Match Feature U-Net: Dynamic Receptive Field Networks for Biomedical Image Segmentation

Qin, Xiaofei; Wu, Chunjin; Chang, Hang; Lu, Hao; Zhang, Xuedian

doi:10.3390/sym12081230

Cited by 19 publications

(13 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [56], a Match Feature U-Net used in the field of medical image dynamic reception is proposed to perform cell segmentation. Like [44,46,[51][52][53][54], Match Feature U-Net improves the ability to segment-specific or public datasets by changing basic convolution units.…”

Section: Other Convolutionmentioning

confidence: 99%

A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification

Wu¹,

Liu²,

Li³

et al. 2022

Preprint

View full text Add to dashboard Cite

Image analysis technology is used to solve the inadvertences of artificial traditional methods in disease, wastewater treatment, environmental change monitoring analysis and convolutional neural networks (CNN) play an important role in microscopic image analysis. An important step in detection, tracking, monitoring, feature extraction, modeling and analysis is image segmentation, in which U-Net has increasingly applied in microscopic image segmentation. This paper comprehensively reviews the development history of U-Net, and analyzes various research results of various segmentation methods since the emergence of U-Net and conducts a comprehensive review of related papers. First, This paper has summarizes the improved methods of U-Net and then listed the existing significances of image segmentation techniques and their improvements that has introduced over the years. Finally, focusing on the different improvement strategies of U-Net in different papers, the related work of each application target is reviewed according to detailed technical categories to facilitate future research. Researchers can clearly see the dynamics of transmission of technological development and keep up with future trends in this interdisciplinary field.

show abstract

Section: Other Convolutionmentioning

confidence: 99%

A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification

Wu¹,

Liu²,

Li³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Through multiple convolutions and pooling operations, the feature dimensions are improved and the images are compressed (Kando et al, 2019;Esch et al, 2020). In the process of image restoration, the U-Net convolutional neural network also convolved the fused images for many times (Qin et al, 2020), which enhanced the detailed information of images and achieved the better segmentation effect.…”

Section: Extracting the Edge Of Interferometric Fringesmentioning

confidence: 99%

A Novel Phase Unwrapping Method Used for Monitoring the Land Subsidence in Coal Mining Area Based on U-Net Convolutional Neural Network

Wang

Lü

et al. 2021

Front. Earth Sci.

View full text Add to dashboard Cite

Large-scale and high-intensity mining underground coal has resulted in serious land subsidence. It has caused a lot of ecological environment problems and has a serious impact on the sustainable development of economy. Land subsidence cannot be accurately monitored by InSAR (interferometric synthetic aperture radar) due to the low coherence in the mining area, excessive deformation gradient, and the atmospheric effect. In order to solve this problem, a novel phase unwrapping method based on U-Net convolutional neural network was constructed. Firstly, the U-Net convolutional neural network is used to extract edge to automatically obtain the boundary information of the interferometric fringes in the region of subsidence basin. Secondly, an edge-linking algorithm is constructed based on edge growth and predictive search. The interrupted interferometric fringes are connected automatically. The whole and continuous edges of interferometric fringes are obtained. Finally, the correct phase unwrapping results are obtained according to the principle of phase unwrapping and the wrap-count (integer jump of 2π) at each pixel by edge detection. The Huaibei Coalfield in China was taken as the study area. The real interferograms from D-InSAR (differential interferometric synthetic aperture radar) processing used Sentinel-1A data which were used to verify the performance of the new method. Subsidence basins with clear interferometric fringes, interrupted interferometric fringes, and confused interferometric fringes are selected for experiments. The results were compared with the other methods, such as MCF (minimum cost flow) method. The tests showed that the new method based on U-Net convolutional neural network can resolve the problem that is difficult to obtain the correct unwrapping phase due to interrupted or partially confused interferometric fringes caused by low coherence or other reasons in the coal mining area. Hence, the new method can help to accurately monitor the subsidence in mining areas under different conditions using InSAR technology.

show abstract

“…[26][27][28] A fully convolutional neural network (U-Net) architecture is one of the most commonly used deep learning architectures used for medical image segmentation. [29][30][31][32][33] U-Net has also been shown to improve the performance of medical image segmentation by concatenating feature maps in the upsampling path with the corresponding cropped feature map in the downsampling path via skip connection. 34 Convolutional neural networks (CNNs) are the backbones of the U-net architectures, and they have been shown to achieve performances close to human experts in the analysis of medical images.…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Network Based Automatic Segmentation of Corneal Subbasal Nerves on In Vivo Confocal Microscopy Images

Yıldız

Arslan

Taş

et al. 2021

Trans. Vis. Sci. Tech.

View full text Add to dashboard Cite

In vivo confocal microscopy (IVCM) is a noninvasive, reproducible, and inexpensive diagnostic tool for corneal diseases. However, widespread and effortless image acquisition in IVCM creates serious image analysis workloads on ophthalmologists, and neural networks could solve this problem quickly. We have produced a novel deep learning algorithm based on generative adversarial networks (GANs), and we compare its accuracy for automatic segmentation of subbasal nerves in IVCM images with a fully convolutional neural network (U-Net) based method. Methods:We have collected IVCM images from 85 subjects. U-Net and GAN-based image segmentation methods were trained and tested under the supervision of three clinicians for the segmentation of corneal subbasal nerves. Nerve segmentation results for GAN and U-Net-based methods were compared with the clinicians by using Pearson's R correlation, Bland-Altman analysis, and receiver operating characteristics (ROC) statistics. Additionally, different noises were applied on IVCM images to evaluate the performances of the algorithms with noises of biomedical imaging. Results:The GAN-based algorithm demonstrated similar correlation and Bland-Altman analysis results with U-Net. The GAN-based method showed significantly higher accuracy compared to U-Net in ROC curves. Additionally, the performance of the U-Net deteriorated significantly with different noises, especially in speckle noise, compared to GAN. Conclusions:This study is the first application of GAN-based algorithms on IVCM images. The GAN-based algorithms demonstrated higher accuracy than U-Net for automatic corneal nerve segmentation in IVCM images, in patient-acquired images and noise applied images. This GAN-based segmentation method can be used as a facilitating diagnostic tool in ophthalmology clinics.Translational Relevance: Generative adversarial networks are emerging deep learning models for medical image processing, which could be important clinical tools for rapid segmentation and analysis of corneal subbasal nerves in IVCM images.

show abstract

Match Feature U-Net: Dynamic Receptive Field Networks for Biomedical Image Segmentation

Cited by 19 publications

References 30 publications

A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification

A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification

A Novel Phase Unwrapping Method Used for Monitoring the Land Subsidence in Coal Mining Area Based on U-Net Convolutional Neural Network

Generative Adversarial Network Based Automatic Segmentation of Corneal Subbasal Nerves on In Vivo Confocal Microscopy Images

Contact Info

Product

Resources

About