Segmenting nailfold capillaries using an improved U-net network

Liu, Shupeng; Li, Yuemei; Zhou, Jingjing; Hu, Junwei; Chen, Na; Shang, Yana; Chen, Zhenyi; Li, Taihao

doi:10.1016/j.mvr.2020.104011

Cited by 18 publications

(9 citation statements)

References 15 publications

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“…U-net has been used on OCT for segmentation of retinal layers [339]- [341], blood vessels [342], fluid regions [343], [344], and Drusen [345]. Other uncommon applications are segmentation of blood vessels in digital subtraction angiography (DSA) [68], [346], [347], white matter tract segmentation in diffusion tensor imaging (DTI) [30], iris segmentation in iris imaging [37], tumor detection in mammograms [56], and capillary segmentation in nailfold capillaroscopy [348]. Table 8 collects the applications of U-net based models on some uncommon image modalities.…”

Section: H Other Modalitiesmentioning

confidence: 99%

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net's potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

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Section: H Other Modalitiesmentioning

confidence: 99%

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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“…Because the continuous pooling and downsampling operations of Unet will lead to the loss of some spatial information, Sun proposes a U-type context residual network (UCR-Net), which can capture more context and efficient information and recover more advanced semantic features [26]. Liu et al proposes a deep neural network with Res-Net structure, which combines the residual network (ResNet) and Unet to build a code-decode network, deepen the layers in the network, and save more details in low-quality images [27]. Beeche et al integrate a dynamic acceptance domain module and a fusion upsampling module into the Unet architecture to form a super Unet, which can be applied to image segmentation of blood vessels, gastrointestinal tract, skin diseases, etc [28].…”

Section: Related Workmentioning

confidence: 99%

Research on liver cancer segmentation method based on PCNN image processing and SE-ResUnet

Zang

Lǐ

et al. 2023

Preprint

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As one of the malignant tumors with high mortality, the initial symptoms of liver cancer are not obvious. In addition, the liver is the largest internal organ of the human body, and its structure and distribution are relatively complex. Therefore, in order to help doctors judge liver cancer more accurately, this paper proposes a variant model based on Unet network. Before segmentation, the image is preprocessed, and Pulse Coupled Neural Network(PCNN) algorithm is used to filterthe image adaptively to make the image clearer. For the segmentation model, the SE module is used as the input of the residual network, and then its output is connected to the Unet model through bilinear interpolation to perform the down-sampling and up-sampling operations. The Lits and collected liver cancer images are combined into a new data set for training. The results show that this method has better segmentation performance and accuracy than the original Unet method, and the dice coefficient, mIou and other evaluation indicators have increased by at least 2.1%, which is a method that can be applied to cancer segmentation.

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“…Tutuncu and Buber 8 presented the segmented capillaroscopy images using Otsu Threshold segmentation, Fuzzy C-mean, and Region Growing methods. Liu et al 9 established an encoding-decoding network and deepened the layers in the network to preserve the features of the deep layer using a combination of ResNet and U-Net. Asghar et al 10 presented the manual and automated system to measure the capillary width and height.…”

Section: Introductionmentioning

confidence: 99%

Analysis of microvascular pattern in diabetes mellitus condition using the nailfold capillaroscopy images

Elumalai,

Krishnamoorthi,

Periyasamy

et al. 2024

Proc Inst Mech Eng H

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Diabetes is often considered a vascular disease due to its impact on blood vessels, it is a complex condition with various metabolic and autoimmune factors involved. One of the long term comorbidities of diabetes includes microvascular complications. The microvascular complications can be analyzed using the Nailfold capillaroscopy, a non-invasive technique that allows for the visualization and analysis of capillaries in the proximal nailfold area. Using advanced video capillaroscopy with high magnification, capillary images can be captured from and processed to analyze their morphology. The capillary images of normal group and diabetic group are acquired from 118 participants using nailfold capillaroscopy and the obtained images are preprocessed using image processing filters. The identification and segmentation of the capillaries are the challenges to be addressed in the processing of the images. Hence segmentation of capillaries is done using morphological operations, thresholding and convolutional neural networks. The performance of the filters and segmentation methods are evaluated using Mean Square Error (MSE), Peak signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Jaccard Index and Sorensen coefficient. By analyzing the morphological features namely the capillary diameter, density, distribution, presence of hemorrhage and the shape of the capillaries from both the groups, the capillary changes associated with diabetic condition were studied. It was found that the non diabetic participants considered in this study has capillary diameter in the range of 8–14 µm and the capillary density in the range of 10–30 capillaries per mm2 whereas the diabetic participants has capillary diameter greater than 30 µm and the capillary density is less than 10 capillaries per mm2. In addition to capillary density and diameter, the presence of hemorrhage, the orientation and distribution of the capillaries are also considered to differentiate the diabetic group from the non diabetic group. The classification of the participants are validated with the clinical history of the participants.

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Segmenting nailfold capillaries using an improved U-net network

Cited by 18 publications

References 15 publications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

Research on liver cancer segmentation method based on PCNN image processing and SE-ResUnet

Analysis of microvascular pattern in diabetes mellitus condition using the nailfold capillaroscopy images

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