Conventional automated segmentation of the human head distinguishes different tissues based on image intensities in an MRI volume 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 volumetric spatial patterns and can be trained to segment lesions, but have thus far not integrated 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. The new architecture, which we call MultiPrior, was designed to be a fully-trainable, three-dimensional convolutional network. Thus, the resulting architecture represents a neural network with learnable spatial memories. When trained on a set of stroke patients and healthy subjects, MultiPrior outperforms the state-of-the-art segmentation tools such as DeepMedic and SPM segmentation. The approach is further demonstrated on patients with disorders of consciousness , where we find that cognitive state correlates positively with gray-matter volumes and negatively with the extent of ventricles . We make the code and trained networks freely available to support future clinical research projects .
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 .
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the human
MRI of the brain and
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in an MRI volume
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volumetric
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be trained
learn
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integrated
leveraged
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The new architecture, which we call MultiPrior, was designed to be a fully-trainable, three-dimensional convolutional network. Thus, the resulting architecture represents a neural network with learnable spatial memories. When trained on a set of
We train and test these networks on images of 43
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healthy subjects, MultiPrior outperforms the state-of-the-art segmentation tools such as DeepMedic and SPM segmentation. The approach is further demonstrated on
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
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, where we find that cognitive state correlates positively with gray-matter volumes and negatively with the extent of ventricles
. Importantly, we demonstrate that the new architecture, which we call MultiPrior network, surpaces the performance of expert human segmenters
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to support future clinical research projects