Knowledge-Based Framework for Segmenting HRIM Catheters in VFSS Images

Overview

This study presents a novel, template-free, knowledge-based algorithm for automatic segmentation of high-resolution impedance manometry (HRIM) catheters in videofluoroscopic swallow study (VFSS) images. The approach addresses challenges posed by complex backgrounds and rapid dynamics in VFSS, enabling improved spatial registration between HRIM and VFSS modalities.

Background

Head and neck cancer patients frequently develop oropharyngeal dysphagia, which is diagnosed using videofluoroscopic swallow studies (VFSS). VFSS provides dynamic visualization of swallowing phases but relies heavily on clinician interpretation, leading to variability. High-resolution impedance manometry (HRIM) offers quantitative swallow metrics but requires manual region delineation, which is challenging in patients with anatomical changes. Combining HRIM and VFSS could enhance diagnosis but demands accurate catheter localization within VFSS images.

Data Highlights

Recent methods for catheter segmentation in fluoroscopy include deep-learning models like U-Net and HRNet, which require large annotated datasets. Knowledge-based approaches, leveraging domain-specific information, offer robust segmentation with less data. Prior template-matching frameworks localized HRIM catheters but lacked generalizability across catheter models. The proposed template-free knowledge-based algorithm overcomes these limitations, handling occlusions and complex backgrounds in VFSS videos.

Key Findings

• Head and neck cancer patients often suffer from oropharyngeal dysphagia, necessitating accurate swallow assessment.
• VFSS is the gold standard for dysphagia diagnosis but is limited by subjective interpretation and inter-rater variability.
• HRIM provides quantitative swallow metrics but requires manual and challenging region delineation, especially in altered anatomy.
• Existing catheter segmentation methods in fluoroscopy rely on deep learning or template matching, both with limitations in data requirements or generalizability.
• The introduced knowledge-based, template-free segmentation algorithm enables automatic, robust localization of HRIM catheters in VFSS images despite complex backgrounds and rapid motion.

Clinical Implications

This framework facilitates accurate spatial registration between HRIM and VFSS, potentially allowing clinicians to perform manometric region delineation directly on VFSS frames. By automating catheter localization, it reduces clinician workload and may improve diagnostic accuracy in dysphagia management for head and neck cancer patients. The approach’s robustness to challenging imaging conditions supports broader clinical applicability without extensive annotated datasets.

Conclusion

The proposed knowledge-based segmentation algorithm represents a significant advancement in integrating HRIM and VFSS modalities by enabling reliable catheter detection in complex fluoroscopy images. This innovation may enhance dysphagia assessment and streamline clinical workflows in head and neck cancer care.

References

1. GLOBOCAN 2022 -- Global Cancer Statistics
2. Wang et al. 2020 -- HRNet for Catheter Segmentation
3. Jell et al. 2022 -- Template-Matching for HRIM Catheter Localization
4. Geiger et al. 2023 -- Two-Stage Template Matching in VFSS