Purpose: Disease progression of retinal atrophy associated with AMD requires the accurate quantification of the retinal atrophy changes on longitudinal OCT studies. It is based on finding, comparing, and delineating subtle atrophy changes on consecutive pairs (prior and current) of unregistered OCT scans. Methods: We present a fully automatic end-to-end pipeline for the simultaneous detection and quantification of time-related atrophy changes associated with dry AMD in pairs of OCT scans of a patient. It uses a novel simultaneous multi-channel column-based deep learning model trained on registered pairs of OCT scans that concurrently detects and segments retinal atrophy segments in consecutive OCT scans by classifying light scattering patterns in matched pairs of vertical pixel-wide columns (A-scans) in registered prior and current OCT slices (B-scans). Results: Experimental results on 4,040 OCT slices with 5.2M columns from 40 scans pairs of 18 patients (66% training/validation, 33% testing) with 24.13+-14.0 months apart in which Complete RPE and Outer Retinal Atrophy (cRORA) was identified in 1,998 OCT slices (735 atrophy lesions from 3,732 segments, 0.45M columns) yield a mean atrophy segments detection precision, recall of 0.90+-0.09, 0.95+-0.06 and 0.74+-0.18, 0.94+-0.12 for atrophy lesions with AUC=0.897, all above observer variability. Simultaneous classification outperforms standalone classification precision and recall by 30+-62% and 27+-0% for atrophy segments and lesions. Conclusions: simultaneous column-based detection and quantification of retinal atrophy changes associated with AMD is accurate and outperforms standalone classification methods. Translational relevance: an automatic and efficient way to detect and quantify retinal atrophy changes associated with AMD.

翻译：目的：与AMD相关的视网膜萎缩疾病进展需要精确量化纵向OCT研究中的视网膜萎缩变化。这基于对连续两对（既往和当前）未配准OCT扫描中细微萎缩变化的发现、比较和描绘。方法：我们提出一种全自动端到端流水线，用于同时检测和量化患者OCT扫描对中与干性AMD相关的时间性萎缩变化。该流水线采用新型同步多通道基于列的深度学习模型，该模型在配准的OCT扫描对上训练，通过分类配准的既往和当前OCT切片（B扫描）中匹配的垂直像素宽度列（A扫描）对中的光散射模式，同时检测和分割连续OCT扫描中的视网膜萎缩区域。结果：对来自18名患者40对扫描（66%训练/验证，33%测试）的4,040张OCT切片（含520万列）进行实验，扫描间隔为24.13±14.0个月，其中在1,998张OCT切片（735个萎缩病灶，源自3,732个分割片段，45万列）中识别出完全RPE和视网膜外层萎缩。结果得到：萎缩片段检测的平均精确率和召回率分别为0.90±0.09、0.95±0.06，萎缩病灶为0.74±0.18、0.94±0.12，AUC=0.897，均高于观察者变异度。同时分类在萎缩片段和病灶上的分类精确率和召回率分别比单独分类方法提高30±62%和27±0%。结论：基于列的同时检测和量化AMD相关视网膜萎缩变化具有高准确性，且优于单独分类方法。转化意义：提供一种自动高效的方式检测和量化AMD相关视网膜萎缩变化。