利用隐式神经表示（INRs）模拟地理萎缩（GA）的进展
Modelling Geographic Atrophy Progression using Implicit Neural Representations
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中文总结 AI 辅助
针对晚期AMD中GA进展的个体化建模难题，提出用INRs在低数据场景下建模GA进展，生成多时间点FAF图像与GA分割，在不损失FAF质量时实现GA分割的最优MAE与DICE。
中文摘要 AI 辅助
年龄相关性黄斑变性（AMD）是西方世界致盲的主要原因，其晚期干性阶段以不可逆的萎缩区域即地理萎缩（GA）为特征。纵向眼底自发荧光（FAF）图像采集目前是在图像层面评估病变随时间生长的主要工具，但由于其进展高度个体化，晚期AMD的演化仍知之甚少。本研究提出在低数据场景下利用隐式神经表示（INRs）对个体水平的GA进展进行建模，该方法可生成过去和未来时间点的FAF图像及GA分割结果。在对比模型中，本方法在不同场景下取得了有竞争力的分割质量，在不牺牲FAF图像质量的前提下，获得了GA病变面积的最低平均绝对误差（MAE）和最高戴斯系数（DICE），代码可在指定网址获取。
英文摘要
Age-related Macular Degeneration (AMD) is the major cause of blindness in the Western world. Its late dry phase is characterised by irreversible atrophic areas, namely Geographic Atrophy (GA). Longitudinal Fundus Autofluorescence (FAF) image acquisitions are currently the main tool for assessing lesion growth over time at the image level. However, due to its highly individualised progression, the evolution of late AMD remains poorly understood. In this work, we propose using Implicit Neural Representations (INRs) to model GA progression at the individual level in a low-data setting. Our approach generates both FAF and GA segmentation at both past and future time points. Among the comparison models, our method achieves competitive segmentation quality across different scenarios, yielding the lowest Mean Absolute Error (MAE) for the GA lesion area and the highest DICE score, without sacrificing FAF image quality. The code is available at https://github.com/SimoneSarrocco/ga-progression-with-inrs.
发表机构
- University of Basel（巴塞尔大学）
- University Hospital Basel（巴塞尔大学医院）
- Moorfields Eye Hospital NHS Foundation Trust（摩尔菲尔兹眼科医院NHS基金会信托）
- Vienna University of Natural Resources and Life Sciences（维也纳自然资源与生命科学大学）
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