Ramezanzadeh Esmat, Shoeibi Naser, Rabbani Hossein, Hosseini Seyedeh Maryam, Astaneh Mohammadreza Ansari, Tavakoli Meysam, Tabesh Hamed, Zare Hoda, Bahreyni-Toosi Mohammad Hossein
Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Social Determinants of Health Research Center, Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
Biomed Phys Eng Express. 2025 Aug 18;11(5). doi: 10.1088/2057-1976/addfdc.
. Fundus fluorescein angiography (FFA) remains the gold standard for retinal vascular imaging, especially for detecting leakage, neovascularization, and ischemia, despite advancements in non-invasive techniques like optical coherence tomography angiography (OCT-A) and color fundus photography (CFP). FFA's unique role, particularly in late-phase imaging, is crucial for diagnosing and managing diabetic retinopathy (DR). This study introduces a novel dual-phase segmentation framework for FFA, enhancing the analysis of early and late-phase images. This study aims to overcome challenges such as noise and blurring in late-phase FFA by developing a method that enhances vascular map detection and monitors changes across two phases, enabling precise identification of lesions responsible for leakage. Validated through expert evaluations and quantitative metrics, this model enhances diagnostic accuracy for diabetic retinopathy and complements existing imaging technologies.. A prospective randomized study of 280 images of 87 DR patients at various stages was included in this study. Our approach involved using four different image enhancement techniques including (1) histogram equalization (HE), (2) contrast limited adaptive HE (CLAHE), (3) recursive mean-square HE (RMSHE), and (4) the proposed method in this paper (CLAHE combined RMSHE). In addition, for robust noise reduction and edge sharpening in each enhancement method, combined median, match, and Hessian filters were used. Finally, four different thresholding methods, including, (i) C-means fuzzy thresholding, (ii) IsoData thresholding, (iii) modified active contour (MAC)+Otsu thresholding, and (iv) the proposed method in this paper (MAC+ IsoData) were used for vessel segmentation in FFA across different datasets.. The most effective segmentation method, MAC+IsoData, was assessed using three metrics (DSC (early: 0.84 ± 0.05, late: 0.84 ± 0.03), Jaccard index (early:0.73 ± 0.06, late:0.74 ± 0.05), and Boundary F1 score (early: 0.98 ± 0.02, late: 0.97 ± 0.02)). The results were validated by three expert ophthalmologists.. This work demonstrates that MAC+Otsu, following CLAHE enhancement, effectively delineates details necessary for the precise identification of lesions responsible for leakage across two different phases. Additionally, MAC+IsoData, with the proposed method enhancement for early phases and RMSHE enhancement for late phases, successfully reveals the vascular map.
荧光素眼底血管造影(FFA)仍然是视网膜血管成像的金标准,特别是在检测渗漏、新生血管形成和缺血方面,尽管光学相干断层扫描血管造影(OCT-A)和彩色眼底摄影(CFP)等非侵入性技术取得了进展。FFA的独特作用,特别是在晚期成像中,对于糖尿病性视网膜病变(DR)的诊断和管理至关重要。本研究介绍了一种用于FFA的新型双相分割框架,增强了对早期和晚期图像的分析。本研究旨在通过开发一种增强血管图检测并监测两个阶段变化的方法来克服晚期FFA中的噪声和模糊等挑战,从而能够精确识别导致渗漏的病变。通过专家评估和定量指标验证,该模型提高了糖尿病性视网膜病变的诊断准确性,并补充了现有的成像技术。本研究纳入了一项对87例不同阶段DR患者的280张图像的前瞻性随机研究。我们的方法涉及使用四种不同的图像增强技术,包括(1)直方图均衡化(HE)、(2)对比度受限自适应HE(CLAHE)、(3)递归均方HE(RMSHE)以及(4)本文提出的方法(CLAHE与RMSHE相结合)。此外,在每种增强方法中,为了实现强大的降噪和边缘锐化,使用了组合中值、匹配和黑塞滤波器。最后,四种不同的阈值化方法,包括(i)C均值模糊阈值化、(ii)IsoData阈值化、(iii)改进的活动轮廓(MAC)+大津阈值化以及(iv)本文提出的方法(MAC+IsoData),用于不同数据集中FFA的血管分割。使用三个指标(DSC(早期:0.84±0.05,晚期:0.84±0.03)、杰卡德指数(早期:0.73±0.06,晚期:0.74±0.05)和边界F1分数(早期:0.98±0.02,晚期:0.97±0.02))评估最有效的分割方法MAC+IsoData。结果由三位眼科专家进行了验证。这项工作表明,在CLAHE增强后,MAC+大津法有效地勾勒出了精确识别两个不同阶段导致渗漏的病变所需的细节。此外,MAC+IsoData,在早期采用本文提出的方法增强,在晚期采用RMSHE增强,成功地揭示了血管图。