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Understanding how the microstructure varies across different brain regions is critical for disease modeling and brain registration. However, current deep learning approaches that work on image data directly may unintentionally focus on textures or other sources of noise in the data and fail to capture meaningful information about the underlying microstructures of interest. In this work, we propose a deep learning method that aims to build salient representations of microstructures inside neuroimage data by working on point cloud representations. We developed a hierarchical PointNet to process extracted 3D point clouds of brain anatomy to solve a brain region classification task. We validate our method on a micron-scale neuroimaging dataset, where we generated point clouds from both pixel-level segmentations and simple edge detection methods. In both cases, we show that point cloud-based models achieve better stability and performance when compared to 3D convolutional networks trained on the same brain region classification task. Our results in using “noisier” data from simple filtering operations provides initial evidence that point cloud representations could be a lightweight and data-efficient approach for brain parcellation. Keywords: Neuroanatomy, Point Cloud, Deep Learning, PointNet, Brain Parcellation
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Thursday 7th July
Poster Session 2.2 - onsite 11:00 - 12:00, virtual 15:20 - 16:20 (UTC+2)
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