论文标题
具有3D U-NET和优化损耗函数的肺叶自动分割
Automatic segmentation of the pulmonary lobes with a 3D u-net and optimized loss function
论文作者
论文摘要
由于解剖学变异，病理和不完全的裂缝，完全自动的肺叶分割具有挑战性。我们在49个主要可用的数据集上训练了3D U-NET进行肺叶分割的3D U-NET，并引入了加权骰子损失函数，以强调Lobar边界。为了验证所提出的方法的性能，我们将结果与其他两种方法进行了比较。新的损耗函数将平均距离提高到1.46毫米（相比之下，简单损失功能而无需加权）。
Fully-automatic lung lobe segmentation is challenging due to anatomical variations, pathologies, and incomplete fissures. We trained a 3D u-net for pulmonary lobe segmentation on 49 mainly publically available datasets and introduced a weighted Dice loss function to emphasize the lobar boundaries. To validate the performance of the proposed method we compared the results to two other methods. The new loss function improved the mean distance to 1.46 mm (compared to 2.08 mm for simple loss function without weighting).
