Multiscale receptive field based on residual network for pancreas segmentation in CT images

Li, Feiyan; Li, Weisheng; Shu, Yucheng; Qin, Shengfeng; Xiao, Bin; Zhan, Ziwei

doi:10.1016/j.bspc.2019.101828

Cited by 32 publications

(5 citation statements)

References 15 publications

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“…We use the Dice similarity coefficient and Jaccard coefficient to evaluate the similarity between prediction results and ground truth. The value range of Dice and Jaccard is [0, 1], and the closer the value is to 1, the better the segmentation result 34,35 . The calculation formulas of Dice and Jaccard are (4) and (5):

D S C = \frac{2 ((||, Y \cap \overset{Y}{true}))}{()(, (||, Y) \cup (||, \hat{Y}))},

J a c c a r d = \frac{(||, Y \cap \overset{Y}{true})}{(||, Y \cup \overset{Y}{true})},

where Y represents the set of pixels of each slice in the ground truth and

\overset{Y}{true}

represents the set of pixels of each slice of the segmentation result of the MAD‐UNet model.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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MAD‐UNet: A deep U‐shaped network combined with an attention mechanism for pancreas segmentation in CT images

Qin

et al. 2020

Medical Physics

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Purpose Pancreas segmentation is a difficult task because of the high intrapatient variability in the shape, size, and location of the organ, as well as the low contrast and small footprint of the CT scan. At present, the U‐Net model is likely to lead to the problems of intraclass inconsistency and interclass indistinction in pancreas segmentation. To solve this problem, we improved the contextual and semantic feature information acquisition method of the biomedical image segmentation model (U‐Net) based on a convolutional network and proposed an improved segmentation model called the multiscale attention dense residual U‐shaped network (MAD‐UNet). Methods There are two aspects considered in this method. First, we adopted dense residual blocks and weighted binary cross‐entropy to enhance the semantic features to learn the details of the pancreas. Using such an approach can reduce the effects of intraclass inconsistency. Second, we used an attention mechanism and multiscale convolution to enrich the contextual information and suppress learning in unrelated areas. We let the model be more sensitive to pancreatic marginal information and reduced the impact of interclass indistinction. Results We evaluated our model using fourfold cross‐validation on 82 abdominal enhanced three‐dimensional (3D) CT scans from the National Institutes of Health (NIH‐82) and 281 3D CT scans from the 2018 MICCAI segmentation decathlon challenge (MSD). The experimental results showed that our method achieved state‐of‐the‐art performance on the two pancreatic datasets. The mean Dice coefficients were 86.10% ± 3.52% and 88.50% ± 3.70%. Conclusions Our model can effectively solve the problems of intraclass inconsistency and interclass indistinction in the segmentation of the pancreas, and it has value in clinical application. Code is available at https://github.com/Mrqins/pancreas-segmentation.

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D S C = \frac{2 ((||, Y \cap \overset{Y}{true}))}{()(, (||, Y) \cup (||, \hat{Y}))},

J a c c a r d = \frac{(||, Y \cap \overset{Y}{true})}{(||, Y \cup \overset{Y}{true})},

where Y represents the set of pixels of each slice in the ground truth and

\overset{Y}{true}

represents the set of pixels of each slice of the segmentation result of the MAD‐UNet model.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The value range of Dice and Jaccard is [0, 1], and the closer the value is to 1, the better the segmentation result. 34,35 The calculation formulas of Dice and Jaccard are (4) and (5):…”

Section: B Quantitative Assessment Metricsmentioning

confidence: 99%

MAD‐UNet: A deep U‐shaped network combined with an attention mechanism for pancreas segmentation in CT images

Qin

et al. 2020

Medical Physics

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“…Mathematical methods of soft tissue modeling can simulate the surface and partial internal features of soft tissues, enabling the model to perform specific deformations for realistic simulation, thus facilitating the application of tactile perception in medical robotics operations on human organs [14]. Medical segmentation of images such as CT and MRI allows for clearer identification and quantification of various lesions [15], for instance, in breast tumor segmentation [16], lung segmentation [17], pancreas segmentation [18], kidney stone segmentation [19], spine segmentation [20], and liver segmentation [21]. Furthermore, image segmentation technology is crucial for surgical planning and intraoperative navigation [22].…”

Section: Introductionmentioning

confidence: 99%

Multi-Convolutional Channel Residual Spatial Attention U-Net for Industrial and Medical Image Segmentation

Chen,

Kim

2024

IEEE Access

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Image segmentation has demonstrated immense potential in computer vision. In particular, the U-Net architecture, built on fully convolutional networks, is highly suitable for image segmentation tasks. Its encoder-decoder structure effectively captures both local and global features. This approach has achieved remarkable outcomes across various sectors, most notably in medical diagnostics and industrial quality control. However, U-Net, by employing skip connections, fuses different low-level and high-level convolutional features between the encoder and decoder, limiting its ability to effectively integrate useful features and harness contextual information. To address these feature disparities between the encoder and decoder, this paper introduces a novel network structure named Multi-Convolutional Channel Residual Spatial Attention U-Net (MCRSAU-Net). Designed for industrial and medical image segmentation, this model is anchored on the U-Net architecture. It replaces the traditional skip connections with channel attention residual paths featuring multiple convolutions, retaining more low-level features. Moreover, spatial attention module is incorporated in the decoding path to ensure the model concentrates on crucial regions of the input space, enhancing its segmentation capability across varied tasks. The proposed method was subjected to 5-fold cross-validation and testing on three public datasets: Mvtec AD, CHASE DB1, and Kvasir SEG. MCRSAU-Net achieved average Dice coefficients of 0.7755, 0.7651, and 0.8958 for defect segmentation of bottles, woods, and tiles, respectively, with average accuracies reaching 0.9751, 0.9815, and 0.9841. For retinal blood vessel and colon polyp segmentation, it exhibited superior performance, achieving average Dice scores of 0.8540 and 0.7053, and average accuracies of 0.9465 and 0.9195, respectively. These results not only underscore MCRSAU-Net's strong performance in image segmentation tasks but also demonstrate its significant potential in addressing specific challenges encountered in industrial and medical image segmentation.

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“…In this paper, the multi-scale strategy and Depthwise Separable Convolution (DSConv) mechanism are integrated into [23][24][25]. First, the DSConv is adopted as the basic component to construct the encoder-decoder, rather than the standard convolution.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, the multi‐scale strategy and Depthwise Separable Convolution (DSConv) mechanism are integrated into the encoder–decoder structure, and how to develop an attention mechanism that is beneficial to segmentation accuracy based on the composite mechanism is explored. Therefore, based on various mechanisms such as multi‐scale strategies, atrous convolutions, channel attentions, and DSConv, two composite attention modules [Dual‐path Multi‐scale Attention Fusion Module (DMAF) and Multi‐scale Normalized Channel Attention Module (MNCA)] are proposed, and the Dual‐Path Multi‐Scale Attention‐Guided Network (DMAGNet) is further proposed [23–25]. First, the DSConv is adopted as the basic component to construct the encoder–decoder, rather than the standard convolution.…”

Section: Introductionmentioning

confidence: 99%

DMAGNet: Dual‐path multi‐scale attention guided network for medical image segmentation

Ji,

Wang,

Ding

et al. 2023

IET Image Processing

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In recent years, convolutional neural networks (CNN)‐based automatic segmentation of medical images has become one of the hot topics in clinical disease diagnosis. It is still a challenging task to improve the segmentation accuracy of the network model with the large variation of pathological regions in different patients and the fuzzy boundary of pathological regions. A Dual‐path Multi‐scale Attention Guided network (DMAGNet) for medical image segmentation is proposed in this paper. First, the Dual‐path Multi‐scale Attention Fusion Module (DMAF) is proposed as a novel skip connection strategy, which is applied to encode semantic dependencies between high‐level and low‐level channels. Second, the Multi‐scale Normalized Channel Attention Module (MNCA) based on the atrous convolution, normalization channel attention mechanism, and the Depthwise Separable Convolutions (DSConv) is developed to strengthen dependencies between channels. Finally, the encoder–decoder backbone employs the DSConv, as well as the pretrained Resnet34 block is combined in the encoder part to further improve the backbone network performance. Comprehensive experiments on brain, lung, and liver segmentation tasks show that the proposed DMAGNet outperforms the original U‐Net method and other advanced methods.

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Multiscale receptive field based on residual network for pancreas segmentation in CT images

Cited by 32 publications

References 15 publications

MAD‐UNet: A deep U‐shaped network combined with an attention mechanism for pancreas segmentation in CT images

MAD‐UNet: A deep U‐shaped network combined with an attention mechanism for pancreas segmentation in CT images

Multi-Convolutional Channel Residual Spatial Attention U-Net for Industrial and Medical Image Segmentation

DMAGNet: Dual‐path multi‐scale attention guided network for medical image segmentation

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