DeepVoxNet2: Yet another CNN framework
This addresses the problem of complex coding and pipeline exchange for researchers in medical imaging, but it is incremental as it builds on existing CNN frameworks.
The authors tackled the lack of a unified framework for CNN-based image analysis that automatically tracks spatial data origins, introducing DeepVoxNet2 (DVN2) as a solution for 1D, 2D, or 3D image classification or segmentation, demonstrated using the BRATS 2018 dataset.
We know that both the CNN mapping function and the sampling scheme are of paramount importance for CNN-based image analysis. It is clear that both functions operate in the same space, with an image axis $\mathcal{I}$ and a feature axis $\mathcal{F}$. Remarkably, we found that no frameworks existed that unified the two and kept track of the spatial origin of the data automatically. Based on our own practical experience, we found the latter to often result in complex coding and pipelines that are difficult to exchange. This article introduces our framework for 1, 2 or 3D image classification or segmentation: DeepVoxNet2 (DVN2). This article serves as an interactive tutorial, and a pre-compiled version, including the outputs of the code blocks, can be found online in the public DVN2 repository. This tutorial uses data from the multimodal Brain Tumor Image Segmentation Benchmark (BRATS) of 2018 to show an example of a 3D segmentation pipeline.