesion recognition accuracy, and unclear guidance value of quantitative indicators. In view of these practical bottlenecks, subsequent research of UWF-OCTA in DR can be pro- moted step by step in combination with short-term, medium-term and long-term layouts, so as to gradually improve its clinical applica- tion system. In the short term, deep learning can be prioritized to optimize the automatic lesion detection capability of UWF-OCTA. Flat NV is often difficult to identify accurately due to ILM segmentation limita- tions, peripheral scan images have relatively low signal-to-noise ratio, and severe DME will also disturb the integrity of retinal structure to a certain extent. Constructing deep learning models with diverse training data covering the above complex scenarios and conducting rigorous cross-device and cross-center verification will help to steadily improve the accuracy of automatic identification of key lesions. In the medium term, large-scale longitudinal and horizontal comparative studies should be carried out simultaneously to clar- ify the clinical value of UWF-OCTA-related indicators. Long-term follow-up of patients during the transition from NPDR to PDR and treatment can help explore the temporal correlation between choroidal and retinal vascular changes, and determine the predic- tive effect of relevant indicators on clinical outcomes. Meanwhile, prospective comparative studies between UWF-OCTA-based evaluation algorithms and FA or clinical examination are needed to confirm whether quantitative indicators such as NV area and vessel density can guide the formulation of clinical strategies including anti-VEGF treatment timing and panretinal photoco- agulation (PRP). In addition, further exploration of choroidal metrics is also an important part of medium-term research, including clarifying the sequence of choroidal thinning and reti- nal damage, verifying the response of choroidal parameters to anti-VEGF therapy, and standardizing the quantification method of choroidal flow voids, so as to establish choroidal metrics as effective clinical biomarkers. In the long run, establishing multi-center consensus and a standardized UWF-OCTA imaging system is the core task. Current inconsistencies in devices, scan protocols, segmentation algorithms and analysis methods limit the generalizability of research results. Developing unified standards for imaging acqui- sition, reporting and quality control through multi-center coop- eration will lay the foundation for cross-study data integration and promote the standardized application of UWF-OCTA in DR clinical management.

  Keywords: octa clinical term choroidal indicators value imaging automatic medium long cross center tion certain application