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- W2164984476 abstract "An automatic method for lung nodule segmentation from computed tomography (CT) data is presented that is different from previous work in several respects. Firstly, it is supervised; it learns how to obtain a reliable segmentation from examples in a training phase. Secondly, the method provides a soft, or probabilistic segmentation, thus taking into account the uncertainty inherent in this segmentation task. The method is trained and tested on a public data set of 23 nodules for which soft labelings are available. The new method is shown to outperform a previously published conventional method. By merely changing the training data, non-solid nodules can also be segmented." @default.
- W2164984476 created "2016-06-24" @default.
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- W2164984476 date "2006-01-01" @default.
- W2164984476 modified "2023-09-26" @default.
- W2164984476 title "Supervised Probabilistic Segmentation of Pulmonary Nodules in CT Scans" @default.
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- W2164984476 doi "https://doi.org/10.1007/11866763_112" @default.
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