Ontology type: schema:Chapter Open Access: True
2018
AUTHORSSuman Sedai , Bhavna Antony , Dwarikanath Mahapatra , Rahil Garnavi
ABSTRACTOptical coherence tomography (OCT) is commonly used to analyze retinal layers for assessment of ocular diseases. In this paper, we propose a method for retinal layer segmentation and quantification of uncertainty based on Bayesian deep learning. Our method not only performs end-to-end segmentation of retinal layers, but also gives the pixel wise uncertainty measure of the segmentation output. The generated uncertainty map can be used to identify erroneously segmented image regions which is useful in downstream analysis. We have validated our method on a dataset of 1487 images obtained from 15 subjects (OCT volumes) and compared it against the state-of-the-art segmentation algorithms that does not take uncertainty into account. The proposed uncertainty based segmentation method results in comparable or improved performance, and most importantly is more robust against noise. More... »
PAGES219-227
Computational Pathology and Ophthalmic Medical Image Analysis
ISBN
978-3-030-00948-9
978-3-030-00949-6
http://scigraph.springernature.com/pub.10.1007/978-3-030-00949-6_26
DOIhttp://dx.doi.org/10.1007/978-3-030-00949-6_26
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