Medical physics · 2021
Choroidal neovascularization fusion visualization for spectral-domain optical coherence tomography and optical coherence tomography angiography images.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Huang Chen, Xie Keren, Zhang Yuhan, Li Mingchao, Yuan Songtao, Chen Qiang
Аннотация
PURPOSE: The spectral-domain optical coherence tomography (SD-OCT) images and OCT angiography (OCTA) images can provide complementary information for choroidal neovascularization (CNV) visualization. We expected to simultaneously display multifaceted characteristics of CNV in a single projection image.
METHODS: We proposed a fusion method for CNV visualization by combining structural and angiographic images, which mainly involves four steps: (a) Generate SD-OCT and OCTA projection images from original volumetric data with retinal layer restriction. (b) For SD-OCT projection images, enhance retinal vessels and CNV. (c) For OCTA images, detect CNV region based on multimodal data and display the neovascularization in false color. (d) A maximum fusion strategy was adopted to generate the fused images.
RESULTS: Experimental results with 30 cases from 30 patients demonstrate that the fused images are more effective in displaying CNV than single-modality projection images. The average information entropy and the mean gradient in the CNV regions for SD-OCT projection images, OCTA images, and the fusion images are 4.66/0.21, 5.45/0.45, and 6.8/0.58, respectively.
CONCLUSIONS: The proposed method is more effective for CNV visualization than the conventional single-modality image-based method. The proposed method can combine complementary information from multimodal images and provide a satisfying visual effect.
METHODS: We proposed a fusion method for CNV visualization by combining structural and angiographic images, which mainly involves four steps: (a) Generate SD-OCT and OCTA projection images from original volumetric data with retinal layer restriction. (b) For SD-OCT projection images, enhance retinal vessels and CNV. (c) For OCTA images, detect CNV region based on multimodal data and display the neovascularization in false color. (d) A maximum fusion strategy was adopted to generate the fused images.
RESULTS: Experimental results with 30 cases from 30 patients demonstrate that the fused images are more effective in displaying CNV than single-modality projection images. The average information entropy and the mean gradient in the CNV regions for SD-OCT projection images, OCTA images, and the fusion images are 4.66/0.21, 5.45/0.45, and 6.8/0.58, respectively.
CONCLUSIONS: The proposed method is more effective for CNV visualization than the conventional single-modality image-based method. The proposed method can combine complementary information from multimodal images and provide a satisfying visual effect.