Segmentation of lesioned brain anatomy with deep volumetric neural networks and multiple spatial priors achieves human-level performance
Lukas Hirsch, Yu Huang, Lucas C. Parra · arXiv (Cornell University) · 2019
Conventional automated segmentation of MRI of the brain and head distinguishes different tissues based on image intensities and prior tissue probability maps (TPM). This works well for normal head anatomies, but fails in the presence of unexpected lesions. Deep convolutional neural networks leverage instead spatial patterns and can learn to segment lesions, but have thus far not leveraged prior probabilities. Here we add to a three-dimensional convolutional network spatial priors with a TPM, morphological priors with conditional random fields, and context with a wider field-of-view at lower resolution. We train and test these networks on images of 43 stroke patients and 4 healthy individuals which have been manually segmented. The analysis demonstrates the benefits of leveraging the three sources of prior information. We also provide an out-of-sample validation and clinical application of the approach on an additional 47 patients with disorders of consciousness. Importantly, we demonstrate that the new architecture, which we call MultiPrior network, surpaces the performance of expert human segmenters. We make the code and trained networks freely available.