Abstract
The accurate segmentation of medical images has important consequences in clinical applications. Noisy and artefact-heavy images can result in erroneous image segmentation and often require expert understanding of the target anatomy by clinicians to interpret and compensate for missing and obfuscated data. This is especially true in ultrasound imaging where shadowing and speckle artefacts are common. We propose a novel approach to handle such artefacts using a conditional Generative Adversarial Network called Anatomical GAN (AGAN) that can correct anatomically-invalid pixel-wise segmentation and impose shape priors in carotid artery ultrasound images by learning the underlying structure of the arteries. These anatomically accurate outputs can then be used in the clinical work flow by clinicians or be further processed by other automated methods for assistance in clinical decision making. AGAN can be chained with any pixel-wise segmentation method and is generalisable for both anatomy and artefacts. Experimental results on a longitudinal ultrasound carotid artery dataset show that AGAN can correct anatomically-invalid segmentation masks obtained with different pixel-wise segmentation methods when other state-of-the-art methods fail.
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Engin, M. et al. (2020). AGAN: An Anatomy Corrector Conditional Generative Adversarial Network. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_68
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DOI: https://doi.org/10.1007/978-3-030-59713-9_68
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