Two-stage GAN for field-of-view standardisation and tongue region enhancement in ultrasound for cleft palate speech pattern analysis

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Al Ani, Saja and Cleland, Joanne and Zoha, Ahmed; Su, Ruidan and Zhang, Yudong and Frangi, Alejandro F., eds. (2026) Two-stage GAN for field-of-view standardisation and tongue region enhancement in ultrasound for cleft palate speech pattern analysis. In: Proceedings of 2025 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2025). Lecture Notes in Electrical Engineering . Springer, GBR, pp. 85-95. ISBN 9789819574254 (https://doi.org/10.1007/978-981-95-7425-4_8)

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Abstract

Ultrasound tongue imaging (UTI) offers a non-invasive, real-time view of tongue motion, but variability in the field of view (FoV) and distracting background anatomy hinder automated analysis. We present a two-stage generative pipeline based on a Pix2Pix conditional generative adversarial network (cGAN) to standardise and refine UTI frames. Stage 1 harmonises FoV by converting wide-angle frames to a uniform 97° view, and Stage 2 enhances the region of interest (ROI) by isolating tongue structures. Stage 1 achieved near-lossless fidelity (SSIM = 0.96, PSNR = 88.5 dB), while Stage 2 preserved structure (SSIM = 0.91, PSNR = 33.2 dB). In binary classification of cleft palate ± lip versus typically developing (TD) children, FoV standardisation reached 98.8% accuracy with mixed real–synthetic training, and ROI refinement increased recall to 97.8%. Cross-domain tests showed Stage 1 outputs were indistinguishable from real data, and mixed training mitigated Stage 2 shifts. The framework enhances data consistency, interpretability, and robustness, demonstrating the potential of generative AI for clinical UTI analysis.

ORCID iDs

Al Ani, Saja ORCID logoORCID: https://orcid.org/0009-0001-3703-8040, Cleland, Joanne ORCID logoORCID: https://orcid.org/0000-0002-0660-1646 and Zoha, Ahmed; Su, Ruidan, Zhang, Yudong and Frangi, Alejandro F.
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