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Automatic liver segmentation in 3D medical images is essential in manyclinical applications, such as pathological diagnosis of hepatic diseases,surgical planning, and postoperative assessment. However, it is still a verychallenging task due to the complex background, fuzzy boundary, and variousappearance of liver. In this paper, we propose an automatic and efficientalgorithm to segment liver from 3D CT volumes. A deep image-to-image network(DI2IN) is first deployed to generate the liver segmentation, employing aconvolutional encoder-decoder architecture combined with multi-level featureconcatenation and deep supervision. Then an adversarial network is utilizedduring training process to discriminate the output of DI2IN from ground truth,which further boosts the performance of DI2IN. The proposed method is trainedon an annotated dataset of 1000 CT volumes with various different scanningprotocols (e.g., contrast and non-contrast, various resolution and position)and large variations in populations (e.g., ages and pathology). Our approachoutperforms the state-of-the-art solutions in terms of segmentation accuracyand computing efficiency.

Automatic Liver Segmentation Using an Adversarial Image-to-Image Network