计算机科学
编码器
联营
人工智能
棱锥(几何)
核(代数)
块(置换群论)
模式识别(心理学)
分割
增采样
残余物
计算机视觉
算法
数学
图像(数学)
组合数学
操作系统
几何学
作者
Abdul Qayyum,Moona Mazher,Muhammad Imran Razzak,Steven Niederer
标识
DOI:10.1109/dicta60407.2023.00011
摘要
Cardiovascular diseases are a global leading cause of death. The automatic segmentation of the left ventricle (LV) from magnetic resonance (MR) images is essential for quantitative analysis and assessment of cardiac functioning and for monitoring and diagnosing several heart diseases. In this paper, we introduce a multi-scale pre-trained framework enhanced by a kernel-based atrous spatial pyramid pooling block which facilitates information propagation by concatenating distinct features from the encoder blocks at various stages. Additionally, we incorporate convolutional block attention modules (CBAM) into the encoder to attain adaptive refinement features and employed different types of residual blocks for multiscale upsampling features at each decoder stage, and we have introduced efficient residual blocks based on dilated convolutional layers in the decoder. We use variety of pre-trained networks, including X-Net, UNet, DSNet, Xception, and Efficient models, serving as the base networks which seamlessly integrated with kernel-based atrous spatial pyramid pooling module to aggregate information from the encoder side. Experiments on the Myocardium benchmark dataset demonstrate better performance in basal and middle slices compared to state-of-the-art methods, although performance in apical slices is relatively less impressive. We have also made our code publicly accessible 1 . 1 https://github.com/RespectKnowledge/LGEMRI MyoSeg DL
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