Abstract：Objective Accurate segmentation of glioma in MR images is the premise of determining the scope of tumor and
formulating treatment plans. In order to solve the problems of high complexity and low accuracy in the process of traditional glioma
segmentation methods, this paper proposes a method that improves U-Net network combined with region growth algorithm to
segment gliomas in MR images. Methods MR images and manual segmentation labels of glioma were downloaded from public
database. The residual module was added between the two convolutional layers of each stage and bridge of the U-Net network to
improve the network, and then a moderate region growth operation was performed on the network segmentation result to describe
the boundaries of the tumor. Use indicators such as the dice similarly coefficient (DSC) and the boundary F1 (BF) contour matching
score (BF score) were used to evaluate the segmentation performance of the proposed method. Results In the dataset of optimized
regional growth parameters, the segmentation results were optimal when the maximum intensity distance of regional growth and the
gray threshold of the seed points were 0.01 and 86. In the test set, which included all layers of the tumor, the DSC and BF scores
reached 0.8332 and 0.7283, respectively. Compared with the traditional FCN-8s and DeepLab v3+ networks, the DSC score were
improved by 7.43% and 4.56%, respectively. Conclusion The improved U-Net network combined with the region growing operation
can well describe the location, scope and boundary information of glioma, which can be used to assist doctors in quantitative analysis
of glioma.
李阳，宋悦，穆伟斌，张淑丽. 深度学习结合区域生长对MR图像胶质瘤的分割[J]. 中国医疗设备, 2022, 37(12): 35-39.
LI Yang, SONG Yue, MU Weibin, ZHANG Shuli. Deep Learning Combined with Region Growth for Segmentation of Glioma in MR Images. China Medical Devices, 2022, 37(12): 35-39.
