Purpose Lung tissue and lung excursion segmentation in thoracic dynamic magnetic resonance imaging (dMRI) is a critical step for quantitative analysis of thoracic structure and function in patients with respiratory disorders such as Thoracic Insufficiency Syndrome (TIS). However, the complex variability of intensity and shape of anatomical structures and the low contrast between the lung and surrounding tissue in MR images seriously hamper the accuracy and robustness of automatic segmentation methods. In this paper, we develop an interactive deep-learning based segmentation system to solve this problem.

中文翻译：

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基于形状引导卷积神经网络的胸部动态 MRI 中肺组织和肺偏移的交互式分割

目的胸部动态磁共振成像（dMRI）中的肺组织和肺偏移分割是定量分析胸廓功能不全综合征（TIS）等呼吸系统疾病患者的胸部结构和功能的关键步骤。然而，MR图像中解剖结构强度和形状的复杂变化以及肺部和周围组织之间的低对比度严重影响了自动分割方法的准确性和鲁棒性。在本文中，我们开发了一种基于交互式深度学习的分割系统来解决这个问题。