Abstract
Most Neural Networks for organ segmentation are trained to recognize the appearance of the organ, without considering the location of the organ in the body. However, a medical expert would include in their reasoning also the context around the organ. In this work, we propose reproducing this human behavior by enhancing the conventional multi-class segmentation pipeline with additional anatomical information. We apply this concept to a ventral organ segmentation model having a vertebrae label map as additional input, and to a vertebrae segmentation model enhanced by ventral organ information. In both cases, our proposed label dependency approach improved the performance of the baseline models: the dice score (DS) of the ventral organ segmentation improved by more than 3.5 % and the vertebrae identification rate by 1.8%.
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© 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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De Benetti, F., Frasch, R., Venegas, L.F.R., Shi, K., Navab, N., Wendler, T. (2023). Enhancing Medical Image Segmentation with Anatomy-aware Label Dependency. In: Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2023. BVM 2023. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-41657-7_12
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DOI: https://doi.org/10.1007/978-3-658-41657-7_12
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