 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  .
Purpose: Conventional automated segmentation of the head anatomy in MRI distinguishes different brain and non-brain 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 often ignore prior probabilities. Approach: We add three sources of prior information to a three-dimensional convolutional network , namely, spatial priors with a TPM, morphological priors with conditional random fields, and spatial context with a wider field-of-view at lower resolution. We train and test these networks on 3D images of 43 stroke patients and 4 healthy individuals which have been manually segmented. Results: We demonstrate the benefits of each sources of prior information , and we show that the new architecture, which we call Multiprior network, improves the performance of existing segmentation software, such as SPM, FSL, and DeepMedic for abnormal anatomies. The relevance of the different priors was compared and the TPM was found to be most beneficial. The benefit of adding a TPM is generic in that it can boost the performance of established segmentation networks such as the DeepMedic and a UNet. We also provide an out-of-sample validation and clinical application of the approach on an additional 47 patients with disorders of consciousness. We make the code and trained networks freely available . Conclusions: Biomedical images follow imaging protocols that can be leveraged as prior information into deep convolutional neural networks to improve performance. The network segmentations match human manual corrections performed in 3D, and are comparable in performance to human segmentations obtained from scratch in 2D for abnormal brain anatomies .
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Purpose:
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MRI of the brain and head distinguishes different
the head anatomy in MRI distinguishes different brain and non-brain
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have thus far not leveraged
often ignore
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Here we add
Approach: We add three sources of prior information
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, namely,
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spatial
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3D
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The analysis demonstrates
Results: We demonstrate
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leveraging the three
each
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.
, and we show that the new architecture, which we call Multiprior network, improves the performance of existing segmentation software, such as SPM, FSL, and DeepMedic for abnormal anatomies. The relevance of the different priors was compared and the TPM was found to be most beneficial. The benefit of adding a TPM is generic in that it can boost the performance of established segmentation networks such as the DeepMedic and a UNet.
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Importantly, we demonstrate that the new architecture, which we call MultiPrior network, surpaces the performance of expert human segmenters.
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. Conclusions: Biomedical images follow imaging protocols that can be leveraged as prior information into deep convolutional neural networks to improve performance. The network segmentations match human manual corrections performed in 3D, and are comparable in performance to human segmentations obtained from scratch in 2D for abnormal brain anatomies