Abstract
Currently, multi-organ segmentation (MOS) in abdominal CT can fail to handle clinical patient population with missing organs due to surgical resection. In order to enable the state-of-the-art MOS for these clinically important cases, we propose 1) automatic missing organ detection (MOD) by testing abnormality of post-surgical organ motion and organ-specific intensity homogeneity, and 2) atlas-based MOS of 10 abdominal organs that handles missing organs automatically. The proposed methods are validated with 44 abdominal CT scans including 9 diseased cases with surgical organ resections, resulting in 93.3% accuracy for MOD and improved overall segmentation accuracy by the proposed MOS method when tested on difficult diseased cases.
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Suzuki, M., Linguraru, M.G., Okada, K. (2012). Multi-Organ Segmentation with Missing Organs in Abdominal CT Images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33454-2_52
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DOI: https://doi.org/10.1007/978-3-642-33454-2_52
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