Weakly-Supervised Individual Ganglion Cell Segmentation from Adaptive Optics OCT Images for Glaucomatous Damage Assessment
Posted on 03.05.2021 - 19:28
Cell-level quantitative features of retinal ganglion cells (GCs) are potentially important biomarkers for improved diagnosis and treatment monitoring of neurodegenerative diseases like glaucoma, Parkinson's disease, and Alzheimer's disease. Yet, due to limited resolution, individual GCs cannot be visualized by commonly used ophthalmic imaging systems, including optical coherence tomography (OCT), and assessment is limited to gross layer thickness analysis. Adaptive optics OCT (AO-OCT) enables in vivo imaging of individual retinal GCs. We present the first automated segmentation of the GC layer (GCL) somas from AO-OCT volumes based on weakly-supervised deep learning (named WeakGCSeg), which effectively utilizes weak annotations in the training process. Experimental results show that WeakGCSeg is on par or superior to human experts and is superior to other state-of-the-art networks. The automated quantitative features of individual GCLs showed an increase in structure-function correlation in glaucoma subjects compared to using thickness measures from OCT images. These results suggest that WeakGCSeg should have a broad appeal for long-term investigations of GC populations.
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Soltanian-Zadeh, Somayyeh; Kurokawa, Kazuhiro; Liu, Zhuolin; Zhang, Furu; Osamah, Saeedi; Hammer, Daniel; et al. (2021): Weakly-Supervised Individual Ganglion Cell Segmentation from Adaptive Optics OCT Images for Glaucomatous Damage Assessment. The Optical Society. Collection. https://doi.org/10.6084/m9.figshare.c.5255954.v2
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