Automatic Retinal Vessel Segmentation Based on an Improved U-Net Approach

Huang, Zihe; Fang, Ying; Huang, He; Xu, Xiaomei; Wang, Jiwei; Lai, Xiaobo

doi:10.1155/2021/5520407

Cited by 8 publications

(9 citation statements)

References 49 publications

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“…Huang et al realized a supervised learning method using an improved U-Net network with 23 convolutional layers, and the accuracy of the DRIVE, STARE, and HRF datasets was 0.9701, 0.9683, and 0.9698, respectively. However, its area under the curve (AUC) was only 0.8895, 0.8845, and 0.8686 [ 20 ]. Liang et al fused the linear features, texture features, and the other features of retinal vessels to train a random forest classifier which realizes automatic segmentation of retinal vessels [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Retinal Vessel Automatic Segmentation Using SegNet

Wang

Liang

et al. 2022

Computational and Mathematical Methods in Medicine

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Extracting retinal vessels accurately is very important for diagnosing some diseases such as diabetes retinopathy, hypertension, and cardiovascular. Clinically, experienced ophthalmologists diagnose these diseases through segmenting retinal vessels manually and analysing its structural feature, such as tortuosity and diameter. However, manual segmentation of retinal vessels is a time-consuming and laborious task with strong subjectivity. The automatic segmentation technology of retinal vessels can not only reduce the burden of ophthalmologists but also effectively solve the problem that is a lack of experienced ophthalmologists in remote areas. Therefore, the automatic segmentation technology of retinal vessels is of great significance for clinical auxiliary diagnosis and treatment of ophthalmic diseases. A method using SegNet is proposed in this paper to improve the accuracy of the retinal vessel segmentation. The performance of the retinal vessel segmentation model with SegNet is evaluated on the three public datasets (DRIVE, STARE, and HRF) and achieved accuracy of 0.9518, 0.9683, and 0.9653, sensitivity of 0.7580, 0.7747, and 0.7070, specificity of 0.9804, 0.9910, and 0.9885, F 1 score of 0.7992, 0.8369, and 0.7918, MCC of 0.7749, 0.8227, and 0.7643, and AUC of 0.9750, 0.9893, and 0.9740, respectively. The experimental results showed that the method proposed in this research presented better results than many classical methods studied and may be expected to have clinical application prospects.

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Section: Introductionmentioning

confidence: 99%

Retinal Vessel Automatic Segmentation Using SegNet

Wang

Liang

et al. 2022

Computational and Mathematical Methods in Medicine

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“…The same U-shaped architecture of DUNet [25] and Sine-Net [27], the latter of which takes an up-sampling followed by down-sampling approach, this method largely compensates for the shortcomings of partial capillary segmentation in the former method. In addition to this, the HAnet [26] and Huang et al [3] methods are also improved U-shaped networks, with the difference that HAnet is designed with multiple decoders focusing on features in different regions. Although HAnet is not as accurate as of the segmentation of Huang et al's method, the vascular continuity of its method segmentation is stronger than that of the latter.…”

Section: Retinal Segmentation Results Of Different Methods On the Drive Datasetmentioning

confidence: 99%

“…The retinal vasculature is again the only deep microvasculature in the blood circulation system which can be directly and noninvasively visualized. It is extremely rich in information about its vascular characteristics [3]. More importantly, the morphological information related to the retinal vascular tree (e.g.…”

Section: Introductionmentioning

confidence: 99%

RFARN: Retinal vessel segmentation based on reverse fusion attention residual network

et al. 2021

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Accurate segmentation of retinal vessels is critical to the mechanism, diagnosis, and treatment of many ocular pathologies. Due to the poor contrast and inhomogeneous background of fundus imaging and the complex structure of retinal fundus images, this makes accurate segmentation of blood vessels from retinal images still challenging. In this paper, we propose an effective framework for retinal vascular segmentation, which is innovative mainly in the retinal image pre-processing stage and segmentation stage. First, we perform image enhancement on three publicly available fundus datasets based on the multiscale retinex with color restoration (MSRCR) method, which effectively suppresses noise and highlights the vessel structure creating a good basis for the segmentation phase. The processed fundus images are then fed into an effective Reverse Fusion Attention Residual Network (RFARN) for training to achieve more accurate retinal vessel segmentation. In the RFARN, we use Reverse Channel Attention Module (RCAM) and Reverse Spatial Attention Module (RSAM) to highlight the shallow details of the channel and spatial dimensions. And RCAM and RSAM are used to achieve effective fusion of deep local features with shallow global features to ensure the continuity and integrity of the segmented vessels. In the experimental results for the DRIVE, STARE and CHASE datasets, the evaluation metrics were 0.9712, 0.9822 and 0.9780 for accuracy (Acc), 0.8788, 0.8874 and 0.8352 for sensitivity (Se), 0.9803, 0.9891 and 0.9890 for specificity (Sp), area under the ROC curve(AUC) was 0.9910, 0.9952 and 0.9904, and the F1-Score was 0.8453, 0.8707 and 0.8185. In comparison with existing retinal image segmentation methods, e.g. UNet, R2UNet, DUNet, HAnet, Sine-Net, FANet, etc., our method in three fundus datasets achieved better vessel segmentation performance and results.

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“…To begin with, the encoder is the most adapted and most changeable component of the UNet architecture. Since it is practically not possible to study each of the architectural variations in the encoder, we have therefore listed here the 23 variations (E1 to E23, representing encoder changes) along with their references in a tabular format and it is as follows: (E1) conventional system (Ronneberger) [43][44][45][46][47][48][49][50][51][52]90]; (E2) cascade of convolutions [77,91,99,116,117]; (E3) parallel convolutions (multiple convolution network) [57]; (E4) convolution with dropout [70,76,86,95,101,102,134,138]; (E5) Residual network [76,78,105,129,135,138,[149][150][151]; (E6) Xception encoder [56,88,112]; (E7) encoder layers with independent inputs [104,140]; (E8) squeeze excitation (SE) network [92,103,138]; (E9) pooling types (max pooling, global average pooling) [95]; (E10) input image dimension change with changing filter (channe...…”

Section: A Encoder Variationsmentioning

confidence: 99%

“…Note that the decoder receives input in many different ways, such as encoder, skip connection or data after data transmission via bridge network (or bottle neck). Using these fundamental changes, the decoder variations can be categorized into 16 different type listed as follows: (D1) convolution with dropout [70,76,86,95,101,102,134,138]; (D2) UNet++ type of change [130,144,154]; (D3) UNet+++ (UNet 3+) Full scale deep supervision [157]; (D4) Output from decoders to make a loss function [104,140]; (D5) fusion of the decoder outputs for scale adjustment [59,107]; (D6) recurrent residual [118,129,138]; (D7) residual block [75,84,88,105,138,150]; (D8) channel attention and scale attention block [65,113]; (D9) transpose convolution [66,88,94,95,139]; (D10) squeeze excitation (SE) Network [103,125]; (D11) cascade convolution [99]; (D12) addition of original image to each layer [100]; (D13) batch normalization [95,106,155]; (D14) inception block [97]; (D15) dense layer [87,91,…”

Section: B Decoder Variationsmentioning

confidence: 99%

UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias

Suri¹,

Bhagawati

Agarwal³

et al. 2023

IEEE Access

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Background & Motivation: Biomedical image segmentation (BIS) task is challenging due to the variations in organ types, position, shape, size, scale, orientation, and image contrast. Conventional methods lack accurate and automated designs. Artificial intelligence (AI)-based UNet has recently dominated BIS. This is the first review of its kind that microscopically addressed UNet types by complexity, stratification of UNet by its components, addressing UNet in vascular vs. non-vascular framework, the key to segmentation challenge vs. UNet-based architecture, and finally interfacing the three facets of AI, the pruning, the explainable AI (XAI), and the AI-bias. Methods: PRISMA was used to select 267 UNet-based studies. Five classes were identified and labeled as conventional UNet, superior UNet, attention-channel UNet, hybrid UNet, and ensemble UNet. We discovered 81 variations of UNet by considering six kinds of components, namely encoder, decoder, skip connection, bridge network, loss function, and their combination. Vascular vs. non-vascular UNet architecture was compared. AP(ai)Bias 2.0-UNet was identified in these UNet classes based on (i) attributes of UNet architecture and its performance, (ii) explainable AI (XAI), and, (iii) pruning (compression). Five bias methods such as (i) ranking, (ii) radial, (iii) regional area, (iv) PROBAST, and (v) ROBINS-I were applied and compared using a Venn diagram. Findings and Conclusions: Vascular and non-vascular UNet systems dominated with sUNet classes with attention. The majority of the studies suffered from low interest in XAI and pruning strategies. None of the UNet models qualified to be bias-free. There is a need to move from paper-to-practice paradigms for clinical evaluation and settings. INDEX TERMS Image segmentation; vascular; non-vascular; UNet classes; UNet variations; UNetcomponents; explainable AI; pruning; bias.

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Automatic Retinal Vessel Segmentation Based on an Improved U-Net Approach

Cited by 8 publications

References 49 publications

Retinal Vessel Automatic Segmentation Using SegNet

Retinal Vessel Automatic Segmentation Using SegNet

RFARN: Retinal vessel segmentation based on reverse fusion attention residual network

UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias

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