Boundary-rendering network for breast lesion segmentation in ultrasound images

Huang, Ruobing; Lin, Min; Dou, Haoran; Lin, Zhiping; Ying, Qilong; Jia, Xiaohong; Xu, Wenwen; Mei, Zihan; Dong, Yijie; Zhou, Jianqiao; Ni, Dong

doi:10.1016/j.media.2022.102478

Cited by 47 publications

(21 citation statements)

References 24 publications

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“…The approach of pooling indices realizes a type of Upsampling that does not require training at the cost of higher spatial complexity. ResUNet [16] is a semantic segmentation network structure, in which U‐Net encoder and decoder as the backbone are combined with residual connectivity and atrous convolution. The experimental results are shown in Figures 9–11.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

“…A 2D-EAG structure is added to X 0,4 ∼ X 4,0 , and weight semantic fusion is added at the ends of the four semantic levels so that the network performance can be further improved by adjusting the weight distribution for different training scenarios. The operation of weight semantic fusion is shown in Equation (16).…”

Section: Eagc_unet++mentioning

confidence: 99%

“…Some scholars proved that the graph convolution structure could prevent the loss of local position and structure information in image segmentation tasks [13][14][15]. The graph model has a more flexible feature representation than the data input to the grid structure in the convolutional model, so it can obtain more relationships between the nodes in the graph and prevent the loss of local location information and structural information [16]. Like the role of convolutional and pooling layers in convolutional operations, the node-based GCN can exchange information between neighboring nodes and extract features in a larger graph neighborhood.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Experiments and Results Analysismentioning

confidence: 99%

Section: Eagc_unet++mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A graph‐based edge attention gate medical image segmentation method

Hao

2023

IET Image Processing

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“…In recent years, thanks to the fast development of machine learning, most researchers have focused on learning-based methods that use deep neural networks to automatically learn features and segmentations from data [ 57 , 58 , 59 , 60 , 61 , 62 ]. Among these methods, U-Net is one of the most popular and widely used architectures for medical image segmentation due to its flexibility, optimized modular design, and success in all medical image modalities [ 57 ].…”

Section: Resultsmentioning

confidence: 99%

Modern Image-Guided Surgery: A Narrative Review of Medical Image Processing and Visualization

Lin,

Lei,

Yang

2023

Sensors

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Medical image analysis forms the basis of image-guided surgery (IGS) and many of its fundamental tasks. Driven by the growing number of medical imaging modalities, the research community of medical imaging has developed methods and achieved functionality breakthroughs. However, with the overwhelming pool of information in the literature, it has become increasingly challenging for researchers to extract context-relevant information for specific applications, especially when many widely used methods exist in a variety of versions optimized for their respective application domains. By being further equipped with sophisticated three-dimensional (3D) medical image visualization and digital reality technology, medical experts could enhance their performance capabilities in IGS by multiple folds. The goal of this narrative review is to organize the key components of IGS in the aspects of medical image processing and visualization with a new perspective and insights. The literature search was conducted using mainstream academic search engines with a combination of keywords relevant to the field up until mid-2022. This survey systemically summarizes the basic, mainstream, and state-of-the-art medical image processing methods as well as how visualization technology like augmented/mixed/virtual reality (AR/MR/VR) are enhancing performance in IGS. Further, we hope that this survey will shed some light on the future of IGS in the face of challenges and opportunities for the research directions of medical image processing and visualization.

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“…Huang et al [ 169 ] explored concept of Graph Convolutional Network for tumor segmentation using ultrasound. Breast ultrasound (BUS) has shown to be a reliable method for finding breast malignancy in its initial stages.…”

Section: Deep Learning For Medical Image Analysis and Cadmentioning

confidence: 99%

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Iqbal

Qureshi

et al. 2023

Arch Computat Methods Eng

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Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.

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Boundary-rendering network for breast lesion segmentation in ultrasound images

Cited by 47 publications

References 24 publications

A graph‐based edge attention gate medical image segmentation method

A graph‐based edge attention gate medical image segmentation method

Modern Image-Guided Surgery: A Narrative Review of Medical Image Processing and Visualization

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

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