Scale-space approximated convolutional neural networks for retinal vessel segmentation

Noh, Kyoung Jin; Park, Sang Jun; Lee, Soochahn

doi:10.1016/j.cmpb.2019.06.030

Cited by 50 publications

(37 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Through this, ResNet showed better performance compared to VGG net and GoogLeNet in deep network learning. ResNet also demonstrated high performance for medical image recognition tasks such as retinal vessel segmentation 40 . Therefore we used ResNet, which has the advantages mentioned above, to develop an age and sex prediction model using the retinal fundus images.…”

Section: Discussionmentioning

confidence: 99%

“…To determine the effect of retinal blood vessels in retinal fundus images on predicting age and sex, we created vessel-erased images. First, the blood vessel region was extracted using the scale-space approximated CNN (SSANet) that was previously reported by our group and demonstrated state of the art performance in retinal vessel segmentation 40 . Subsequently, we used the inpainting technique, which naturally fills holes or some regions in the image by extrapolation from the surrounding background.…”

Section: Prediction Of Age and Sex In Retinal Fundus Images After Inpmentioning

confidence: 99%

See 1 more Smart Citation

Effects of Hypertension, Diabetes, and Smoking on Age and Sex Prediction from Retinal Fundus Images

Kim

Noh

Byun

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

Retinal fundus images are used to detect organ damage from vascular diseases (e.g. diabetes mellitus and hypertension) and screen ocular diseases. We aimed to assess convolutional neural network (CNN) models that predict age and sex from retinal fundus images in normal participants and in participants with underlying systemic vascular-altered status. In addition, we also tried to investigate clues regarding differences between normal ageing and vascular pathologic changes using the CNN models. In this study, we developed CNN age and sex prediction models using 219,302 fundus images from normal participants without hypertension, diabetes mellitus (DM), and any smoking history. The trained models were assessed in four test-sets with 24,366 images from normal participants, 40,659 images from hypertension participants, 14,189 images from DM participants, and 113,510 images from smokers. The CNN model accurately predicted age in normal participants; the correlation between predicted age and chronologic age was R 2 = 0.92, and the mean absolute error (MAE) was 3.06 years. MAEs in test-sets with hypertension (3.46 years), DM (3.55 years), and smoking (2.65 years) were similar to that of normal participants; however, R 2 values were relatively low (hypertension, R 2 = 0.74; DM, R 2 = 0.75; smoking, R 2 = 0.86). In subgroups with participants over 60 years, the MAEs increased to above 4.0 years and the accuracies declined for all test-sets. Fundus-predicted sex demonstrated acceptable accuracy (area under curve > 0.96) in all test-sets. Retinal fundus images from participants with underlying vascular-altered conditions (hypertension, DM, or smoking) indicated similar MAEs and low coefficients of determination (R 2) between the predicted age and chronologic age, thus suggesting that the ageing process and pathologic vascular changes exhibit different features. Our models demonstrate the most improved performance yet and provided clues to the relationship and difference between ageing and pathologic changes from underlying systemic vascular conditions. In the process of fundus change, systemic vascular diseases are thought to have a different effect from ageing. Research in context. Evidence before this study. The human retina and optic disc continuously change with ageing, and they share physiologic or pathologic characteristics with brain and systemic vascular status. As retinal fundus images provide high-resolution in-vivo images of retinal vessels and parenchyma without any invasive procedure, it has been used to screen ocular diseases and has attracted significant attention as a predictive biomarker for cerebral and systemic vascular diseases. Recently, deep neural networks have revolutionised the field of medical image analysis including retinal fundus images and shown reliable results in predicting age, sex, and presence of cardiovascular diseases. Added value of this study. This is the first study demonstrating how a convolutional neural network (CNN) trained using retinal fundus images from normal participants meas...

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Prediction Of Age and Sex In Retinal Fundus Images After Inpmentioning

confidence: 99%

Effects of Hypertension, Diabetes, and Smoking on Age and Sex Prediction from Retinal Fundus Images

Kim

Noh

Byun

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Blood vessels are segmented using convolutional neural network structure incorporated with scale-space theory in [41]. The proposed scale-space approximated network (SSANet) is designed by combining upsampling, downsampling and residual blocks.…”

Section: ) Neural Network-based Methodsmentioning

confidence: 99%

Comparative Analysis of Vessel Segmentation Techniques in Retinal Images

Imran

Pei

et al. 2019

IEEE Access

View full text Add to dashboard Cite

The blood vessels are the primary anatomical structure that can be visible in retinal images. The segmentation of retinal blood vessels has been accepted worldwide for the diagnosis of both cardiovascular (CVD) and retinal diseases. Thus, it requires an appropriate vessel segmentation method for automatic detection of retinal diseases such as diabetic retinopathy and cataract. The detection of retinal diseases using computer-aided diagnosis (CAD) can help people to avoid the risks of visual impairment and save medical resources. This survey presents a comparative analysis of various machine learning and deep learning-based methods for automated blood vessel segmentation in retinal images. This paper briefly describes fundus photography, publicly available retinal databases, pre-processing and post-processing techniques for retinal vessels segmentation. A comprehensive review of the state of the art supervised and unsupervised blood vessel segmentation methodologies are presented in this paper. The objective of this study is to establish a professional structure to familiarize an individual with up-to-date vessel segmentation techniques. Moreover, we compared these approaches to the dataset, evaluation metrics, pre-processing and post-processing steps, feature extraction, segmentation methods, and induced results.

show abstract

“…We apply the SSANet that was proposed by Noh et al [19], which incorporates a layer for scale-space approximation to better deal with vessels of various widths, in order to generate a pixelwise vessel probability map for each frame. We then construct a binary mask from the map through thresholding, which is then used as a stencil for enhancing the frame contrast.…”

Section: Vessel Segmentation and Frame Preprocessingmentioning

confidence: 99%

“…Visual description of the proposed preprocessing scheme based on vessel segmentation using the SSANet of[19].…”

mentioning

confidence: 99%

Fully Leveraging Deep Learning Methods for Constructing Retinal Fundus Photomontages

Kim

Noh

et al. 2021

Applied Sciences

Self Cite

View full text Add to dashboard Cite

Retinal photomontages, which are constructed by aligning and integrating multiple fundus images, are useful in diagnosing retinal diseases affecting peripheral retina. We present a novel framework for constructing retinal photomontages that fully leverage recent deep learning methods. Deep learning based object detection is used to define the order of image registration and blending. Deep learning based vessel segmentation is used to enhance image texture to improve registration performance within a two step image registration framework comprising rigid and non-rigid registration. Experimental evaluation demonstrates the robustness of our montage construction method with an increased amount of successfully integrated images as well as reduction of image artifacts.

show abstract

Scale-space approximated convolutional neural networks for retinal vessel segmentation

Cited by 50 publications

References 18 publications

Effects of Hypertension, Diabetes, and Smoking on Age and Sex Prediction from Retinal Fundus Images

Effects of Hypertension, Diabetes, and Smoking on Age and Sex Prediction from Retinal Fundus Images

Comparative Analysis of Vessel Segmentation Techniques in Retinal Images

Fully Leveraging Deep Learning Methods for Constructing Retinal Fundus Photomontages

Contact Info

Product

Resources

About