LLASN: Lung Lobes Segmentation that Uses Adversarial Network

The segmentation of the lung lobes is generally a prerequisite for some specific diagnostic tasks and even more so for the planning of the therapy of lung diseases. Recently this task has been tackled with the help of deep learning and more particularly by using U-Net networks. However, these networks have great difficulty in segmenting the lobes because the lung is extremely heterogeneous and the fissures between lobes are extremely fine and therefore blurred in the images. This leads to relatively inaccurate segmentation results, especially in terms of spatial continuity over the 2D lung. Inspired by the adversarial networks in medical image segmentation, we propose in this paper a new segmentation scheme: the lung lobes adversarial based segmentation network (LLASN). In this scheme U-Net is used to generate segmentation results and a discriminator network is used to discriminate the generated segmentation results from Ground Truth labels. The proposed method is evaluated on the LUNA16 dataset and the Tianchi Medical AI Competition dataset. Compared to the classical U-Net network, the proposed network improved Dice similarity coefficient from 0.82 to 0.84, 0.83 to 0.84, and 0.59 to 0.64 for left lower lobe, left upper lobe, and right middle lobe in LUNA16 dataset.