3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures
This is an incremental improvement for medical imaging researchers focusing on brain tumor segmentation.
The paper tackled brain tumor segmentation in MR images using 3D Convolutional Neural Networks, comparing three multi-resolution architectures that improved over single-resolution methods, though no concrete numbers were provided.
This paper analyzes the use of 3D Convolutional Neural Networks for brain tumor segmentation in MR images. We address the problem using three different architectures that combine fine and coarse features to obtain the final segmentation. We compare three different networks that use multi-resolution features in terms of both design and performance and we show that they improve their single-resolution counterparts.