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

ABSTRACT: 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 det

SUBMITTER: Chlebus G 

PROVIDER: S-EPMC6195599 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature