TY - JOUR T1 - Residual and Dense Connection Combine Fully Convolutional Network for Infant Brain MRI Segmentation AU - Yuhang Qin and Mao Cai JO - Journal of Information and Computing Science VL - 2 SP - 156 EP - 160 PY - 2024 DA - 2024/01 SN - 14 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jics/22425.html KW - medical image segmentation, infant brain MR images, Convolutional network, Residual Connection Block, Dense Connection Block AB - In the development of medical image segmentation, the application of convolutional neural networks has begun a profound revolution. The deep learning model is famous for excellent flexibility, efficiency and accuracy. The U-Net model is the beginning of task in the segmentation of medical images, which includes the basic operations of convolution, maxpooling, deconvolution, and concatenation. However, the U-Net model is disable to perform well on many types of data sets, because the model can’t solve the exact segmentation of the details. We proposed Residual and Dense Fully Convolutional Network (RDFCN) that consist of Residual Connection Block and Dense Connection Block, which makes up for the shortcomings of U-Net. The dataset we used for training and testing comes from iSeg-2017 challenge (http://iseg2017.web.unc.edu). This dataset is comprised of infant(between 6 and 9 months of age) brain MR images. After the testing, our model outperforms the U-Net and some of its improved models in evaluation of WM, GM and CSF.

Yuhang Qin and Mao Cai. (2024). Residual and Dense Connection Combine Fully Convolutional Network for Infant Brain MRI Segmentation. Journal of Information and Computing Science. 14 (2). 156-160. doi: