EAI Endorsed Transactions on e-Learning

Applications of Image Segmentation Techniques in Medical Images

Yang-yang Hou — Fri, 19 Jul 2024 00:00:00 +0000

Image segmentation is an important research direction in medical image processing tasks, and it is also a challenging task in the field of computer vision. At present, there have been many image segmentation methods, including traditional segmentation methods and deep learning-based segmentation methods. Through the understanding and learning of the current situation in the field of medical image segmentation, this paper systematically combs it. Firstly, it briefly introduces the traditional image segmentation methods such as threshold method, region method and graph cut method, and focuses on the commonly used network architectures based on deep learning such as CNN, FCN, U-Net, SegNet, PSPNet, Mask R-CNN. At the same time, the application in medical image segmentation is expounded. Finally, the challenges and development opportunities of medical image segmentation technology based on deep learning are discussed.

A Community Detection Algorithm Based on Balanced Label Propagation

Huijuan Jia, Ting Liu, Xiaohong Zhang — Tue, 16 Jul 2024 00:00:00 +0000

OBJECTIVES: In conventional label propagation algorithms, the randomness inherent in the selection order of nodes and subsequent label propagation frequently leads to instability and reduces the accuracy of community detection outcomes.

METHODS: First, select the initial node according to the node importance and assign different labels to each initial node, aiming to reduce the number of iterations of the algorithm and improve the efficiency and stability of the algorithm; second, identify the neighbor node with the largest connection to each initial node for the pre-propagation of the labels; then, the algorithm traverses the nodes in descending order of the node importance for the propagation of labels to reduce the randomness of the label propagation process; finally, the final community is formed through the rapid merging of small communities.

RESULTS: The experimental results on multiple real datasets and artificially generated networks show that the stability and accuracy are all improved.

CONCLUSION: The proposed community detection algorithm based on balanced label propagation is better than the other four advanced algorithms on Q and NMI values of community division results.

ARM-Net: Improved MRI brain tumor segmentation method based on attentional mechanism and residual module

MingHu — Fri, 26 Jul 2024 00:00:00 +0000

INTRODUCTION: Accurate tumor segmentation is a prerequisite for reliable diagnosis and treatment of brain cancer. Gliomas, a highly prevalent and life-threatening type of brain tumor, pose a challenge for segmentation due to the intricate nature of brain structures and unpredictable appearances on brain MRI images.

OBJECTIVES: Current methods for brain tumor segmentation mostly rely on deep convolutional neural networks, which suffer from significant loss of feature information during encoding and decoding and the inability to capture tumor contours in detail.

METHODS: To address these challenges, this study rethinks the network architecture for MRI brain tumor segmentation. It proposes ARM-Net: an improved method for MRI brain tumor segmentation based on attention mechanisms and residual modules. Firstly, inverted external attention and dilated gated attention are employed in the last two layers of the encoder to enable the network to interact with both lesion areas and global information, facilitating better interaction among the four modalities. Secondly, different numbers of Res-Paths are added in the encoder's first two layers and the decoder's last two layers to effectively mitigate the semantic gap issues caused by traditional skip connections.

RESULTS: Experiments on the BraTS 2019 dataset demonstrate that ARM-Net outperforms other similar models in terms of segmentation performance.

CONCLUSION: The experiment showed that the ARM-Net model could segment the contour structure of the tumor better than other methods.