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

: Chlebus, G.; Schenk, A.; Moltz, J.H.; Ginneken, B. van; Hahn, H.K.; Meine, H.

Volltext ()

Scientific Reports 8 (2018), Art.15497, 7 S.
ISSN: 2045-2322
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer MEVIS ()

Automatic liver tumor segmentation would have a big impact on liver therapy planning procedures and follow-up assessment, thanks to standardization and incorporation of full volumetric information. In this work, we develop a fully automatic method for liver tumor segmentation in CT images based on a 2D fully convolutional neural network with an object-based postprocessing step. We describe our experiments on the LiTS challenge training data set and evaluate segmentation and detection performance. Our proposed design cascading two models working on voxel- and object-level allowed for a significant reduction of false positive findings by 85% when compared with the raw neural network output. In comparison with the human performance, our approach achieves a similar segmentation quality for detected tumors (mean Dice 0.69 vs. 0.72), but is inferior in the detection performance (recall 63% vs. 92%). Finally, we describe how we participated in the LiTS challenge and achieved state-of-the-art performance.