DilUnet: A U-net based architecture for blood vessels segmentation

Hussain, Snawar; Guo, Fan; Li, Weiqing; Shen, Ziqi

doi:10.1016/j.cmpb.2022.106732

Cited by 22 publications

(6 citation statements)

References 51 publications

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“…All OCT images were segmented using an OCT B-scan automatic image segmentation model utilizing deep learning [ 19 ]. For a brief overview, the segmentation model is based on U-Net [ 20 ] and consists of an encoder and decoder, a skip connection between the two, and a multiple dilated convolution (MDC) block.…”

Section: Methodsmentioning

confidence: 99%

Wayfinding artificial intelligence to detect clinically meaningful spots of retinal diseases: Artificial intelligence to help retina specialists in real world practice

et al. 2023

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Aim/background To aim of this study is to develop an artificial intelligence (AI) that aids in the thought process by providing retinal clinicians with clinically meaningful or abnormal findings rather than just a final diagnosis, i.e., a “wayfinding AI.” Methods Spectral domain optical coherence tomography B-scan images were classified into 189 normal and 111 diseased eyes. These were automatically segmented using a deep-learning based boundary-layer detection model. During segmentation, the AI model calculates the probability of the boundary surface of the layer for each A-scan. If this probability distribution is not biased toward a single point, layer detection is defined as ambiguous. This ambiguity was calculated using entropy, and a value referred to as the ambiguity index was calculated for each OCT image. The ability of the ambiguity index to classify normal and diseased images and the presence or absence of abnormalities in each layer of the retina were evaluated based on the area under the curve (AUC). A heatmap, i.e., an ambiguity-map, of each layer, that changes the color according to the ambiguity index value, was also created. Results The ambiguity index of the overall retina of the normal and disease-affected images (mean ± SD) were 1.76 ± 0.10 and 2.06 ± 0.22, respectively, with a significant difference (p < 0.05). The AUC used to distinguish normal and disease-affected images using the ambiguity index was 0.93, and was 0.588 for the internal limiting membrane boundary, 0.902 for the nerve fiber layer/ganglion cell layer boundary, 0.920 for the inner plexiform layer/inner nuclear layer boundary, 0.882 for the outer plexiform layer/outer nuclear layer boundary, 0.926 for the ellipsoid zone line, and 0.866 for the retinal pigment epithelium/Bruch’s membrane boundary. Three representative cases reveal the usefulness of an ambiguity map. Conclusions The present AI algorithm can pinpoint abnormal retinal lesions in OCT images, and its localization is known at a glance when using an ambiguity map. This will help diagnose the processes of clinicians as a wayfinding tool.

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Section: Methodsmentioning

confidence: 99%

Wayfinding artificial intelligence to detect clinically meaningful spots of retinal diseases: Artificial intelligence to help retina specialists in real world practice

et al. 2023

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“…Finally, Gamma Correction (GC) is introduced as an effective contrast enhancement technique. GC effectively highlights darker vessel structures in retinal images [ 36 ]. Figure 7 illustrates the image processing results of the aforementioned steps.…”

Section: Materials and Data Preprocessingmentioning

confidence: 99%

FRD-Net: a full-resolution dilated convolution network for retinal vessel segmentation

Huang,

Shang,

2024

Biomed. Opt. Express

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Accurate and automated retinal vessel segmentation is essential for performing diagnosis and surgical planning of retinal diseases. However, conventional U-shaped networks often suffer from segmentation errors when dealing with fine and low-contrast blood vessels due to the loss of continuous resolution in the encoding stage and the inability to recover the lost information in the decoding stage. To address this issue, this paper introduces an effective full-resolution retinal vessel segmentation network, namely FRD-Net, which consists of two core components: the backbone network and the multi-scale feature fusion module (MFFM). The backbone network achieves horizontal and vertical expansion through the interaction mechanism of multi-resolution dilated convolutions while preserving the complete image resolution. In the backbone network, the effective application of dilated convolutions with varying dilation rates, coupled with the utilization of dilated residual modules for integrating multi-scale feature maps from adjacent stages, facilitates continuous learning of multi-scale features to enhance high-level contextual information. Moreover, MFFM further enhances segmentation by fusing deeper multi-scale features with the original image, facilitating edge detail recovery for accurate vessel segmentation. In tests on multiple classical datasets,compared to state-of-the-art segmentation algorithms, FRD-Net achieves superior performance and generalization with fewer model parameters.

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“…(M8) combination of different application [146,151]; (M9) fusion of different classifiers [145]; (M10) linear combination [147]; (M11) UNet training model [64]; (M12) combination of fully convolutional net (FCN) and UNet 7 [143]; (M13) hierarchically-fused multi-task learning (MTL) [83]; (M14) ZNet [85]; (M15) BRAVE-Net [110]; (M16) DilUNet [115]; (M17) T-Net [117]; (M18) RFARN [119]; and (M19) CondenseUNet [163]. A set of representative examples will be discussed in section VI.…”

Section: E Miscellaneous Variations In Unet By External Additionsmentioning

confidence: 99%

“…Initiation networks have improved gradually with more up-to-date and fresher variants and have outperformed different structure (Figure 7 (a)). The other modification is the transpose convolution [46,64,71,113,115], which is opposite of the convolution.…”

Section: G Understanding Major Blocks Affecting For Unet Modificationmentioning

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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DilUnet: A U-net based architecture for blood vessels segmentation

Cited by 22 publications

References 51 publications

Wayfinding artificial intelligence to detect clinically meaningful spots of retinal diseases: Artificial intelligence to help retina specialists in real world practice

Wayfinding artificial intelligence to detect clinically meaningful spots of retinal diseases: Artificial intelligence to help retina specialists in real world practice

FRD-Net: a full-resolution dilated convolution network for retinal vessel segmentation

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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