Simultaneous column-based deep learning progression analysis of atrophy
  associated with AMD in longitudinal OCT studies
Adi Szeskin Roei Yehuda Or Shmueli Jaime Levy Leo Joskowicz
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.