Abstract
The diagnostic quantification of thyroid gland, mostly based on its volume, is commonly done by ultrasound. Typically, three orthogonal length measurements on 2D images are used to estimate the thyroid volume from an ellipsoid approximation, which may vary substantially from its true shape. In this work, we propose a more accurate direct volume determination using 3D reconstructions from two freehand clips in transverse and sagittal directions. A deep learning based trajectory estimation on individual clips is followed by an image-based 3D model optimization of the overlapping transverse and sagittal image data. The image data and automatic thyroid segmentation are then reconstructed and compared in 3D space. The algorithm is tested on 200 pairs of sweeps, and shows that it can provide fully automated, but also more accurate and consistent volume estimations than the standard ellipsoid method, with a median volume error of \(11\%\).
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Wein, W. et al. (2020). Three-Dimensional Thyroid Assessment from Untracked 2D Ultrasound Clips. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12263. Springer, Cham. https://doi.org/10.1007/978-3-030-59716-0_49
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DOI: https://doi.org/10.1007/978-3-030-59716-0_49
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