Multi-Attention Mechanism Medical Image Segmentation Combined with Word Embedding Technology

Cheng, Junlong; Tian, Shengwei; Yu, Long; You, Hongfeng

doi:10.3103/s0146411620060024

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…The existing image segmentation technology has low robustness when processing medical images and has poor matching ability for soft tissues (such as internal organs) with inconspicuous grayscale intervals and hard tissues (such as teeth) with small structural gaps. Edge prediction is also unsatisfactory [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Image Segmentation Technology Based on Attention Mechanism and ENet

Ling

Hou

Gong

2022

Computational Intelligence and Neuroscience

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With the development of today’s society, medical technology is becoming more and more important in people’s daily diagnosis and treatment and the number of computed tomography (CT) images and MRI images is also increasing. It is difficult to meet today’s needs for segmentation and recognition of medical images by manpower alone. Therefore, the use of computer technology for automatic segmentation has received extensive attention from researchers. We design a tooth CT image segmentation method combining attention mechanism and ENet. First, dilated convolution is used with the spatial information path, with a small downsampling factor to preserve the resolution of the image. Second, an attention mechanism is added to the segmentation network based on CT image features to improve the accuracy of segmentation. Then, the designed feature fusion module obtains the segmentation result of the tooth CT image. It was verified on tooth CT image dataset published by West China Hospital, and the average intersection ratio and accuracy were used as the metric. The results show that, on the dataset of West China Hospital, Mean Intersection over Union (MIOU) and accuracy are 83.47% and 95.28%, respectively, which are 3.3% and 8.09% higher than the traditional model. Compared with the multiple watershed algorithm, the Chan–Vese segmentation algorithm, and the graph cut segmentation algorithm, our algorithm increases the calculation time by 56.52%, 91.52%, and 62.96%, respectively. It can be seen that our algorithm has obvious advantages in MIOU, accuracy, and calculation time.

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Section: Introductionmentioning

confidence: 99%

Image Segmentation Technology Based on Attention Mechanism and ENet

Ling

Hou

Gong

2022

Computational Intelligence and Neuroscience

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“…With the rapid development of deep learning, convolutional neural networks (CNNs) research on gliomas has achieved remarkable results by extracting a large number of deep features from medical images and training prediction models (Ribalta Lorenzo et al 2019, Cheng et al 2020. For gliomas grading/classification, Pan et al (2015) proposed a classification scheme based on the simple CNN structure.…”

Section: Introductionmentioning

confidence: 99%

A lightweight 3D UNet model for glioma grading

Yu¹,

Wu²,

Han³

et al. 2022

Phys. Med. Biol.

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Objective. Glioma is one of the most fatal cancers in the world which has been divided into Low Grade Glioma (LGG) and High Grade Glioma (HGG), and its image grading has become a hot topic of contemporary research. Magnetic Resonance Imaging (MRI) is a vital diagnostic tool for brain tumor detection, analysis, and surgical planning. Accurate and automatic glioma grading is crucial for speeding up diagnosis and treatment planning. Aiming at the problems of 1) large number of parameters, 2) complex calculation, and 3) poor speed of the current glioma grading algorithms based on deep learning, this paper proposes a lightweight 3D UNet deep learning framework, which can improve classification accuracy in comparison with the existing methods. Approach. To improve efficiency while maintaining accuracy, existing 3D UNet has been excluded, and depthwise separable convolution has been applied to 3D convolution to reduce the number of network parameters. The weight of parameters on the basis of space and channel compression & excitation module has been strengthened to improve the model in the feature map, reduce the weight of redundant parameters, and strengthen the performance of the model. Main results. A total of 560 patients with glioma were retrospectively reviewed. All patients underwent MRI before surgery. The experiments were carried out on T1w, T2w, FLAIR, and CET1w images. Additionally, a way of marking tumor area by cube bounding box is presented which has no significant difference in model performance with the manually drawn ground truth. Evaluated on test data sets using the proposed model has shown good results (with accuracy of 89.29%). Significance. This work serves to achieve LGG/HGG grading by simple, effective, and non-invasive diagnostic approaches to provide diagnostic suggestions for clinical usage, thereby facilitating hasten treatment decisions.

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Research on image segmentation methods based on optimization theory

Lihua

2023

Int J Adv Manuf Technol

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Multi-Attention Mechanism Medical Image Segmentation Combined with Word Embedding Technology

Cited by 6 publications

References 18 publications

Image Segmentation Technology Based on Attention Mechanism and ENet

Image Segmentation Technology Based on Attention Mechanism and ENet

A lightweight 3D UNet model for glioma grading

Research on image segmentation methods based on optimization theory

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