The automatic lung lobe segmentation algorithm is of great significance for the diagnosis and treatment of lung diseases, however, which has great challenges due to the incompleteness of pulmonary fissures in lung CT images and the large variability of pathological features. Therefore, we propose a new automatic lung lobe segmentation framework, in which we urge the model to pay attention to the area around the pulmonary fissure during the training process, which is realized by a task-specific loss function. In addition, we introduce an end-to-end pulmonary fissure generation method in the auxiliary pulmonary fissure segmentation task, without any additional network branch. Finally, we propose a registration-based loss function to alleviate the convergence difficulty of the Dice loss supervised pulmonary fissure segmentation task. We achieve 97.83% and 94.75% dice scores on our private dataset STLB and public LUNA16 dataset respectively.

翻译：自动肺叶分割算法对肺部疾病的诊断与治疗具有重要意义，然而由于肺部CT图像中肺裂的不完整性以及病理特征的高度变异性，该任务面临巨大挑战。为此，我们提出一种新型自动肺叶分割框架，通过在训练过程中引导模型聚焦肺裂周边区域——这一目标由特定任务损失函数实现。此外，我们在辅助肺裂分割任务中引入端到端的肺裂生成方法，无需额外网络分支。最后，我们提出基于配准的损失函数，以缓解经Dice损失监督的肺裂分割任务收敛困难的问题。本方法在私有数据集STLB和公开数据集LUNA16上分别达到97.83%和94.75%的Dice系数。