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An arbitrary-modal fusion network for volumetric cranial nerves tract segmentation
Lei Xie1, Huajun Zhou2, Junxiong Huang1
1Institute of Advanced Technology, Zhejiang University of Technology, Hangzhou, China.
Summary
We developed CNTSeg-v2, a new AI model for segmenting cranial nerves (CNs) using various MRI data combinations. This method achieves top performance, improving analysis of nerve pathways.
Area of Science:
- Neuroimaging
- Medical Image Analysis
- Computational Neuroscience
Background:
- Accurate segmentation of cranial nerves (CNs) is crucial for quantitative analysis of their morphology and trajectory.
- Existing multimodal CN segmentation networks (e.g., CNTSeg) show promise but require complete multimodal data, which is often clinically infeasible.
- Limitations in data acquisition, privacy, and equipment hinder the routine use of multimodal MRI for CN segmentation.
Purpose of the Study:
- To propose CNTSeg-v2, a novel arbitrary-modal fusion network for volumetric cranial nerve segmentation.
- To develop a single model capable of handling diverse combinations of available MRI modalities.
- To improve the efficiency and feasibility of CN segmentation in clinical settings.
Main Methods:
- Developed CNTSeg-v2, an arbitrary-modal fusion network for volumetric CN segmentation.
- Utilized T1-weighted (T1w) MRI as the primary modality for supervision, guiding the selection of features from auxiliary modalities.
- Incorporated an Arbitrary-Modal Collaboration Module (ACM) for effective feature extraction and a Deep Distance-guided Multi-stage (DDM) decoder for error correction using signed distance maps.
Main Results:
- CNTSeg-v2 demonstrated state-of-the-art performance in volumetric cranial nerve segmentation.
- The model achieved superior results compared to all competing methods on both the Human Connectome Project (HCP) and Multi-shell Diffusion MRI (MDM) datasets.
- The arbitrary-modal fusion approach effectively leveraged available MRI data combinations for improved segmentation accuracy.
Conclusions:
- CNTSeg-v2 offers a flexible and high-performing solution for cranial nerve segmentation using varied MRI data.
- The proposed method overcomes the limitations of requiring complete multimodal datasets in clinical practice.
- CNTSeg-v2 represents a significant advancement in automated neuroimaging analysis for cranial nerve pathways.
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