Segmentation of Locally Varying Numbers of Outer Retinal Layers by a Model Selection Approach

A Hybrid Model Composed of Two Convolutional Neural Networks (CNNs) for Automatic Retinal Layer Segmentation of OCT Images in Retinitis Pigmentosa (RP)

Purpose

We propose and evaluate a hybrid model composed of two convolutional neural networks (CNNs) with different architectures for automatic segmentation of retina layers in spectral domain optical coherence tomography (SD-OCT) B-scans of retinitis pigmentosa (RP).

Methods

The hybrid model consisted of a U-Net for initial semantic segmentation and a sliding-window (SW) CNN for refinement by correcting the segmentation errors of U-Net. The U-Net construction followed Ronneberger et al. (2015) with an input image size of 256 × 32. The SW model was similar to our previously reported approach. Training image patches were generated from 480 horizontal midline B-scans obtained from 220 patients with RP and 20 normal participants. Testing images were 160 midline B-scans from a separate group of 80 patients with RP. The Spectralis segmentation of B-scans was manually corrected for the boundaries of the inner limiting membrane, inner nuclear layer, ellipsoid zone (EZ), retinal pigment epithelium, and Bruch's membrane by one grader for the training set and two for the testing set. The trained U-Net and SW, as well as the hybrid model, were used to classify all pixels in the testing B-scans. Bland–Altman and correlation analyses were conducted to compare layer boundary lines, EZ width, and photoreceptor outer segment (OS) length and area determined by the models to those by human graders.

Results

The mean times to classify a B-scan image were 0.3, 65.7, and 2.4 seconds for U-Net, SW, and the hybrid model, respectively. The mean ± SD accuracies to segment retinal layers were 90.8% ± 4.8% and 90.7% ± 4.0% for U-Net and SW, respectively. The hybrid model improved mean ± SD accuracy to 91.5% ± 4.8% (P < 0.039 vs. U-Net), resulting in an improvement in layer boundary segmentation as revealed by Bland–Altman analyses. EZ width, OS length, and OS area measured by the models were highly correlated with those measured by the human graders (r > 0.95 for EZ width; r > 0.83 for OS length; r > 0.97 for OS area; P < 0.05). The hybrid model further improved the performance of measuring retinal layer thickness by correcting misclassification of retinal layers from U-Net.

Conclusions

While the performances of U-Net and the SW model were comparable in delineating various retinal layers, U-Net was much faster than the SW model to segment B-scan images. The hybrid model that combines the two improves automatic retinal layer segmentation from OCT images in RP.

Translational Relevance

A hybrid deep machine learning model composed of CNNs with different architectures can be more effective than either model separately for automatic analysis of SD-OCT scan images, which is becoming increasingly necessary with current high-resolution, high-density volume scans.