Abstract
Change detection and progression assessment in multiple sclerosis (MS) by serial magnetic resonance imaging (MRI) are important, yet challenging tasks. Analysis algorithms such as Voxel-Guided Morphometry (VGM) enable detection and quantification of even minor changes of the brain at different time points. To shorten computation times and ameliorate clinical applicability, we developed a convolutional neural network based VGM (Deep VGM) providing a fast solution for intra-individual serial volume change analysis in MS.
We developed a residual architecture based on the 3D U-Net and investigated several loss functions to predict VGM maps from a base line and a follow up brain MRI. We train and test our approach in 71 MS patients. The Deep VGM maps are compared to the respective VGM maps via several image metrics and rated by an experienced neurologist.
Deep VGM configured with the Mean Absolute Error and Gradient loss outperformed all other tested loss functions. Deep VGM maps showed high similarity to the original VGM maps (SSIM \(= 0.9521 \pm 0.0236\)). This was additionally confirmed by a neurologist analysing the MS lesions. Deep VGM resulted in a 3% lesion error rate compared to the original VGM approach. Computation time of Deep VGM was 99.62% shorter than VGM. Our experiments demonstrate that Deep VGM can approximate the complex VGM mapping at high quality while saving computation time.
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This research project is funded by the Ministry of Economic Affairs Baden Württemberg within the framework “KI für KMU”.
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Schnurr, AK. et al. (2020). Deep Voxel-Guided Morphometry (VGM): Learning Regional Brain Changes in Serial MRI. In: Kia, S.M., et al. Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology. MLCN RNO-AI 2020 2020. Lecture Notes in Computer Science(), vol 12449. Springer, Cham. https://doi.org/10.1007/978-3-030-66843-3_16
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