# Filtering in tractography using autoencoders (FINTA)

arXiv: https://arxiv.org/abs/2010.04007 · HTML: https://mlsanity.com/paper/2010.04007/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

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

- [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)
- [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)
- [Generative Sampling in Bundle Tractography using Autoencoders (GESTA)](https://mlsanity.com/paper/2204.10891/) (2022)
- [TractOracle: towards an anatomically-informed reward function for RL-based tractography](https://mlsanity.com/paper/2403.17845/) (2024)
- [FIESTA: Autoencoders for accurate fiber segmentation in tractography](https://mlsanity.com/paper/2212.00143/) (2022)
- [StreamNet: A WAE for White Matter Streamline Analysis](https://mlsanity.com/paper/2209.01498/) (2022)
- [Supervised Tractogram Filtering using Geometric Deep Learning](https://mlsanity.com/paper/2212.03300/) (2022)
- [Differentiable VQ-VAE's for Robust White Matter Streamline Encodings](https://mlsanity.com/paper/2311.06212/) (2023)
- [TractoGPT: A GPT architecture for White Matter Segmentation](https://mlsanity.com/paper/2501.15464/) (2025)
- [Exploring the robustness of TractOracle methods in RL-based tractography](https://mlsanity.com/paper/2507.11486/) (2025)
- [DMVFC: Deep Learning Based Functionally Consistent Tractography Fiber Clustering Using Multimodal Diffusion MRI and Functional MRI](https://mlsanity.com/paper/2510.24770/) (2025)
- [Merging multiple input descriptors and supervisors in a deep neural network for tractogram filtering](https://mlsanity.com/paper/2307.05786/) (2023)
- [Deep Representation Learning for Unsupervised Clustering of Myocardial Fiber Trajectories in Cardiac Diffusion Tensor Imaging](https://mlsanity.com/paper/2504.01953/) (2025)
- [GenTract: Generative Global Tractography](https://mlsanity.com/paper/2511.13183/) (2025)
- [TrackletGPT: A Language-like GPT Framework for White Matter Tract Segmentation](https://mlsanity.com/paper/2601.13935/) (2026)
- [TractoGraphVLM: A Unified Vision-Language Framework for White Matter Tractography](https://mlsanity.com/paper/2608.18166/) (2026)

## Abstract (arXiv)

Current brain white matter fiber tracking techniques show a number of problems, including: generating large proportions of streamlines that do not accurately describe the underlying anatomy; extracting streamlines that are not supported by the underlying diffusion signal; and under-representing some fiber populations, among others. In this paper, we describe a novel autoencoder-based learning method to filter streamlines from diffusion MRI tractography, and hence, to obtain more reliable tractograms. Our method, dubbed FINTA (Filtering in Tractography using Autoencoders) uses raw, unlabeled tractograms to train the autoencoder, and to learn a robust representation of brain streamlines. Such an embedding is then used to filter undesired streamline samples using a nearest neighbor algorithm. Our experiments on both synthetic and in vivo human brain diffusion MRI tractography data obtain accuracy scores exceeding the 90% threshold on the test set. Results reveal that FINTA has a superior filtering performance compared to conventional, anatomy-based methods, and the RecoBundles state-of-the-art method. Additionally, we demonstrate that FINTA can be applied to partial tractograms without requiring changes to the framework. We also show that the proposed method generalizes well across different tracking methods and datasets, and shortens significantly the computation time for large (>1 M streamlines) tractograms. Together, this work brings forward a new deep learning framework in tractography based on autoencoders, which offers a flexible and powerful method for white matter filtering and bundling that could enhance tractometry and connectivity analyses.