TY - JOUR T1 - A Multi-Scale Fully Convolutional Networks Model for Brain MRI Segmentation AU - Zhihui Cao, Yuhang Qin and Yunjie Chen JO - Journal of Information and Computing Science VL - 2 SP - 083 EP - 088 PY - 2024 DA - 2024/01 SN - 13 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jics/22450.html KW - Convolutional neural networks KW - Fully convolutional networks KW - magnetic resonance image KW - multi-scale. AB - 1School of math and statistics, Nanjing University of Information Science & Technology, Nanjing 210044, China (Received October 01 2017, accepted January 15 2018) Abstract 。 Accurate segmentation for brain magnetic resonance (MR) images is of great significance to quantitative analysis of brain image. However, traditional segmentation methods suffer from the problems existing in brain images such as noise, weak edges and intensity inhomogeneity (also named as bias field). Convolutional neural networks based methods have been used to segment images; however, it is still hard to find accurate results for brain MR images. In order to obtain accurate segmentation results, a multi-scale fully convolution networks model (MSFCN) is proposed in this paper. First, we use padding convolutions in conv-layer to preserve the resolution of feature maps. So we can obtain segmentation results with the same resolution as inputs. Then, different sized filters are utilized in the same conv-layer, after that, the outputs of these filters are concatenated together and fed to the next layer, which makes the model learn features from different scales. Both experimental results and statistic results show that the proposed model can obtain more accurate results.

Zhihui Cao, Yuhang Qin and Yunjie Chen. (2024). A Multi-Scale Fully Convolutional Networks Model for Brain MRI Segmentation. Journal of Information and Computing Science. 13 (2). 083-088. doi: