Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema.

By Stephanie J. Chiu, Michael J. Allingham, Priyatham S. Mettu, S. Cousins, J. Izatt, Sina Farsiu. Biomedical Optics Express, 2015

359 citations recorded by the source corpus.

We present a fully automatic algorithm to identify fluid-filled regions and seven retinal layers on spectral domain optical coherence tomography images of eyes with diabetic macular edema (DME). To achieve this, we developed a kernel regression (KR)-based classification method to estimate fluid and retinal layer positions. We then used these classification estimates as a guide to more accurately segment the retinal layer boundaries using our previously described graph theory and dynamic programming (GTDP) framework. We validated our algorithm on 110 B-scans from ten patients with severe DME pathology, showing an overall mean Dice coefficient of 0.78 when comparing our KR + GTDP algorithm to an expert grader. This is comparable to the inter-observer Dice coefficient of 0.79. The entire data set is available online, including our automatic and manual segmentation results. To the best of our knowledge, this is the first validated, fully-automated, seven-layer and fluid segmentation method which has been applied to real-world images containing severe DME.

Subfields: Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c5045bb9ef687fdf8ec9209d809d3052fcd9fe07

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Recalibrating Fully Convolutional Networks With Spatial and Channel “Squeeze and Excitation” Blocks (2018, methodology): “Scans from subjects 1-5 were used for training, and subjects 6-10 for testing, consistent with [14] and [21].”