CA‐Unet++: An improved structure for medical CT scanning based on the Unet++ Architecture

Li, Bo; Wu, Fei; Liu, Sikai; Tang, Jinghong; Li, GuangHui; Zhong, Meiling; Guan, Xiaohui

doi:10.1002/int.22969

Cited by 17 publications

(5 citation statements)

References 33 publications

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“…The UNet model was proposed by Ronneberger et al in 2015. The network uses the concept of skip connections for image segmentation [12][13][14] and has been widely exploited in the field of medical image segmentation. As depicted in figure 3, UNet [15][16][17][18][19] is an encoder-decoder network.…”

Section: Network Structurementioning

confidence: 99%

Reading recognition of pointer meters based on an improved UNet++ network

Huo,

Bai,

Sun

et al. 2023

Meas. Sci. Technol.

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Pointer meters are widely used in modern industries, such as petrochemical applications, substations, and nuclear power plants. To overcome the reading errors and inaccurate measurements due to uneven or fluctuating illumination in practical applications, this paper proposes an improved UNet++ network for recognizing pointer meter readings. First, the scale invariant feature transform feature-matching algorithm is used to adjust the captured tilted meter images to a symmetrical and upright shape. Then, the UNet++ network is used to segment the scale and pointer regions in the dashboard to eliminate background interference. Furthermore, part of the convolution in the UNet++ network is replaced with dilated convolution with different expansion rates to expand the perceptual field during network training. In the UNet++ network jump connection, the attention mechanism module is also introduced in the path to enhance the region’s features to be segmented and suppress the parts of the non-segmented area. A hybrid loss function is used for the network model training to prevent the imbalance of the segmented region share. Finally, the distance method is used to read the gauge representation. Experiments were conducted to compare the performance of the proposed method with that of the original UNet++ network in terms of feasibility and precision. The experimental results showed that the recognition reading accuracy was significantly improved by the enhanced network, with the accuracy, sensitivity, and specificity reaching 98.65%, 84.33%, and 99.38%, respectively. Furthermore, when using the improved UNet++ network for numerical reading, the average relative error was only 0.122%, indicating its robustness in a natural environment.

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Section: Network Structurementioning

confidence: 99%

Reading recognition of pointer meters based on an improved UNet++ network

Huo,

Bai,

Sun

et al. 2023

Meas. Sci. Technol.

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“…U-Net++ 7 is based on nested and dense skip connections and UNet 3+ 26 is the further developed version with deep supervision in each level of the decoder path. Li et al 29 upgraded UNet++ architecture by applying a channel attention mechanism to long-hop connections. The implementation of channel attention could reduce the eigenvalue loss.…”

Section: Related Workmentioning

confidence: 99%

MANet: a multi-attention network for automatic liver tumor segmentation in computed tomography (CT) imaging

Hettihewa,

Kobchaisawat,

Tanpowpong

et al. 2023

Sci Rep

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Automatic liver tumor segmentation is a paramount important application for liver tumor diagnosis and treatment planning. However, it has become a highly challenging task due to the heterogeneity of the tumor shape and intensity variation. Automatic liver tumor segmentation is capable to establish the diagnostic standard to provide relevant radiological information to all levels of expertise. Recently, deep convolutional neural networks have demonstrated superiority in feature extraction and learning in medical image segmentation. However, multi-layer dense feature stacks make the model quite inconsistent in imitating visual attention and awareness of radiological expertise for tumor recognition and segmentation task. To bridge that visual attention capability, attention mechanisms have developed for better feature selection. In this paper, we propose a novel network named Multi Attention Network (MANet) as a fusion of attention mechanisms to learn highlighting important features while suppressing irrelevant features for the tumor segmentation task. The proposed deep learning network has followed U-Net as the basic architecture. Moreover, residual mechanism is implemented in the encoder. Convolutional block attention module has split into channel attention and spatial attention modules to implement in encoder and decoder of the proposed architecture. The attention mechanism in Attention U-Net is integrated to extract low-level features to combine with high-level ones. The developed deep learning architecture is trained and evaluated on the publicly available MICCAI 2017 Liver Tumor Segmentation dataset and 3DIRCADb dataset under various evaluation metrics. MANet demonstrated promising results compared to state-of-the-art methods with comparatively small parameter overhead.

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“…In the feld of medical image segmentation based on deep learning [8][9][10], the encoderdecoder structure is one of the most commonly used network structures [11][12][13]. UNet [7] and SegNet [14] are two representatives of the encoder-decoder structure-based methods.…”

Section: Encoder-decoder Structurementioning

confidence: 99%

An Edge-Assisted Computing and Mask Attention Based Network for Lung Region Segmentation

Wang

Zhong

Huang

et al. 2023

International Journal of Intelligent Systems

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Recent years have witnessed the success of encoder-decoder structure-based approaches in lung region segmentation of chest X-ray (CXR) images. However, accurate lung region segmentation is still challenging due to the following three issues: (1) inaccurate lung region segmentation boundaries, (2) existence of lesion-related artifacts (e.g., opacity and pneumonia), and (3) lack of the ability to utilize multiscale information. To address these issues, we propose an edge-assisted computing and mask attention based network (called EAM-Net), which consists of an encoder-decoder network, an edge-assisted computing module, and multiple mask attention modules. Based on the encoder-decoder structure, an edge-assisted computing module is first proposed, which integrates the feature maps of the shallow encoding layers for edge prediction, and uses the edge evidence map as a strong cue to guide the lung region segmentation, thereby refining the lung region segmentation boundaries. We further design a mask attention module after each decoding layer, which employs a mask attention operation to make the model focus on lung regions while suppressing the lesion-related artifacts. Besides, a multiscale aggregation loss is proposed to optimize EAM-Net. Extensive experiments on the JSRT, Shenzhen, and Montgomery datasets demonstrate that EAM-Net outperforms existing state-of-the-art lung region segmentation methods.

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CA‐Unet++: An improved structure for medical CT scanning based on the Unet++ Architecture

Cited by 17 publications

References 33 publications

Reading recognition of pointer meters based on an improved UNet++ network

Reading recognition of pointer meters based on an improved UNet++ network

MANet: a multi-attention network for automatic liver tumor segmentation in computed tomography (CT) imaging

An Edge-Assisted Computing and Mask Attention Based Network for Lung Region Segmentation

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