Paper Info
Title
A deeply supervised maximum response texton based SegNet for simultaneous multi retinal lesion segmentation
Abstract
Diabetic Retinopathy (DR) is a diabetic mellitus complication that causes vision impairment and may lead to permanent blindness. The early signs of DR that appear on the retinal surface are microaneurysms, hemorrhages, hard exudates, and soft exudates. Hence the automatic detection of these retinal lesions assists in the early diagnosis of DR. This paper presents a novel deep learning model, MRT-SegNet (Maximum Response Texton - Segmentation Network) for the automatic segmentation of different retinal lesions simultaneously along with the optic disc. In the proposed MRT-SegNet, each encoder block consists of an MRT filter bank that extracts the textural feature maps of the retinal images and then fuses them with the local feature maps that are extracted from the traditional encoder block of the network. This fusion enables the network to segment the minute lesions from the retinal surface. The proposed model is evaluated on the IDRiD dataset and achieves a mean Area Under the Precision & Recall Curve (mAUC_PR) of 0.698 and AUC_PR scores of 0.495, 0.706, 0.823, 0.769 for microaneurysms, hemorrhages, hard exudates, and soft exudates respectively. The experimental results demonstrate that the MRT-SegNet outperformed other multi retinal lesion segmentation models by achieving superior performance.
Year
DOI
Venue
2022
10.1002/ima.22723
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
Keywords
DocType
Volume
diabetic retinopathy, IDRiD dataset, maximum response texton filter bank, multi retinal lesion segmentation
Journal
32
Issue
ISSN
Citations 
5
0899-9457
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Geetha Pavani Pappu100.34
Talabhakthula Krishna200.34
Birendra Biswal300.34
Prakash Kumar Karn400.34
Pradyut Kumar Biswal5144.31
Shazia Hasan600.68
Debasish Nayak700.34