Automatic Liver Segmentation Using an Adversarial Image-to-Image Network View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2017-09-04

AUTHORS

Dong Yang , Daguang Xu , S. Kevin Zhou , Bogdan Georgescu , Mingqing Chen , Sasa Grbic , Dimitris Metaxas , Dorin Comaniciu

ABSTRACT

Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment. However, it is still a very challenging task due to the complex background, fuzzy boundary, and various appearance of liver. In this paper, we propose an automatic and efficient algorithm to segment liver from 3D CT volumes. A deep image-to-image network (DI2IN) is first deployed to generate the liver segmentation, employing a convolutional encoder-decoder architecture combined with multi-level feature concatenation and deep supervision. Then an adversarial network is utilized during training process to discriminate the output of DI2IN from ground truth, which further boosts the performance of DI2IN. The proposed method is trained on an annotated dataset of 1000 CT volumes with various different scanning protocols (e.g., contrast and non-contrast, various resolution and position) and large variations in populations (e.g., ages and pathology). Our approach outperforms the state-of-the-art solutions in terms of segmentation accuracy and computing efficiency. More... »

PAGES

507-515

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-66179-7_58

DOI

http://dx.doi.org/10.1007/978-3-319-66179-7_58

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1091428914


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