Automated identification of cone photoreceptors in adaptive optics optical coherence tomography images using transfer learning

Abstract

Automated measurements of the human cone mosaic requires the identification of individual cone photoreceptors. The current gold standard, manual labeling, is a tedious process and can not be done in a clinically useful timeframe. As such, we present an automated algorithm for identifying cone photoreceptors in adaptive optics optical coherence tomography (AO-OCT) images. Our approach fine-tunes a pre-trained convolutional neural network originally trained on AO scanning laser ophthalmoscope (AO-SLO) images, to work on previously unseen data from a different imaging modality. On average, the automated method correctly identified 94% of manually labeled cones when compared to manual raters, from twenty different AO-OCT images acquired from five normal subjects. Voronoi analysis confirmed the general hexagonal-packing structure of the cone mosaic as well as the general cone density variability across portions of the retina. The consistency of our measurements demonstrates the high reliability and practical utility of having an automated solution to this problem.

Detecting abnormal fundus images by employing deep transfer learning

“…Compared with this study, they all deployed a small dataset, but the performance of the Inception-ResNet-v2 architecture was significantly better than that of the VGG16. Similarly, Heisler M, et al [18] demonstrated three different transfer learning methods to identify the cones in a small set of AO-OCT images using a base network trained on AO-SLO images, which all obtained results similar to that of a manual rater. Using the results from the fine-tuning (Layer 5) method, they calculated four different cone mosaic parameters that were similar to the results found in AO-SLO images, showing the utility of their method.…”

Detecting abnormal fundus images by employing deep transfer learning

“…In an independent sample of 189 new thyroid images resulted in an AUC of 0.70. Similarly, Heisler M, et al [18]demonstrated three different transfer learning methods to identify the cones in a small set of AO-OCT images using a base network trained on AO-SLO images which all obtained results similar to that of a manual rater. Using the results from the Fine-Tuning (Layer 5) method, they calculated four 9 different cone mosaic parameters which were similar to the results found in AO-SLO images showing the utility of their method.…”

RAC-CNN: multimodal deep learning based automatic detection and classification of rod and cone photoreceptors in adaptive optics scanning light ophthalmoscope images

“…The current gold standard method of manually marking these photoreceptors is highly subjective [33] and time consuming, which acts as a bottleneck limiting the clinical utilization of AOSLO systems. To combat this problem, several automated algorithms have been developed to detect cones in ophthalmic AO images taken from healthy [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50] and pathological [31,48,50] subjects. To date, no well validated automatic method for detecting individual rods in AOSLO images or method for classifying between cones and rods has been published.…”

“…CNNs have been utilized for a variety of tasks in ophthalmic image processing including classification [52][53][54][55][56], segmentation [57][58][59][60][61][62], and image enhancement [63]. CNNs have been used to achieve state-of-the-art performances for cone localization in ophthalmic AO images in healthy [46,49] and pathologic [31,50] eyes. Our recent work [31] showed that a CNN using multimodal confocal and split detector AOSLO information could improve the performance of detecting cones in subjects with achromatopsia (ACHM) by utilizing the complimentary information captured in both modalities.…”