Automatic liver segmentation from CT volumes is a crucial prerequisite yet
challenging task for computer-aided hepatic disease diagnosis and treatment. In
this paper, we present a novel 3D deeply supervised network (3D DSN) to address
this challenging task. The proposed 3D DSN takes advantage of a fully
convolutional architecture which performs efficient end-to-end learning and
inference. More importantly, we introduce a deep supervision mechanism during
the learning process to combat potential optimization difficulties, and thus
the model can acquire a much faster convergence rate and more powerful
discrimination capability. On top of the high-quality score map produced by the
3D DSN, a conditional random field model is further employed to obtain refined
segmentation results. We evaluated our framework on the public MICCAI-SLiver07
dataset. Extensive experiments demonstrated that our method achieves
competitive segmentation results to state-of-the-art approaches with a much
faster processing speed.

本文提出了一个新颖的 3D 深度监督网络（3D DSN）并引入深度监督机制来解决自动肝脏分割的挑战性任务，并且采用条件随机场模型来获得更精细的分割结果。在公共数据集上进行的广泛实验证明，我们的方法可以实现与最先进方法竞争的分割结果，并且具有更快的处理速度。