# Tractogram filtering of anatomically non-plausible fibers with geometric deep learning

arXiv: https://arxiv.org/abs/2003.11013 · HTML: https://mlsanity.com/paper/2003.11013/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2020.

## Built on by (most cited first, 17 of 17)

- [3D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysis](https://mlsanity.com/paper/2112.04863/) (2021)
- [Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions](https://mlsanity.com/paper/2207.08975/) (2022)
- [Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellation](https://mlsanity.com/paper/2205.00627/) (2022)
- [SupWMA: Consistent and Efficient Tractography Parcellation of Superficial White Matter with Deep Learning](https://mlsanity.com/paper/2201.12528/) (2022)
- [TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance](https://mlsanity.com/paper/2307.03982/) (2023)
- [TractCloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representation](https://mlsanity.com/paper/2307.09000/) (2023)
- [Diffusion MRI with Machine Learning](https://mlsanity.com/paper/2402.00019/) (2024)
- [White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learning](https://mlsanity.com/paper/2207.02402/) (2022)
- [TractGraphFormer: Anatomically Informed Hybrid Graph CNN-Transformer Network for Interpretable Sex and Age Prediction from Diffusion MRI Tractography](https://mlsanity.com/paper/2407.08883/) (2024)
- [PointNeuron: 3D Neuron Reconstruction via Geometry and Topology Learning of Point Clouds](https://mlsanity.com/paper/2210.08305/) (2022)
- [PatchMixer: Rethinking network design to boost generalization for 3D point cloud understanding](https://mlsanity.com/paper/2307.15692/) (2023)
- [TractShapeNet: Efficient Multi-Shape Learning with 3D Tractography Point Clouds](https://mlsanity.com/paper/2410.22099/) (2024)
- [Assessing Streamline Plausibility Through Randomized Iterative Spherical-Deconvolution Informed Tractogram Filtering](https://mlsanity.com/paper/2205.04843/) (2022)
- [Supervised Tractogram Filtering using Geometric Deep Learning](https://mlsanity.com/paper/2212.03300/) (2022)
- [Merging multiple input descriptors and supervisors in a deep neural network for tractogram filtering](https://mlsanity.com/paper/2307.05786/) (2023)
- [A Multimodal Deep Learning Approach for White Matter Shape Prediction in Diffusion MRI Tractography](https://mlsanity.com/paper/2504.18400/) (2025)
- [DeepMultiConnectome: Deep Multi-Task Prediction of Structural Connectomes Directly from Diffusion MRI Tractography](https://mlsanity.com/paper/2505.22685/) (2025)

## Abstract (arXiv)

Tractograms are virtual representations of the white matter fibers of the brain. They are of primary interest for tasks like presurgical planning, and investigation of neuroplasticity or brain disorders. Each tractogram is composed of millions of fibers encoded as 3D polylines. Unfortunately, a large portion of those fibers are not anatomically plausible and can be considered artifacts of the tracking algorithms. Common methods for tractogram filtering are based on signal reconstruction, a principled approach, but unable to consider the knowledge of brain anatomy. In this work, we address the problem of tractogram filtering as a supervised learning problem by exploiting the ground truth annotations obtained with a recent heuristic method, which labels fibers as either anatomically plausible or non-plausible according to well-established anatomical properties. The intuitive idea is to model a fiber as a point cloud and the goal is to investigate whether and how a geometric deep learning model might capture its anatomical properties. Our contribution is an extension of the Dynamic Edge Convolution model that exploits the sequential relations of points in a fiber and discriminates with high accuracy plausible/non-plausible fibers.