An FA-SegNet Image Segmentation Model Based on Fuzzy Attention and Its Application in Cardiac MRI Segmentation

Yang, Ruiping; Yu, Jiguo; Yin, Jian; Liu, Kun; Xu, Shengli

doi:10.1007/s44196-022-00080-x

Cited by 11 publications

(4 citation statements)

References 26 publications

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“…Furthermore, the proposed model is able to segment LV with a DSC that is 1.295% higher than that of the Yang et. al [ 13 ] work. Also, it is able to segment RV with a DSC that is 4.065% higher than that of the Yang et.…”

Section: Discussionmentioning

confidence: 99%

“…Also, it is able to segment RV with a DSC that is 4.065% higher than that of the Yang et. al [ 13 ] model. Furthermore, the improvement in segmentation Myo is 4.36% in DSC compared to Yang et.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, the improvement in segmentation Myo is 4.36% in DSC compared to Yang et. al [ 13 ] model. Moreover, the CAT-Seg outperforms Silva et al [ 32 ]’s model in segmenting the three substructures in both ED and ES phases.…”

Section: Discussionmentioning

confidence: 99%

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CAT-Seg: cascaded medical assistive tool integrating residual attention mechanisms and Squeeze-Net for 3D MRI biventricular segmentation

Shoieb,

Fathalla,

Youssef

et al. 2023

Phys Eng Sci Med

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Cardiac image segmentation is a critical step in the early detection of cardiovascular disease. The segmentation of the biventricular is a prerequisite for evaluating cardiac function in cardiac magnetic resonance imaging (CMRI). In this paper, a cascaded model CAT-Seg is proposed for segmentation of 3D-CMRI volumes. CAT-Seg addresses the problem of biventricular confusion with other regions and localized the region of interest (ROI) to reduce the scope of processing. A modified DeepLabv3+ variant integrating SqueezeNet (SqueezeDeepLabv3+) is proposed as a part of CAT-Seg. SqueezeDeepLabv3+ handles the different shapes of the biventricular through the different cardiac phases, as the biventricular only accounts for small portion of the volume slices. Also, CAT-Seg presents a segmentation approach that integrates attention mechanisms into 3D Residual UNet architecture (3D-ResUNet) called 3D-ARU to improve the segmentation results of the three major structures (left ventricle (LV), Myocardium (Myo), and right ventricle (RV)). The integration of the spatial attention mechanism into ResUNet handles the fuzzy edges of the three structures. The proposed model achieves promising results in training and testing with the Automatic Cardiac Diagnosis Challenge (ACDC 2017) dataset and the external validation using MyoPs. CAT-Seg demonstrates competitive performance with state-of-the-art models. On ACDC 2017, CAT-Seg is able to segment LV, Myo, and RV with an average minimum dice symmetry coefficient (DSC) performance gap of 1.165%, 4.36%, and 3.115% respectively. The average maximum improvement in terms of DSC in segmenting LV, Myo and RV is 4.395%, 6.84% and 7.315% respectively. On MyoPs external validation, CAT-Seg outperformed the state-of-the-art in segmenting LV, Myo, and RV with an average minimum performance gap of 6.13%, 5.44%, and 2.912% respectively.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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CAT-Seg: cascaded medical assistive tool integrating residual attention mechanisms and Squeeze-Net for 3D MRI biventricular segmentation

Shoieb,

Fathalla,

Youssef

et al. 2023

Phys Eng Sci Med

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“…The above shortcomings have inspired scholars to explore the possibilities of utilizing fuzzy theory. Yang [40] designed a deep convolutional neural network incorporating a fuzzy attention mechanism, effectively improving medical image segmentation accuracy. Nan [41] proposed an airway segmentation method, which enhances the continuity of the segmentation utilizing a fuzzy attention neural network and a combined loss function.…”

Section: ⅰ Introductionmentioning

confidence: 99%

Fuzzy EfficientDet: An Approach for Precise Detection of Larch Infestation Severity in UAV Imagery Under Dynamic Environmental Conditions

Yang,

Li,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this paper, a novel deep learning framework, fuzzy EfficientDet, is proposed to address the challenge of accurately detecting larch infested by Coleophora laricella pests in UAV imagery, where the key innovation is the incorporation of the fuzzy spatial attention mechanism (FSAM), which can effectively deal with the problem of model uncertainty due to the complexity of environmental transformations and image features. First, this study designs and implements a Global-Local Squeeze-and-Excitation Module, which profoundly integrates global and local feature information, realizes the dynamic adaptation of the importance of feature channels in the EfficientNet, and thus improves the overall feature expression efficiency of the network. Second, this study constructed a dense Bi-FPN architecture, which adds a dense connection structure to the original Bi-FPN to enhance the modeling accuracy for small targets and long-range spatial dependencies. Finally, this study develops the fuzzy spatial attention mechanism (FSAM), which can effectively mitigate the unstable performance of the EfficientDet in the face of image feature fluctuations triggered by changes in lighting conditions and seasonal effects. Experiments demonstrate that the proposed fuzzy EfficientDet model shows superior performance compared to the traditional SSD, Faster R-CNN, YOLO V5, and the unimproved EfficientDet target detection method on the Swedish Forest Agency (2021) dataset, with its mAP as high as 94.29%. This result demonstrates that fuzzy EfficientDet provides an efficient and reliable solution when dealing with the task of target detection in UAV images, especially in dealing with environmental uncertainty and complex feature extraction.

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An Intelligent Day Ahead Solar Plant’s Power Forecasting System

Engel,

Engel

2023

Studies in Computational Intelligence

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An FA-SegNet Image Segmentation Model Based on Fuzzy Attention and Its Application in Cardiac MRI Segmentation

Cited by 11 publications

References 26 publications

CAT-Seg: cascaded medical assistive tool integrating residual attention mechanisms and Squeeze-Net for 3D MRI biventricular segmentation

CAT-Seg: cascaded medical assistive tool integrating residual attention mechanisms and Squeeze-Net for 3D MRI biventricular segmentation

Fuzzy EfficientDet: An Approach for Precise Detection of Larch Infestation Severity in UAV Imagery Under Dynamic Environmental Conditions

An Intelligent Day Ahead Solar Plant’s Power Forecasting System

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