RT Journal Article
SR Electronic
T1 Semantic segmentation of gonio-photographs via adaptive ROI localisation and uncertainty estimation
JF BMJ Open Ophthalmology
JO BMJ Open Ophth
FD BMJ Publishing Group Ltd
SP e000898
DO 10.1136/bmjophth-2021-000898
VO 6
IS 1
A1 Andrea Peroni
A1 Anna Paviotti
A1 Mauro Campigotto
A1 Luis Abegão Pinto
A1 Carlo Alberto Cutolo
A1 Jacintha Gong
A1 Sirjhun Patel
A1 Caroline Cobb
A1 Stewart Gillan
A1 Andrew Tatham
A1 Emanuele Trucco
YR 2021
UL http://bmjophth.bmj.com/content/6/1/e000898.abstract
AB Objective To develop and test a deep learning (DL) model for semantic segmentation of anatomical layers of the anterior chamber angle (ACA) in digital gonio-photographs.Methods and analysis We used a pilot dataset of 274 ACA sector images, annotated by expert ophthalmologists to delineate five anatomical layers: iris root, ciliary body band, scleral spur, trabecular meshwork and cornea. Narrow depth-of-field and peripheral vignetting prevented clinicians from annotating part of each image with sufficient confidence, introducing a degree of subjectivity and features correlation in the ground truth. To overcome these limitations, we present a DL model, designed and trained to perform two tasks simultaneously: (1) maximise the segmentation accuracy within the annotated region of each frame and (2) identify a region of interest (ROI) based on local image informativeness. Moreover, our calibrated model provides results interpretability returning pixel-wise classification uncertainty through Monte Carlo dropout.Results The model was trained and validated in a 5-fold cross-validation experiment on ~90% of available data, achieving ~91% average segmentation accuracy within the annotated part of each ground truth image of the hold-out test set. An appropriate ROI was successfully identified in all test frames. The uncertainty estimation module located correctly inaccuracies and errors of segmentation outputs.Conclusion The proposed model improves the only previously published work on gonio-photographs segmentation and may be a valid support for the automatic processing of these images to evaluate local tissue morphology. Uncertainty estimation is expected to facilitate acceptance of this system in clinical settings.No data are available. Not applicable.