# Tractography-Guided Dual-Label Collaborative Learning for Multi-Modal Cranial Nerves Parcellation

arXiv: https://arxiv.org/abs/2508.01577 · HTML: https://mlsanity.com/paper/2508.01577/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 3 Aug 2025.

## Builds on (most cited first, 5 of 5)

- [TractSeg - Fast and accurate white matter tract segmentation](https://mlsanity.com/paper/1805.07103/) (2018)
- [Anatomy-guided fiber trajectory distribution estimation for cranial nerves tractography](https://mlsanity.com/paper/2402.18856/) (2024)
- [Partitioned Shape Modeling with On-the-Fly Sparse Appearance Learning for Anterior Visual Pathway Segmentation](https://mlsanity.com/paper/1508.01128/) (2015)
- [A deep learning classification scheme based on augmented-enhanced features to segment organs at risk on the optic region in brain cancer patients](https://mlsanity.com/paper/1703.10480/) (2017)
- [Modality Exchange Network for Retinogeniculate Visual Pathway Segmentation](https://mlsanity.com/paper/2401.01685/) (2024)

## Built on by (most cited first, 1 of 1)

- [CNsEMD: An Expert-Annotated Multi-Field-Strength MRI Dataset and a Hyperspherical Manifold Network for Multimodal Cranial Nerve Parcellation](https://mlsanity.com/paper/2609.07058/) (2026)

## Abstract (arXiv)

The parcellation of Cranial Nerves (CNs) serves as a crucial quantitative methodology for evaluating the morphological characteristics and anatomical pathways of specific CNs. Multi-modal CNs parcellation networks have achieved promising segmentation performance, which combine structural Magnetic Resonance Imaging (MRI) and diffusion MRI. However, insufficient exploration of diffusion MRI information has led to low performance of existing multi-modal fusion. In this work, we propose a tractography-guided Dual-label Collaborative Learning Network (DCLNet) for multi-modal CNs parcellation. The key contribution of our DCLNet is the introduction of coarse labels of CNs obtained from fiber tractography through CN atlas, and collaborative learning with precise labels annotated by experts. Meanwhile, we introduce a Modality-adaptive Encoder Module (MEM) to achieve soft information swapping between structural MRI and diffusion MRI. Extensive experiments conducted on the publicly available Human Connectome Project (HCP) dataset demonstrate performance improvements compared to single-label network. This systematic validation underscores the effectiveness of dual-label strategies in addressing inherent ambiguities in CNs parcellation tasks.