2D medical image segmentation via learning multi-scale contextual dependencies

Pang, Shuchao; Du, Anan; Yu, Zhenmei; Orgun, Mehmet A.

doi:10.1016/j.ymeth.2021.05.015

Cited by 6 publications

(3 citation statements)

References 44 publications

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“…The neural network takes in an entire image as input and learns semantic information through the convolutional kernel, which consists of both relevant and redundant features (Pang et al, 2021). However, redundant information can negatively impact the performance of network in the given task.…”

Section: Attention Mechanismmentioning

confidence: 99%

“…The selfattention mechanism explores correlations within an image to effectively preserve the structure of vessel branches and the vessel tree. Several studies have demonstrated this, including Pang et al (2021), who employed the self-attention mechanism to adjust multi-scale features and facilitate the restoration of microvessel structures during the decoding process. Similarly, Shen et al (2022) substituted the convolutional layer with the selfattention mechanism in UNet, leading to outstanding segmentation results and enhanced feature extraction efficiency.…”

Section: Attention Mechanismmentioning

confidence: 99%

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MFA-UNet: a vessel segmentation method based on multi-scale feature fusion and attention module

Cao,

Chen,

et al. 2023

Front. Neurosci.

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IntroductionThe accurate segmentation of retinal vessels is of utmost importance in the diagnosis of retinal diseases. However, the complex vessel structure often leads to poor segmentation performance, particularly in the case of microvessels.MethodsTo address this issue, we propose a vessel segmentation method composed of preprocessing and a multi-scale feature attention network (MFA-UNet). The preprocessing stage involves the application of gamma correction and contrast-limited adaptive histogram equalization to enhance image intensity and vessel contrast. The MFA-UNet incorporates the Multi-scale Fusion Self-Attention Module(MSAM) that adjusts multi-scale features and establishes global dependencies, enabling the network to better preserve microvascular structures. Furthermore, the multi-branch decoding module based on deep supervision (MBDM) replaces the original output layer to achieve targeted segmentation of macrovessels and microvessels. Additionally, a parallel attention mechanism is embedded into the decoder to better exploit multi-scale features in skip paths.ResultsThe proposed MFA-UNet yields competitive performance, with dice scores of 82.79/83.51/84.17/78.60/81.75/84.04 and accuracies of 95.71/96.4/96.71/96.81/96.32/97.10 on the DRIVE, STARE, CHASEDB1, HRF, IOSTAR and FIVES datasets, respectively.DiscussionIt is expected to provide reliable segmentation results in clinical diagnosis.

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Section: Attention Mechanismmentioning

confidence: 99%

Section: Attention Mechanismmentioning

confidence: 99%

MFA-UNet: a vessel segmentation method based on multi-scale feature fusion and attention module

Cao,

Chen,

et al. 2023

Front. Neurosci.

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“…The sample of small regions of interest is improved (especially in deep layers) and clear global contextual relationships in multi-scale feature spaces are learned. With high percentage sensitivity, the suggested framework provides region performance on the retinal vascular detection job, COVID-19 lung infection segmentation task, and liver tumor segmentation task [68]. Biological cells and organisms depend heavily on cytoskeletal filaments because of their adaptability and the vital roles they play.…”

Section: Biochemistry Genetics and Molecularmentioning

confidence: 99%

Image Segmentation Using Hybrid Optimization Algorithms: Review

Hemeida¹,

Yahya

Al-Sanary

2022

Aswan University Journal of Sciences and Technology

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Image segmentation, often based on the properties of image pixels, is a widely used method in digital image processing and analysis to divide an image into multiple parts or areas. The main goal of image segmentation is to simplify the image for easier analysis. It would be very difficult to implement computer vision without performing image segmentation. An important component of computer vision is image segmentation, which has numerous commercial applications. Google and others Image-based search engines use image segmentation algorithms to identify the objects in your image and match their results to the relevant images they find to present you with search results.

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