Convolutional neural network model for segmentation of magnetic resonance images with brain tumors

Accurate and fast segmentation of magnetic resonance (MR) images with volumetric brain formations, such as glioblastomas and meningiomas, helps plan surgical and radiation treatment, increase the safety and radicality of surgical intervention, which in turn allows to increase the overall life expectancy of patients and the disease-free period. It is required for the development and evaluation of the effectiveness of a deep convolutional neural network with transmission connections for automatic segmentation of volumetric brain formations (meningiomas and glioblastomas) on postoperative MR images, as well as analysis of its accuracy in comparison with manual expert segmentation and existing methods. Material and methods . The paper considers the creation of an architecture inspired by the SegResNet model, training on BraTS2024-GLI and BraTS2024-MEN-RT, describes a method for compiling a training sample that reduces class imbalance, and analyzes the results in comparison with participants in the BraTS 2024 competition. Results and discussion . The developed model was trained and tested on two datasets of postoperative images of glioblastoma and meningioma. Several metrics are analyzed to compare the model with the methods described in the literature, as well as to evaluate it in the context of the variability of manual segmentation by different experts. The model achieves a Sorensen coefficient 0.8299 for meningioma segmentation and 0.7028 for glioblastoma contrast-accumulating region segmentation. In addition, the segmentation of the model provides an accurate estimate of the volume of the tumor area, as evidenced by the high values of the coefficient of intra-class correlation – 0.9661 for meningioma and 0.8339 for glioblastoma. Overall, the developed model requires fewer resources for learning and getting results. Conclusions. Model performs segmentation at least at the expert level, but with significantly less variability, especially when assessing the volume of the tumor after surgical treatment.