Objective:

To develop a knowledge-based segmentation algorithm for automatic localization of HRIM catheters in VFSS images, specifically addressing challenges such as manual delineation difficulties and inter-rater variability among clinicians.

Approach:

Key Findings:

• Head and neck cancer patients often experience oropharyngeal dysphagia, which severely impacts their quality of life.
• Current VFSS analysis relies heavily on clinician interpretation, leading to significant inter-rater variability.
• HRIM provides quantitative swallow assessments but requires challenging manual region delineation, particularly in HNC patients.
• Existing segmentation methods struggle with the dynamic and complex backgrounds of VFSS, complicating accurate catheter localization.

Interpretation:

The proposed knowledge-based segmentation algorithm aims to enhance the accuracy and efficiency of HRIM analysis in conjunction with VFSS, potentially leading to improved dysphagia management outcomes for HNC patients.

Limitations:

• Existing knowledge-based techniques are not easily applicable to VFSS due to fast dynamics and complex backgrounds, which complicate segmentation.
• Previous frameworks relied on template matching, which limits their generalizability across different catheter models and clinical scenarios.

Conclusion:

The development of a robust, knowledge-based segmentation algorithm could significantly streamline the integration of HRIM and VFSS, thereby reducing clinician workload and improving patient outcomes in dysphagia management.

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