Dense Residual Convolutional Auto Encoder For Retinal Blood Vessels Segmentation

Adarsh, R; Amarnageswarao, Gadipudi; Pandeeswari, R.; Deivalakshmi, S.

doi:10.1109/icaccs48705.2020.9074172

Cited by 13 publications

(5 citation statements)

References 11 publications

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“…The two most interesting classes regard the vascular structures of the used specimen. There are many applications, in which delineating vessels with various imaging modalities plays an important role, e.g., for early detection of severe diseases, like diabetes, hypertensive retinopathy, or retinal vessel segmentation [39][40][41]. Retinal images, however, consist of long and fine-grained homogeneous structures with significant lower complexity than histological tissue images.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…Vessel Segmentation: Segmentation of blood vessels in various medical images, such as magnetic resonance angiography (MRA) scans [38], retinal images [39][40][41], and brain scans [42], which is important for the diagnosis and treatment of various diseases, such as diabetic retinopathy and stroke. Vessel segmentation is applied to various image modalities, yet the vessels are mostly homogeneous in structure.…”

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confidence: 99%

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Vessel Delineation Using U-Net: A Sparse Labeled Deep Learning Approach for Semantic Segmentation of Histological Images

Glänzer,

Masalkhi,

Roeth

et al. 2023

Cancers

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Semantic segmentation is an important imaging analysis method enabling the identification of tissue structures. Histological image segmentation is particularly challenging, having large structural information while providing only limited training data. Additionally, labeling these structures to generate training data is time consuming. Here, we demonstrate the feasibility of a semantic segmentation using U-Net with a novel sparse labeling technique. The basic U-Net architecture was extended by attention gates, residual and recurrent links, and dropout regularization. To overcome the high class imbalance, which is intrinsic to histological data, under- and oversampling and data augmentation were used. In an ablation study, various architectures were evaluated, and the best performing model was identified. This model contains attention gates, residual links, and a dropout regularization of 0.125. The segmented images show accurate delineations of the vascular structures (with a precision of 0.9088 and an AUC-ROC score of 0.9717), and the segmentation algorithm is robust to images containing staining variations and damaged tissue. These results demonstrate the feasibility of sparse labeling in combination with the modified U-Net architecture.

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Section: Discussion and Future Workmentioning

confidence: 99%

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confidence: 99%

Vessel Delineation Using U-Net: A Sparse Labeled Deep Learning Approach for Semantic Segmentation of Histological Images

Glänzer,

Masalkhi,

Roeth

et al. 2023

Cancers

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“…Residual learning [90] was also introduced to increase the depth of networks as well as alleviate vanishing/exploding gradients. It was applied to building blocks [91][92][93][94][95] or skip connections [95,96].…”

Section: U-net For Retinal Vessel Segmentationmentioning

confidence: 99%

Retinal Vessel Segmentation Using Deep Learning: A Review

et al. 2021

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This paper presents a comprehensive review of retinal blood vessel segmentation based on deep learning. The geometric characteristics of retinal vessels reflect the health status of patients and help to diagnose some diseases such as diabetes and hypertension. The accurate diagnosis and timing treatment of these diseases can prevent global blindness of patients. Recently, deep learning algorithms have been rapidly applied to retinal vessel segmentation due to their higher efficiency and accuracy, when compared with manual segmentation and other computer-aided diagnosis techniques. In this work, we reviewed recent publications for retinal vessel segmentation based on deep learning. We surveyed these proposed methods especially the network architectures and figured out the trend of models. We summarized obstacles and key aspects for applying deep learning to retinal vessel segmentation and indicated future research directions. This article will help researchers to construct more advanced and robust models.INDEX TERMS Retinal vessel segmentation, fundus images, deep learning, convolutional neural network.

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“… Ribeiro, Lopes & Silva (2019) explored the implementation of two ensemble techniques for RVS, Stochastic Weight Averaging and Snapshot Ensembles. Adarsh et al (2020) implemented an auto encoder deep learning network model based on residual paths and a U-Net that effectively segmented retinal blood vessels. Guo et al presented a multi-scale supervised deep learning network with short connections (BTS-DSN) ( Guo et al, 2019 ) for vessel segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…A trimap is obtained via a bi-level thresholding of the score map using existing methods, which is instrumental in focusing the attention to the pixels of these unknown areas. Among these ANN methods, ( Yang et al, 2020 ; Kromm & Rohr, 2020 ; Adarsh et al, 2020 ; Guo et al, 2019 ; Yan, Yang & Cheng, 2019 ) have researched on multi-scale features, ( Li et al, 2020 ) has researched attention mechanisms, ( Ribeiro, Lopes & Silva, 2019 ) has researched ensemble strategy methods, Leopold et al (2017) has researched the dependency between pixels, and ( Zhao, Li & Cheng, 2020 ) has researched post-processing methods. These studies can improve the accuracy of segmentation models.…”

Section: Introductionmentioning

confidence: 99%

DBFU-Net: Double branch fusion U-Net with hard example weighting train strategy to segment retinal vessel

Huang

Zhong

Chen

et al. 2022

PeerJ Computer Science

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Background Many fundus imaging modalities measure ocular changes. Automatic retinal vessel segmentation (RVS) is a significant fundus image-based method for the diagnosis of ophthalmologic diseases. However, precise vessel segmentation is a challenging task when detecting micro-changes in fundus images, e.g., tiny vessels, vessel edges, vessel lesions and optic disc edges. Methods In this paper, we will introduce a novel double branch fusion U-Net model that allows one of the branches to be trained by a weighting scheme that emphasizes harder examples to improve the overall segmentation performance. A new mask, we call a hard example mask, is needed for those examples that include a weighting strategy that is different from other methods. The method we propose extracts the hard example mask by morphology, meaning that the hard example mask does not need any rough segmentation model. To alleviate overfitting, we propose a random channel attention mechanism that is better than the drop-out method or the L2-regularization method in RVS. Results We have verified the proposed approach on the DRIVE, STARE and CHASE datasets to quantify the performance metrics. Compared to other existing approaches, using those dataset platforms, the proposed approach has competitive performance metrics. (DRIVE: F1-Score = 0.8289, G-Mean = 0.8995, AUC = 0.9811; STARE: F1-Score = 0.8501, G-Mean = 0.9198, AUC = 0.9892; CHASE: F1-Score = 0.8375, G-Mean = 0.9138, AUC = 0.9879). Discussion The segmentation results showed that DBFU-Net with RCA achieves competitive performance in three RVS datasets. Additionally, the proposed morphological-based extraction method for hard examples can reduce the computational cost. Finally, the random channel attention mechanism proposed in this paper has proven to be more effective than other regularization methods in the RVS task.

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Dense Residual Convolutional Auto Encoder For Retinal Blood Vessels Segmentation

Cited by 13 publications

References 11 publications

Vessel Delineation Using U-Net: A Sparse Labeled Deep Learning Approach for Semantic Segmentation of Histological Images

Vessel Delineation Using U-Net: A Sparse Labeled Deep Learning Approach for Semantic Segmentation of Histological Images

Retinal Vessel Segmentation Using Deep Learning: A Review

DBFU-Net: Double branch fusion U-Net with hard example weighting train strategy to segment retinal vessel

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