Advanced Brain Tumor Segmentation With a Multiscale CNN and Conditional Random Fields

The use of high-precision automatic algorithms for segmenting brain tumors has the potential to improve disease diagnosis, treatment monitoring, and large-scale pathological studies. In this study, we present a novel 9-layer multiscale architecture designed specifically for the semantic segmentation of 3D medical images, with a particular focus on brain tumor images, using convolutional neural networks. Our innovative solution incorporates several significant enhancements, including the use of variable-sized filters between layers and the early incorporation of residual connections from the very first layer. These modifications allow us to reduce the number of layers in our network while significantly improving the accuracy of 3D medical image segmentation. For post-processing the network’s soft segmentation, we employ a 3D fully connected Conditional Random Field, which effectively removes false positives. This approach enables our model to achieve an impressive accuracy of 99.88%, a sensitivity of 99.86%, and a specificity of 99.96%, outperforming existing models. This combination of innovations positions our solution as a major advancement in the field of 3D medical image segmentation, offering an optimal balance between accuracy and efficiency for clinical applications.