Combining edge guidance and feature pyramid for medical image segmentation

Chen, Shaolong; Qiu, Changzhen; Yang, Weiping; Zhang, Zhiyong

doi:10.1016/j.bspc.2022.103960

Cited by 21 publications

(10 citation statements)

References 21 publications

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“…An edge guidance feature pyramid network (EGFPNet). EGFPNet [ 36 ] is a network proposed by the authors, which mainly introduces edge information to improve the quality of edge segmentation. In this study, we used EGFPNet as the base network because the quality of edge segmentation is important for improving the efficiency of semi-automatic annotation, and EGFPNet can improve the performance of tissue edge segmentation.…”

Section: Methodsmentioning

confidence: 99%

A Semi-Automatic Magnetic Resonance Imaging Annotation Algorithm Based on Semi-Weakly Supervised Learning

Chen,

Zhang

2024

Sensors

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The annotation of magnetic resonance imaging (MRI) images plays an important role in deep learning-based MRI segmentation tasks. Semi-automatic annotation algorithms are helpful for improving the efficiency and reducing the difficulty of MRI image annotation. However, the existing semi-automatic annotation algorithms based on deep learning have poor pre-annotation performance in the case of insufficient segmentation labels. In this paper, we propose a semi-automatic MRI annotation algorithm based on semi-weakly supervised learning. In order to achieve a better pre-annotation performance in the case of insufficient segmentation labels, semi-supervised and weakly supervised learning were introduced, and a semi-weakly supervised learning segmentation algorithm based on sparse labels was proposed. In addition, in order to improve the contribution rate of a single segmentation label to the performance of the pre-annotation model, an iterative annotation strategy based on active learning was designed. The experimental results on public MRI datasets show that the proposed algorithm achieved an equivalent pre-annotation performance when the number of segmentation labels was much less than that of the fully supervised learning algorithm, which proves the effectiveness of the proposed algorithm.

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Section: Methodsmentioning

confidence: 99%

A Semi-Automatic Magnetic Resonance Imaging Annotation Algorithm Based on Semi-Weakly Supervised Learning

Chen,

Zhang

2024

Sensors

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“…Wang et al [23] proposed an edge-attention-preserving (EAP) module to effectively remove noise and focus the model on boundary-related information. Chen et al [37] proposed an edge guidance feature pyramid (EGFP) to make the edge features interact with regional features at different scales to improve the adaptability to different organizational scales. Unlike these studies, we designed a novel enhanced edge feature attention gate structure.…”

Section: Edge-based Modelsmentioning

confidence: 99%

“…Chen et al. [37] proposed an edge guidance feature pyramid (EGFP) to make the edge features interact with regional features at different scales to improve the adaptability to different organizational scales.…”

Section: Related Studiesmentioning

confidence: 99%

A graph‐based edge attention gate medical image segmentation method

Hao

2023

IET Image Processing

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For the purpose of solving the problems of missing edges and low segmentation accuracy in medical image segmentation, a medical image segmentation network (EAGC_UNet++) based on residual graph convolution UNet++ with edge attention gate (EAG) is proposed in the study. With UNet++ as the backbone network, the idea of graph theory is introduced into the model. First, the dropout residual graph convolution block (DropRes_GCN Block) and the traditional convolution structure in UNet++ are used as encoders. Second, EAGs are adopted so that the model pays more attention to image edge features during decoding. Finally, aiming at the imbalance problem of positive and negative samples in medical image segmentation, a new weighted loss function is introduced to enhance segmentation accuracy. In the experimental part, three datasets (LiTS2017, ISIC2018, COVID‐19 CT scans) were used to evaluate the performances of various models; multiple groups of ablation experiments were designed to verify the effectiveness of each part of the model. The experimental results showed that EAGC_UNet++ had better segmentation performance than the other models under three quantitative evaluation indicators and better solved the problem of missing edges in medical image segmentation.

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“…Deep learning algorithms can automatically extract features directly from raw images, showing strong resistance to interference, and have achieved significant results in various areas such as traffic signal detection [11], license plate recognition [12], and medical image segmentation [13]. Based on deep learning algorithms, the development of ACCR can be divided into two types: (1) using a single neural network to complete the entire container code recognition process [14] and (2) stacking two neural networks to separately accomplish CCL and CCR [15].…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Automatic Container Code Recognition Method Considering Environmental Interference

Yu,

Zhu,

et al. 2024

Applied Sciences

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Automatic Container Code Recognition (ACCR) is critical for enhancing the efficiency of container terminals. However, existing ACCR methods frequently fail to achieve satisfactory performance in complex environments at port gates. In this paper, we propose an approach for accurate, fast, and compact container code recognition by utilizing YOLOv4 for container region localization and Deeplabv3+ for character recognition. To enhance the recognition speed and accuracy of YOLOv4 and Deeplabv3+, and to facilitate their deployment at gate entrances, we introduce several improvements. First, we optimize the feature-extraction process of YOLOv4 and Deeplabv3+ to reduce their computational complexity. Second, we enhance the multi-scale recognition and loss functions of YOLOv4 to improve the accuracy and speed of container region localization. Furthermore, we adjust the dilated convolution rates of the ASPP module in Deeplabv3+. Finally, we replace two upsampling structures in the decoder of Deeplabv3+ with transposed convolution upsampling and sub-pixel convolution upsampling. Experimental results on our custom dataset demonstrate that our proposed method, C-YOLOv4, achieves a container region localization accuracy of 99.76% at a speed of 56.7 frames per second (FPS), while C-Deeplabv3+ achieves an average pixel classification accuracy (MPA) of 99.88% and an FPS of 11.4. The overall recognition success rate and recognition speed of our approach are 99.51% and 2.3 ms per frame, respectively. Moreover, C-YOLOv4 and C-Deeplabv3+ outperform existing methods in complex scenarios.

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Combining edge guidance and feature pyramid for medical image segmentation

Cited by 21 publications

References 21 publications

A Semi-Automatic Magnetic Resonance Imaging Annotation Algorithm Based on Semi-Weakly Supervised Learning

A Semi-Automatic Magnetic Resonance Imaging Annotation Algorithm Based on Semi-Weakly Supervised Learning

A graph‐based edge attention gate medical image segmentation method

A Two-Stage Automatic Container Code Recognition Method Considering Environmental Interference

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