NeuroMorphNet: A Multiscale Convolutional Neural Network for High-Precision Brain Tumor Segmentation in 3D Medical Images

Abstract

Abstract The use of high-precision automatic algorithms to segment brain tumors offers the potential for improved disease diagnosis, treatment monitoring, as well as the possibility of large-scale pathological studies. In this study, we present a new 9-layer multiscale architecture dedicated to the semantic segmentation of 3D medical images, with a particular focus on brain tumor images, using convolutional neural networks. Our innovative solution draws inspiration from the Deepmedic architecture while incorporating significant enhancements. The use of variable-sized filters between layers and the early incorporation of residual connections from the very first layer greatly enhance the accuracy of 3D medical image segmentation. Additionally, the reduction in the number of layers in our dual-pathway network optimizes efficiency while maintaining exceptional performance. This combination of innovations, with Deepmedic as a starting point, 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.