Unsupervised Approaches for the Segmentation of Dry ARMD Lesions in Eye Fundus cSLO Images

Efficient and Robust Method to Detect the Location of Macular Center Based on Optimal Temporal Determination

The location of the macular central is very important for the examination of macular edema when using an automated screening system. The erratic character of the macular light intensity and the absence of a clear border make this anatomical structure difficult to detect. This paper presents a new method for detecting the macular center based on its geometrical location in the temporal direction of the optic disc. Also, a new method of determining the temporal direction using the vascular features visible on the optic disc is proposed. After detecting the optic disc, the temporal direction is determined by considering blood vessel positions. The macular center is detected using thresholding and simple morphology operations with optimum macular region of interest (ROI) direction. The results show that the proposed method has a low computation time of 0.34 s/image with 100% accuracy for the DRIVE dataset, while that of DiaretDB1 was 0.57 s/image with 98.87% accuracy.

Frontiers in Retinal Image Processing

Visual impairment is considered as a primary global challenge in the present era [...]

Encoder-Weighted W-Net for Unsupervised Segmentation of Cervix Region in Colposcopy Images

Simple Summary

The cervix region segmentation significantly affects the accuracy of diagnosis when analyzing colposcopy. Detecting the cervix region requires manual, intensive, and time-consuming labor from a trained gynecologist. In this paper, we propose a deep learning-based automatic cervix region segmentation method that enables the extraction of the region of interest from colposcopy images in an unsupervised manner. The segmentation performance with a Dice coefficient improved from 0.612 to 0.710 by applying the proposed loss function and encoder-weighted learning scheme and showed the best performance among all the compared methods. The automatically detected cervix region can improve the performance of image-based interpretation and diagnosis by suggesting where the clinicians should focus during colposcopy analysis.

Abstract

Cervical cancer can be prevented and treated better if it is diagnosed early. Colposcopy, a way of clinically looking at the cervix region, is an efficient method for cervical cancer screening and its early detection. The cervix region segmentation significantly affects the performance of computer-aided diagnostics using a colposcopy, particularly cervical intraepithelial neoplasia (CIN) classification. However, there are few studies of cervix segmentation in colposcopy, and no studies of fully unsupervised cervix region detection without image pre- and post-processing. In this study, we propose a deep learning-based unsupervised method to identify cervix regions without pre- and post-processing. A new loss function and a novel scheduling scheme for the baseline W-Net are proposed for fully unsupervised cervix region segmentation in colposcopy. The experimental results showed that the proposed method achieved the best performance in the cervix segmentation with a Dice coefficient of 0.71 with less computational cost. The proposed method produced cervix segmentation masks with more reduction in outliers and can be applied before CIN detection or other diagnoses to improve diagnostic performance. Our results demonstrate that the proposed method not only assists medical specialists in diagnosis in practical situations but also shows the potential of an unsupervised segmentation approach in colposcopy.