Automatic segmentation of human supraclavicular adipose tissue using high-resolution T2-weighted magnetic resonance imaging

Wu, Bingxia; Cheng, Chuanli; Qi, Yingyong; Peng, Hao; Wan, Qian; Liu, Xin; Zheng, Hairong; Zhang, Huimao; Zou, Chao

doi:10.1007/s10334-022-01056-w

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“…In order to objectively evaluate these three image segmentation methods, we select Dice, Precision, and Recall three evaluation indexes to analyze their advantages and disadvantages [8] . After calculation, we obtained the evaluation index data of the previous six groups of experiments.…”

Section: Experimental Results and Comparisonmentioning

confidence: 99%

A medical image segmentation method based on SGFC-MSPCNN

Zhang,

Zhou,

Zhang

et al. 2024

International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024)

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In recent years, pulse-coupled neural network (PCNN) has been widely used in the image segmentation field and has obtained relatively satisfactory results. In this paper, based on fire-controlled MSPCNN(FC-MSPCNN), an image segmentation method based on saliency-guided FCMSPCNN(SGFC-MSPCNN) is proposed, which enhances the reasonableness of the variations between the internal activity term and dynamic threshold for the proposed model. and after experimental validation, the method significantly improves the detection accuracy while reducing the detection cost, and thus it is confirmed to be an effective medical image segmentation method.

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Section: Experimental Results and Comparisonmentioning

confidence: 99%