A knowledge-based framework for robust segmentation of high-resolution impedance manometry catheters in video-fluoroscopy images.

PURPOSE: Simultaneous high-resolution impedance manometry (HRIM) and videofluoroscoic swallow studies (VFSS) can address important limitations of VFSS. However, clinicians must analyze each modality independently, doubling workload and leaving the challenge of manometric region delineation unresolved. We hypothesize that spatially registering HRIM and VFSS would allow manometric regions to be defined directly on VFSS frames, reducing clinician burden. Achieving this requires reliable detection of the HRIM catheter in VFSS images.

METHODS

We introduce a template-free, knowledge-based algorithm that automatically localizes the catheter centreline in VFSS frames. The method identifies the main visible portion of the catheter in each frame, and then recursively adds catheter segments based on proximity and directional alignment. This approach defines a region-of-interest containing the individual HRIM sensors. The algorithm was validated on two datasets comprising frames from 122 single-swallow VFSS videos of head and neck cancer patients.

RESULTS

The segmentation module achieved 93.8% precision, 83.8% recall, and an F1-score of 88.5% for a 1.77 mm tolerance.

CONCLUSION

The framework demonstrated robust performance across diverse anatomies and imaging conditions, outperforming existing knowledge-based methods. By relying on geometric and directional priors rather than pixel intensities, it delivers consistent, interpretable predictions without requiring large annotated datasets. This algorithm lays the groundwork for manometric region delineation directly on imaging data, and could likely be extended to other clinical applications involving thin radiopaque structures, provided that the pre-processing pipeline is adjusted and the hyperparameters are appropriately fine-tuned.