Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing

Author:

Chlebus GrzegorzORCID,Schenk Andrea,Moltz Jan Hendrik,van Ginneken Bram,Hahn Horst Karl,Meine Hans

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference24 articles.

1. Forner, A., Llovet, J. M. & Bruix, J. Hepatocellular carcinoma. The Lancet 379, 1245–1255 (2012).

2. Cornelis, F. et al. Precision of manual two-dimensional segmentations of lung and liver metastases and its impact on tumour response assessment using recist 1.1. Eur. Radiol. Exp. 1, 16 (2017).

3. Oliver, J. H. & Baron, R. L. Helical biphasic contrast-enhanced ct of the liver: technique, indications, interpretation, and pitfalls. Radiol. 201, 1–14 (1996).

4. Niessen, W. et al. 3d liver tumor segmentation challenge. https://web.archive.org/web/20140606121659/http://lts08.bigr.nl:80/index.php Accessed: 2017-11-23 (2008).

5. Shimizu, A. et al. Ensemble segmentation using adaboost with application to liver lesion extraction from a ct volume. In Proc. MICCAI Workshop on 3D Segmentation in the Clinic: A Grand Challenge II., NY, USA (2008).

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