A Multi-Scale Feature Fusion Method Based on U-Net for Retinal Vessel Segmentation

Yang, Dan; Liu, Guo-Ru; Ren, Mengcheng; Bin, Xu; Wang, Jiao

doi:10.3390/e22080811

Cited by 41 publications

(16 citation statements)

References 50 publications

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“…Literature shows that models incorporating multi-scale features perform better at segmentation. 20 In conclusion, a 2D U-Net was implemented and trained on OCT b-scans for the segmentation of the follicular structure and surrounding non-follicular tissue. The method allowed a fast and reliable segmentation of the tissue microstructure, without the need for user intervention.…”

Section: Discussionmentioning

confidence: 99%

Deep-learning for thyroid microstructure segmentation in 2D OCT images

Tampu

Eklund

Haj‐Hosseini

2021

Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXV

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Optical coherence tomography (OCT) can provide exquisite details of tissue microstructure without traditional tissue sectioning, with potential diagnostic and intraoperative applications in a variety of clinical areas. In thyroid surgery, OCT could provide information to reduce the risk of damaging normal tissue. Thyroid tissue's follicular structure alters in case of various pathologies including the non-malignant ones which can be imaged using OCT. The success of deep learning for medical image analysis encourages its application on OCT thyroid images for quantitative analysis of tissue microstructure. To investigate the potential of a deep learning approach to segment the follicular structure in OCT images, a 2D U-Net was trained on b-scan OCT images acquired from ex vivo adult human thyroid samples affected by a range of pathologies. Results on a pool of 104 annotated images showed a mean Dice score of 0.74±0.19 and 0.92±0.09 when segmenting the follicular structure and the surrounding tissue on the test dataset (n=10 images). This study shows that a deep learning approach for tissue microstructure segmentation in OCT images is possible. The achieved performance without requiring manual intervention encourages the application of a deep-learning method for real-time analysis of OCT data.

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Section: Discussionmentioning

confidence: 99%

Deep-learning for thyroid microstructure segmentation in 2D OCT images

Tampu

Eklund

Haj‐Hosseini

2021

Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXV

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“…Based on the advantages of the U-Net network in medical images, many experts and scholars have proposed improvements to the U-Net model. [10][11][12][13] In this paper, we propose a dense connected U-Net retinal vessel segmentation algorithm based on multiscale feature convolution extraction. The proposed algorithm adopts Inception and dense block structure on the basis of U-Net.…”

Section: Introductionmentioning

confidence: 99%

“…The residual unit in R2 U‐Net is helpful to deepen the training structure, and recurrent residual can realize feature accumulation fusion and achieve better segmentation effect. Based on the advantages of the U‐Net network in medical images, many experts and scholars have proposed improvements to the U‐Net model 10–13 …”

Section: Introductionmentioning

confidence: 99%

Densely connected U‐Net retinal vessel segmentation algorithm based on multi‐scale feature convolution extraction

Wang

Sun

2021

Medical Physics

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The segmentation results of retinal blood vessels have a significant impact on the automatic diagnosis of various ophthalmic diseases. In order to further improve the segmentation accuracy of retinal vessels, we propose an improved algorithm based on multiscale vessel detection, which extracts features through densely connected networks and reuses features. Methods: A parallel fusion and serial embedding multiscale feature dense connection U-Net structure are designed. In the parallel fusion method, features of the input images are extracted for Inception multiscale convolution and dense block convolution, respectively, and then the features are fused and input into the subsequent network. In serial embedding mode, the Inception multiscale convolution structure is embedded in the dense connection network module, and then the dense connection structure is used to replace the classical convolution block in the U-Net network encoder part, so as to achieve multiscale feature extraction and efficient utilization of complex structure vessels and thereby improve the network segmentation performance. Results: The experimental analysis on the standard DRIVE and CHASE_DB1 databases shows that the sensitivity, specificity, accuracy, and AUC of the parallel fusion and serial embedding methods reach 0.

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“…ere are some limitations, such as insufficient segmentation degree and poor continuity of microretinal vessels, which cannot meet clinical diagnosis needs. e U-Net model has been applied to the segmentation of medical images for years, and many improved structures based on the U-Net model [29][30][31][32][33][34][35][36] have achieved good segmentation results. In this paper, we explore an automatic segmentation algorithm of retinal vessels based on improved U-Net.…”

Section: Introductionmentioning

confidence: 99%

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

Huang

Fang

Huang

et al. 2021

Scientific Programming

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Retinal blood vessels are the only deep microvessels in the blood circulation system that can be observed directly and noninvasively, providing us with a means of observing vascular pathologies. Cardiovascular and cerebrovascular diseases, such as glaucoma and diabetes, can cause structural changes in the retinal microvascular network. Therefore, the study of effective retinal vessel segmentation methods is of great significance for the early diagnosis of cardiovascular diseases and the vascular network’s quantitative results. This paper proposes an automatic retinal vessel segmentation method based on an improved U-Net network. Firstly, the image patches are rotated to amplify the image data, and then, the RGB fundus image is preprocessed by normalization. Secondly, after the improved U-Net model is constructed with 23 convolutional layers, 4 pooling layers, 4 upsampling layers, 2 dropout layers, and Squeeze and Excitation (SE) block, the extracted image patches are utilized for training the model. Finally, the fundus images are segmented through the trained model to achieve precise extraction of retinal blood vessels. According to experimental results, the accuracy of 0.9701, 0.9683, and 0.9698, sensitivity of 0.8011, 0.6329, and 0.7478, specificity of 0.9849, 0.9967, and 0.9895, F1-Score of 0.8099, 0.8049, and 0.8013, and area under the curve (AUC) of 0.8895, 0.8845, and 0.8686 were achieved on DRIVE, STARE, and HRF databases, respectively, which is better than most classical algorithms.

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A Multi-Scale Feature Fusion Method Based on U-Net for Retinal Vessel Segmentation

Cited by 41 publications

References 50 publications

Deep-learning for thyroid microstructure segmentation in 2D OCT images

Deep-learning for thyroid microstructure segmentation in 2D OCT images

Densely connected U‐Net retinal vessel segmentation algorithm based on multi‐scale feature convolution extraction

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

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