Abstract
Our project aims to help physicians and experts, to diagnose Gougerot-Sjögren’s syndrome, by segmenting salivary glands, on ultrasound images (US). Our database contains 1143 US images of left and right parotid and sub-mandibular glands obtained with a Toshiba Aplio 800 at the CHRU hospital of Brest. This manuscript proposes a method based on a Siamese architecture with a Convolutional Neural Network. To reach our goal with a relatively small database, we train the Siamese network for texture differentiation on a gray-level texture dataset, that can be transferred without any further retraining, to segment salivary glands on a US dataset.
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Fodop, G. et al. (2022). Siamese Network for Salivary Glands Segmentation. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 309. Springer, Singapore. https://doi.org/10.1007/978-981-19-3444-5_39
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DOI: https://doi.org/10.1007/978-981-19-3444-5_39
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