Abstract
This paper proposes a method of interactive segmentation of pancreases from abdominal CT images based on the anatomical knowledge of medical doctors as well as the statistical information of pancreases. This method is composed of two phases: training and testing. In the training phase, pancreas regions are manually extracted from sample CT images, and then a probabilistic atlas of pancreases is constructed from the extracted regions. In the testing phase, a medical doctor selects seed voxels for a pancreas and background in a test image. The probabilistic atlas is translated so that the atlas and the seeds are fitted as much as possible. The graph cut technique whose data term is weighted by the probabilistic atlas is applied to the test image. The seed selection, the atlas translation and the graph cut are executed iteratively. This doctor-in-the-loop segmentation method is applied to actual abdominal CT images, and experimental results are shown.
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Suzuki, T., Takizawa, H., Kudo, H., Okada, T. (2016). Interactive Segmentation of Pancreases from Abdominal CT Images by Use of the Graph Cut Technique with Probabilistic Atlases. In: Chen, YW., Torro, C., Tanaka, S., Howlett, R., C. Jain, L. (eds) Innovation in Medicine and Healthcare 2015. Smart Innovation, Systems and Technologies, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-319-23024-5_52
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DOI: https://doi.org/10.1007/978-3-319-23024-5_52
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