Superpixel/voxel medical image segmentation algorithm based on the regional interlinked value

Fang, Lingling; Wang, Xin; Wang, Mengyi

doi:10.1007/s10044-021-01021-8

Cited by 5 publications

(1 citation statement)

References 55 publications

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“…Gheshlaghi et al [ 84 ] used the superpixel segmentation technique to overcome dimensionality problems for multiple sclerosis lesion detection. Fang et al [ 85 ] proposed a superpixel segmentation algorithm to segment two-dimensional bone images and three-dimensional brain images. In this paper, the blocks with the same features were merged and to segment the superpixel/voxel medical image, the final distance with the intensity feature and the location feature, and the gradient feature was considered.…”

Section: Network Typesmentioning

confidence: 99%

Applications of Neural Networks in Biomedical Data Analysis

et al. 2022

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Neural networks for deep-learning applications, also called artificial neural networks, are important tools in science and industry. While their widespread use was limited because of inadequate hardware in the past, their popularity increased dramatically starting in the early 2000s when it became possible to train increasingly large and complex networks. Today, deep learning is widely used in biomedicine from image analysis to diagnostics. This also includes special topics, such as forensics. In this review, we discuss the latest networks and how they work, with a focus on the analysis of biomedical data, particularly biomarkers in bioimage data. We provide a summary on numerous technical aspects, such as activation functions and frameworks. We also present a data analysis of publications about neural networks to provide a quantitative insight into the use of network types and the number of journals per year to determine the usage in different scientific fields.

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Section: Network Typesmentioning

confidence: 99%