Comparison of various neural network-based models for retinal lesion analysis

Eddie M. Gil; Mark A. Keppler; Vladislav V. Yakovlev; Adam Boretsky; Joel N. Bixler

doi:10.1117/12.2507908

1 March 2019 Comparison of various neural network-based models for retinal lesion analysis

Eddie M. Gil, Mark A. Keppler, Vladislav V. Yakovlev, Adam Boretsky, Joel N. Bixler

Proceedings Volume 10876, Optical Interactions with Tissue and Cells XXX; 108760M (2019) https://doi.org/10.1117/12.2507908
Event: SPIE BiOS, 2019, San Francisco, California, United States

Abstract

Identification and analysis of laser-induced lesions on the retina can be challenging in both the research and clinical settings depending on the age of a lesion and the imaging modality used for detection. Previous research exploring retinal damage thresholds utilized the consensus of an expert panel to confirm energies required for minimal visible lesions, a method that includes some subjectivity. Because of this, there is a desire to develop an image processing architecture to accurately locate retinal laser lesions in images generated from clinically relevant modalities. Issues such as imaging aberrations inducing circular artifacts, perceived stretch in lesions, and differences in the appearance of lesions across the dataset preclude use of traditional image processing tools. A database containing images of laser lesions has been developed in order to provide a reference for researchers and clinicians. In this work, we explored using various Convolutional Neural Network (CNN) architectures and preprocessing techniques to more objectively identify and analyze retinal laser lesions. Specifically, we developed frequency domain filtering techniques in order to emphasize lesion qualities. We consider this task to be one of image segmentation to make the networks somewhat size invariant. Since the lesions account for a small amount of the image pixels, we implemented an intersection-based loss function. We evaluated the performance of our trained networks against more complicated architecture variants. Additionally, we trained a network to segment and classify lesions as the result of photochemical, photomechanical or photothermal damage.

Citation Download Citation

Eddie M. Gil, Mark A. Keppler, Vladislav V. Yakovlev, Adam Boretsky, and Joel N. Bixler "Comparison of various neural network-based models for retinal lesion analysis", Proc. SPIE 10876, Optical Interactions with Tissue and Cells XXX, 108760M (1 March 2019); https://doi.org/10.1117/12.2507908

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