用隐式神经表示建模干性老年黄斑变性进展，实现个体化预测。
Modelling Geographic Atrophy Progression using Implicit Neural Representations
- 基于隐式神经表示，从少量图像推断病变演变路径。
- 在不同场景下，病灶面积误差最低，分割重合度最高。
- 适合缺乏大量纵向数据的个性化医疗研究者使用。
年龄相关性黄斑变性（AMD）是西方世界失明的主要原因。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。目前，纵向荧光素眼底成像（FAF）是评估病变随时间增长的主要工具。然而，由于个体差异显著，晚期AMD的进展机制仍不明确。本文提出利用隐式神经表示（INRs）在低数据条件下建模个体层面的GA进展。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，本方法在多种场景下均达到竞争性分割质量，其GA病灶面积的平均绝对误差（MAE）最低，且DICE分数最高，同时保持了良好的FAF图像质量。
原文摘要 · Abstract (English)
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.
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