Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function

Hu, Kai; Zhang, Zhenzhen; Niu, Xiaorui; Zhang, Yuan; Cao, Chunhong; Xiao, Fen; Gao, Xieping

doi:10.1016/j.neucom.2018.05.011

Cited by 289 publications

(113 citation statements)

References 20 publications

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“…Gu et al [35] proposed CE-Net for medical image segmentation which adopted pre-trained ResNet block in the feature encoder and applied dense atrous convolution block and residual multi-kernel pooling in context extractor. Hu et al [36] proposed a multiscale CNN architecture with an improved cross-entropy loss function and fully connected conditional random field (CRF) to detect hard examples and more details in fundus images. Mo et al [37] introduced a multi-level deep supervised network to retinal vessel segmentation.…”

Section: Supervised Methodsmentioning

confidence: 99%

“…The R2U-Net method proposed by Alom et al [33] lost some vascular context features in the coding process, and thus failed to segment the discontinuous blood vessels. The convolutional neural network and the fully connected condition method proposed by Hu et al [36] has a poor smoothing ability on the bright spot, which makes the segmentation results more disturbed by noise and fails to segment the blood vessel accurately. The scale detection module in our method has two feature propagation paths from the low layer to the high layer and from the high layer to the low layer, which can generate blood vessel contour prediction maps of different scales, effectively eliminating the influence of the bright spot background and segment discontinuous blood effectively.…”

Section: As Shown Inmentioning

confidence: 99%

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BSCN: bidirectional symmetric cascade network for retinal vessel segmentation

Guo

Peng

2020

BMC Med Imaging

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Background: Retinal blood vessel segmentation has an important guiding significance for the analysis and diagnosis of cardiovascular diseases such as hypertension and diabetes. But the traditional manual method of retinal blood vessel segmentation is not only time-consuming and laborious but also cannot guarantee the accuracy and efficiency of diagnosis. Therefore, it is especially significant to create a computer-aided method of automatic and accurate retinal vessel segmentation. Methods: In order to extract the blood vessels' contours of different diameters to realize fine segmentation of retinal vessels, we propose a Bidirectional Symmetric Cascade Network (BSCN) where each layer is supervised by vessel contour labels of specific diameter scale instead of using one general ground truth to train different network layers. In addition, to increase the multi-scale feature representation of retinal blood vessels, we propose the Dense Dilated Convolution Module (DDCM), which extracts retinal vessel features of different diameters by adjusting the dilation rate in the dilated convolution branches and generates two blood vessel contour prediction results by two directions respectively. All dense dilated convolution module outputs are fused to obtain the final vessel segmentation results. Results: We experimented the three datasets of DRIVE, STARE, HRF and CHASE_DB1, and the proposed method reaches accuracy of 0.9846/0.9872/0.9856/0.9889 and AUC of 0.9874/0.9941/0.9882/0.9874 on DRIVE, STARE, HRF and CHASE_DB1. Conclusions:The experimental results show that compared with the state-of-art methods, the proposed method has strong robustness, it not only avoids the adverse interference of the lesion background but also detects the tiny blood vessels at the intersection accurately.A new study published in the British "Lancet Global Health" predicts that if the treatment of eye diseases is not improved by better funding, the number of blind people worldwide will increase to 115 million by 2050, 2.2 times more than the current 36 million [1]. In fact, Retinal vessel disease in the fundus is one of the vital causes of blindness and many can be prevented in advance by fundus

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Section: Supervised Methodsmentioning

confidence: 99%

Section: As Shown Inmentioning

confidence: 99%

BSCN: bidirectional symmetric cascade network for retinal vessel segmentation

Guo

Peng

2020

BMC Med Imaging

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“…Using other loss function might obtain different results. However, the crossentropy loss function is widely used in deep learning field [44], [45].…”

Section: Threats To Construct Validitymentioning

confidence: 99%

Unsupervised Deep Domain Adaptation for Heterogeneous Defect Prediction

Gong

Jiang

et al. 2019

IEICE Trans. Inf. & Syst.

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Heterogeneous defect prediction (HDP) is to detect the largest number of defective software modules in one project by using historical data collected from other projects with different metrics. However, these data can not be directly used because of different metrics set among projects. Meanwhile, software data have more non-defective instances than defective instances which may cause a significant bias towards defective instances. To completely solve these two restrictions, we propose unsupervised deep domain adaptation approach to build a HDP model. Specifically, we firstly map the data of source and target projects into a unified metric representation (UMR). Then, we design a simple neural network (SNN) model to deal with the heterogeneous and class-imbalanced problems in software defect prediction (SDP). In particular, our model introduces the Maximum Mean Discrepancy (MMD) as the distance between the source and target data to reduce the distribution mismatch, and use the cross-entropy loss function as the classification loss. Extensive experiments on 18 public projects from four datasets indicate that the proposed approach can build an effective prediction model for heterogeneous defect prediction (HDP) and outperforms the related competing approaches.

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“…The second type is deep supervision that utilizes the groundtruth to guide model learning at intermediate layers of a CNN. It yielded better performance on several segmentation tasks, as demonstrated in [9][19] [22]. The third type is devising architectures that stack or concatenate multiple U-net-like FCNs.…”

Section: Introductionmentioning

confidence: 99%

ACE-Net: Biomedical Image Segmentation with Augmented Contracting and Expansive Paths

Zhu

Chen

Zhao

et al. 2019

Lecture Notes in Computer Science

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Nowadays U-net-like FCNs predominate various biomedical image segmentation applications and attain promising performance, largely due to their elegant architectures, e.g., symmetric contracting and expansive paths as well as lateral skip-connections. It remains a research direction to devise novel architectures to further benefit the segmentation. In this paper, we develop an ACE-net that aims to enhance the feature representation and utilization by augmenting the contracting and expansive paths. In particular, we augment the paths by the recently proposed advanced techniques including ASPP, dense connection and deep supervision mechanisms, and novel connections such as directly connecting the raw image to the expansive side. With these augmentations, ACE-net can utilize features from multiple sources, scales and reception fields to segment while still maintains a relative simple architecture. Experiments on two typical biomedical segmentation tasks validate its effectiveness, where highly competitive results are obtained in both tasks while ACE-net still runs fast at inference.

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Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function

Cited by 289 publications

References 20 publications

BSCN: bidirectional symmetric cascade network for retinal vessel segmentation

BSCN: bidirectional symmetric cascade network for retinal vessel segmentation

Unsupervised Deep Domain Adaptation for Heterogeneous Defect Prediction

ACE-Net: Biomedical Image Segmentation with Augmented Contracting and Expansive Paths

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