Drusen segmentation with sparse volumetric SD-OCT sampling

Drusen segmentation with sparse volumetric SD-OCT sampling
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使用稀疏体积 SD-OCT 采样进行玻璃疣分割

DOI:
10.1117/12.2581116
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发表时间:
2021
期刊:
SPIE Medical Imaging Symposium
影响因子:
--
通讯作者:
Raicu, Daniela
Raicu, Daniela
中科院分区:
--
文献类型:
--
作者:
Okemgbo, Abum F.;Rein, Irving D.;Wang, Yiyang;Fawzi, Amani;Furst, Jacob;Raicu, Daniela

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视网膜相关性黄斑变性(AMD)是一种常见的眼部疾病,其特征在于玻璃疣的积聚,即黄斑中细胞外物质的小沉积。早期发现玻璃疣是了解AMD进展的关键。因此,AMD进展期间玻璃疣的准确且稳健的分割对于自动化检测、分类、诊断和预后任务是重要的。光谱域光学相干断层扫描(SD-OCT)是一种流行的黄斑成像模式用于这些任务。然而,由于分辨率和速度之间的权衡,通常临床OCT扫描将包含比玻璃疣分割文献通常使用的100-200个图像少得多的每体积图像。为了解决这一差异,我们开发了一种新的玻璃疣分割算法的SD-OCT体积与低体积分辨率。我们实现了类似的工作,而平均使用16%的体积信息的结果。我们通过两个分级者对手动分割的图像评估了我们的分割方法,并分别实现了0.75和0.66的中值Dice系数得分,这接近于我们的中值读者间变异性得分0.75。
Age-Related Macular Degeneration (AMD) is a common eye disease characterized by the build-up of drusen, small deposits of extracellular materials in the macula. Early detection of drusen is key to understanding the progression of AMD. Therefore, accurate and robust segmentation of drusen during AMD progression is important for automated detection, classification, diagnosis, and prognosis tasks. Spectral-domain optical coherence tomography (SD-OCT) is a popular macular imaging modality used for these tasks. However, because of the trade-off between resolution and speed, often clinical OCT scans will contain far fewer images per volume than the 100-200 images the drusen segmentation literature generally utilizes. To address this disparity, we develop a novel drusen segmentation algorithm for SD-OCT volumes with low volumetric resolution. We achieve comparable results to similar work, while using on average 16% of the volumetric information. We evaluate our segmentation approach on manually segmented images by two graders, and achieve median Dice coefficient scores of 0.75 and 0.66, respectively, which are close to our median inter-reader variability score of 0.75.
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