Medical Informatics Laboratory, Yaroslav-the-Wise Novgorod State University, 41 B. St. Petersburgskaya, Veliky Novgorod 173003, Russia.
Tomography. 2024 Oct 9;10(10):1645-1664. doi: 10.3390/tomography10100121.
Both lung lobe segmentation and lung fissure segmentation are useful in the clinical diagnosis and evaluation of lung disease. It is often of clinical interest to quantify each lobe separately because many diseases are associated with specific lobes. Fissure segmentation is important for a significant proportion of lung lobe segmentation methods, as well as for assessing fissure completeness, since there is an increasing requirement for the quantification of fissure integrity.
We propose a method for the fully automatic segmentation of pulmonary fissures on lung computed tomography (CT) based on U-Net and PAN models using a Derivative of Stick (DoS) filter for data preprocessing. Model ensembling is also used to improve prediction accuracy.
Our method achieved an F1 score of 0.916 for right-lung fissures and 0.933 for left-lung fissures, which are significantly higher than the standalone DoS results (0.724 and 0.666, respectively). We also performed lung lobe segmentation using fissure segmentation. The lobe segmentation algorithm shows results close to those of state-of-the-art methods, with an average Dice score of 0.989.
The proposed method segments pulmonary fissures efficiently and have low memory requirements, which makes it suitable for further research in this field involving rapid experimentation.
肺叶分割和肺裂分割在肺部疾病的临床诊断和评估中都很有用。由于许多疾病与特定的肺叶有关，因此分别量化每个肺叶通常具有临床意义。裂分割对于很大一部分肺叶分割方法都很重要，同时对于评估裂的完整性也很重要，因为越来越需要对裂的完整性进行量化。
我们提出了一种基于 U-Net 和 PAN 模型的肺 CT 全自动肺裂分割方法，使用导数 Stick（DoS）滤波器进行数据预处理。还使用模型集成来提高预测准确性。
我们的方法在右肺裂和左肺裂的 F1 得分为 0.916 和 0.933，明显高于独立的 DoS 结果（分别为 0.724 和 0.666）。我们还使用裂分割进行了肺叶分割。叶段分割算法的结果接近最先进的方法，平均 Dice 得分达到 0.989。
所提出的方法能够有效地分割肺裂，且内存需求低，这使其适合涉及快速实验的该领域的进一步研究。
