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Accurate liver and lesion segmentation from computed tomography (CT) imagesare highly demanded in clinical practice for assisting the diagnosis andassessment of hepatic tumor disease. However, automatic liver and lesionsegmentation from contrast-enhanced CT volumes is extremely challenging due tothe diversity in contrast, resolution, and quality of images. Previous methodsbased on UNet for 2D slice-by-slice or 3D volume-by-volume segmentation eitherlack sufficient spatial contexts or suffer from high GPU computational cost,which limits the performance. To tackle these issues, we propose a novelcontext-aware PolyUNet for accurate liver and lesion segmentation. It jointlyexplores structural diversity and consecutive t-adjacent slices to enrichfeature expressive power and spatial contextual information while avoiding theoverload of GPU memory consumption. In addition, we utilize zoom out/in andtwo-stage refinement strategy to exclude the irrelevant contexts and focus onthe specific region for the fine-grained segmentation. Our method achieved verycompetitive performance at the MICCAI 2017 Liver Tumor Segmentation (LiTS)Challenge among all tasks with a single model and ranked the $3^{rd}$,$12^{th}$, $2^{nd}$, and $5^{th}$ places in the liver segmentation, lesionsegmentation, lesion detection, and tumor burden estimation, respectively.

Context-aware PolyUNet for Liver and Lesion Segmentation from Abdominal CT Images