scholarly journals Semantic Segmentation of Eye Fundus Images Using Convolutional Neural Networks

2020 ◽  
Vol 90 ◽  
pp. 116-128
Author(s):  
Ričardas Toliušis ◽  
Olga Kurasova ◽  
Jolita Bernatavičienė
Keyword(s):  
Neural Networks ◽  
Diabetic Retinopathy ◽  
Macular Degeneration ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Optical Disk ◽  
Fundus Images ◽  
Excellent Performance ◽  
Segmentation Algorithms ◽  
Performance Results

The article reviews the problems of eye bottom fundus analysis and semantic segmentation algorithms used to distinguish eye vessels, optical disk. Various diseases, such as glaucoma, hypertension, diabetic retinopathy, macular degeneration, etc., can be diagnosed by changes and anomalies of vesssels and optical disk. For semantic segmentation convolutional neural networks, especially U-Net architecture, are well suited. Recently a number of U-Net modifications have been developed that deliver excellent performance results.

scholarly journals Semantic Segmentation of Eye Fundus Images Using Convolutional Neural Networks

2019 ◽  
Vol 85 ◽  
pp. 135-147
Author(s):  
Ričardas Toliušis ◽  
Olga Kurasova ◽  
Jolita Bernatavičienė
Keyword(s):  
Neural Networks ◽  
Diabetic Retinopathy ◽  
Macular Degeneration ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Optical Disk ◽  
Fundus Images ◽  
Excellent Performance ◽  
Segmentation Algorithms ◽  
Performance Results

This article reviews the problems of eye bottom fundus analysis and semantic segmentation algorithms used to distinguish the eye vessels and the optical disk. Various diseases, such as glaucoma, hypertension, diabetic retinopathy, macular degeneration, etc., can be diagnosed through changes and anomalies of the vesssels and optical disk. Convolutional neural networks, especially the U-Net architecture, are well-suited for semantic segmentation. A number of U-Net modifications have been recently developed that deliver excellent performance results.

scholarly journals Semantic Segmentation of Cerebellum in 2D Fetal Ultrasound Brain Images using Convolutional Neural Networks

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Vishal Singh ◽  
Pradeeba Sridar ◽  
Jinman Kim ◽  
Ralph Nanan ◽  
N. Poornima ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Fetal Ultrasound ◽  
Brain Images

scholarly journals Convolutional Neural Networks for Semantic Segmentation as a Tool for Multiclass Face Analysis in Thermal Infrared

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
David Müller ◽  
Andreas Ehlen ◽  
Bernd Valeske
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Thermal Infrared ◽  
Relative Temperature ◽  
Face Analysis ◽  
Image Translation ◽  
The Face ◽  
Live Image ◽  
Quantitative Thermography

AbstractConvolutional neural networks were used for multiclass segmentation in thermal infrared face analysis. The principle is based on existing image-to-image translation approaches, where each pixel in an image is assigned to a class label. We show that established networks architectures can be trained for the task of multiclass face analysis in thermal infrared. Created class annotations consisted of pixel-accurate locations of different face classes. Subsequently, the trained network can segment an acquired unknown infrared face image into the defined classes. Furthermore, face classification in live image acquisition is shown, in order to be able to display the relative temperature in real-time from the learned areas. This allows a pixel-accurate temperature face analysis e.g. for infection detection like Covid-19. At the same time our approach offers the advantage of concentrating on the relevant areas of the face. Areas of the face irrelevant for the relative temperature calculation or accessories such as glasses, masks and jewelry are not considered. A custom database was created to train the network. The results were quantitatively evaluated with the intersection over union (IoU) metric. The methodology shown can be transferred to similar problems for more quantitative thermography tasks like in materials characterization or quality control in production.

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