CPFNet: Context Pyramid Fusion Network for Medical Image Segmentation

Feng, Shuanglang; Zhao, Heming; Shi, Fei; Cheng, Xuena; Wang, Meng; Ma, Yuhui; Xiang, Dehui; Zhu, Weifang; Chen, Xinjian

doi:10.1109/tmi.2020.2983721

Cited by 422 publications

(194 citation statements)

References 33 publications

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“…ey prevented the adverse effect of dimensionality reduction in SE-Net on channel attention. However, most U-shaped networks use only abstract features, neglect certain details, and cannot effectively use multiscale context information [20].…”

Section: Related Workmentioning

confidence: 99%

“…We propose a pyramid fusion module with multiscale global information interaction in the bottom encoder, which enhances the performance of the network by fusing the feature information of different scales and improving the performance of the network for lesion area segmentation. [20], which improved the segmentation performance by utilising two pyramid modules to fuse multiscale context information.…”

Section: Introductionmentioning

confidence: 99%

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Improved 3D U-Net for COVID-19 Chest CT Image Segmentation

Zheng

Dongye

2021

Scientific Programming

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Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide. The rapid and accurate automatic segmentation of COVID-19 infected areas using chest computed tomography (CT) scans is critical for assessing disease progression. However, infected areas have irregular sizes and shapes. Furthermore, there are large differences between image features. We propose a convolutional neural network, named 3D CU-Net, to automatically identify COVID-19 infected areas from 3D chest CT images by extracting rich features and fusing multiscale global information. 3D CU-Net is based on the architecture of 3D U-Net. We propose an attention mechanism for 3D CU-Net to achieve local cross-channel information interaction in an encoder to enhance different levels of the feature representation. At the end of the encoder, we design a pyramid fusion module with expanded convolutions to fuse multiscale context information from high-level features. The Tversky loss is used to resolve the problems of the irregular size and uneven distribution of lesions. Experimental results show that 3D CU-Net achieves excellent segmentation performance, with Dice similarity coefficients of 96.3% and 77.8% in the lung and COVID-19 infected areas, respectively. 3D CU-Net has high potential to be used for diagnosing COVID-19.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved 3D U-Net for COVID-19 Chest CT Image Segmentation

Zheng

Dongye

2021

Scientific Programming

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“…Second, the segmentation performance of four recently proposed mass segmentation methods 35–38 is reported to objectively evaluate the validity of the proposed DCANet. Finally, five state‐of‐the‐art medical image segmentation methods, which include U‐Net, 27 scSENet, 28 CE‐Net, 29 CPFNet, 30 and CS 2 ‐Net, 31 are also reproduced to have a further comparative analysis. Note that these five methods are reproduced from their original implementations, while the completely equal training conditions including loss function, data division approach, data augmentations, and hyper‐parameter settings are employed for fair comparisons.…”

Section: Experimental Designmentioning

confidence: 99%

DCANet: Dual contextual affinity network for mass segmentation in whole mammograms

Lou

Meng

et al. 2021

Medical Physics

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Purpose Breast mass segmentation in mammograms remains a crucial yet challenging topic in computer‐aided diagnosis systems. Existing algorithms mainly used mass‐centered patches to achieve mass segmentation, which is time‐consuming and unstable in clinical diagnosis. Therefore, we aim to directly perform fully automated mass segmentation in whole mammograms with deep learning solutions. Methods In this work, we propose a novel dual contextual affinity network (a.k.a., DCANet) for mass segmentation in whole mammograms. Based on the encoder–decoder structure, two lightweight yet effective contextual affinity modules including the global‐guided affinity module (GAM) and the local‐guided affinity module (LAM) are proposed. The former aggregates the features integrated by all positions and captures long‐range contextual dependencies, aiming to enhance the feature representations of homogeneous regions. The latter emphasizes semantic information around each position and exploits contextual affinity based on the local field‐of‐view, aiming to improve the indistinction among heterogeneous regions. Results The proposed DCANet is greatly demonstrated on two public mammographic databases including the DDSM and the INbreast, achieving the Dice similarity coefficient (DSC) of 85.95% and 84.65%, respectively. Both segmentation performance and computational efficiency outperform the current state‐of‐the‐art methods. Conclusion According to extensive qualitative and quantitative analyses, we believe that the proposed fully automated approach has sufficient robustness to provide fast and accurate diagnoses for possible clinical breast mass segmentation.

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“…Hasan et al (2020) presented automatic semantic segmentation method called dermoscopy skin network (DSNet) to minimize the parameters number of network (lightweight). Feng et al (2020) proposed Context Pyramid Fusion Network (named CPFNet) to solve context information extraction capability of a single stage that is insufficient in the deep learning, due to the problems such as imbalanced class and blurred boundary. Zafar et al (2020) adapted an automated technique for segmenting lesion boundaries that combines two architectures, the U-net and the ResNet, collectively called Res-Unet.…”

Section: Related Workmentioning

confidence: 99%

Skin Lesion Segmentation Using Local Binary Convolution-Deconvolution Architecture

Salih

Viriri

2020

Image Anal Stereol

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Deep learning techniques such as Deep Convolutional Networks have achieved great success in skin lesion segmentation towards melanoma detection. The performance is however restrained by distinctive and challenging features of skin lesions such as irregular and fuzzy border, noise and artefacts presence and low contrast between lesions. The methods are also restricted with scarcity of annotated lesion images training dataset and limited computing resources. Recent research in convolutional neural network (CNN) has provided a variety of new architectures for deep learning. One interesting new architecture is the local binary convolutional neural network (LBCNN), which can reduce the workload of CNNs and improve the classification accuracy. The proposed framework employs the local binary convolution on U-net architecture instead of the standard convolution in order to reduced-size deep convolutional encoder-decoder network that adopts loss function for robust segmentation. The proposed framework replaced the encoder part in U-net by LBCNN layers. The approach automatically learns and segments complex features of skin lesion images. The encoder stage learns the contextual information by extracting discriminative features while the decoder stage captures the lesion boundaries of the skin images. This addresses the issues with encoder-decoder network producing coarse segmented output with challenging skin lesions appearances such as low contrast between healthy and unhealthy tissues and fine grained variability. It also addresses issues with multi-size, multi-scale and multi-resolution skin lesion images. The deep convolutional network also adopts a reduced-size network with just five levels of encoding-decoding network. This reduces greatly the consumption of computational processing resources. The system was evaluated on publicly available dataset of ISIC and PH2. The proposed system outperforms most of the existing state-of-art.

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CPFNet: Context Pyramid Fusion Network for Medical Image Segmentation

Cited by 422 publications

References 33 publications

Improved 3D U-Net for COVID-19 Chest CT Image Segmentation

Improved 3D U-Net for COVID-19 Chest CT Image Segmentation

DCANet: Dual contextual affinity network for mass segmentation in whole mammograms

Skin Lesion Segmentation Using Local Binary Convolution-Deconvolution Architecture

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