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Automatic Segmentation of White Matter Tracts Using Multiple Brain Mri Sequences
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Automatic Segmentation of White Matter Tracts Using Multiple Brain Mri Sequences
White matter tractography mapping is a must in neuro-surgical planning and navigation to minimize risks of iatrogenic damages. Clinical tractography pipelines still require time consuming manual operations and significant neuro-anatomical expertise, to accurately seed the tracts and remove tractography outliers. The automatic segmentation of white matter (WM) tracts using deep neural networks has been recently demonstrated. However, most of the works in this area use a single brain MRI sequence, whereas neuro-radiologists rely on 2 or more MRI sequences, e.g. T1w and the principal direction of diffusion (PDD), for pre-surgical WM mapping. In this work, we propose a novel neural architecture for the automatic segmentation of white matter tracts by fusing multiple MRI sequences. The proposed method is demonstrated and validated on joint T1w and PDD input sequences. It is shown to compare favorably against state-of-the art methods (Vnet, TractSeg) on the Human Connectome Project (HCP) brain scans dataset for clinically important WM tracts.
White matter tractography mapping is a must in neuro-surgical planning and navigation to minimize risks of iatrogenic damages. Clinical tractography pipelines still require time consuming manual operations and significant neuro-anatomical expertise, to accurately seed the tracts and remove tractography outliers. The automatic segmentation of white matter (WM) tracts using deep neural networks has been recently demonstrated. However, most of the works in this area use a single brain MRI sequence, whereas neuro-radiologists rely on 2 or more MRI sequences, e.g. T1w and the principal direction of diffusion (PDD), for pre-surgical WM mapping. In this work, we propose a novel neural architecture for the automatic segmentation of white matter tracts by fusing multiple MRI sequences. The proposed method is demonstrated and validated on joint T1w and PDD input sequences. It is shown to compare favorably against state-of-the art methods (Vnet, TractSeg) on the Human Connectome Project (HCP) brain scans dataset for clinically important WM tracts.
