Abstract
Pulmonary segment segmentation is crucial for cancer localization and surgical planning. However, the pixel-wise annotation of pulmonary segments is laborious, as the boundaries between segments are indistinguishable in medical images. To this end, we propose a weakly supervised learning (WSL) method, termed Anatomy-Hierarchy Supervised Learning (AHSL), which consults the precise clinical anatomical definition of pulmonary segments to perform pulmonary segment segmentation. Since pulmonary segments reside within the lobes and are determined by the bronchovascular tree, i.e., artery, airway and vein, the design of the loss function is founded on two principles. First, segment-level labels are utilized to directly supervise the output of the pulmonary segments, ensuring that they accurately encompass the appropriate bronchovascular tree. Second, lobe-level supervision indirectly oversees the pulmonary segment, ensuring their inclusion within the corresponding lobe. Besides, we introduce a two-stage segmentation strategy that incorporates bronchovascular priori information. Furthermore, a consistency loss is proposed to enhance the smoothness of segment boundaries, along with an evaluation metric designed to measure the smoothness of pulmonary segment boundaries. Visual inspection and evaluation metrics from experiments conducted on a private dataset demonstrate the effectiveness of our method.
Abstract (translated)
肺段分割对于癌症定位和手术规划至关重要。然而，由于医学图像中各肺段之间的边界不明显，对其进行逐像素标注是一项费时的工作。为此，我们提出了一种弱监督学习（WSL）方法，称为解剖层次监督学习（AHSL），该方法利用精确的临床解剖定义来执行肺段分割。鉴于肺段位于肺叶内，并由支气管血管树（即动脉、气道和静脉）决定，损失函数的设计基于两个原则。首先，使用段级标签直接监管输出的肺段结果，确保其准确地包含相应的支气管血管结构。其次，通过肺叶级别的监督间接监管各肺段，确保它们位于对应的肺叶内。 此外，我们还引入了一种两阶段分割策略，该策略结合了支气管血管先验信息。另外，提出了一致性损失函数以增强肺段边界的平滑度，并设计了一个评估指标来衡量肺段边界平滑度的大小。通过在私有数据集上进行实验并使用视觉检查和评估指标，证明了我们方法的有效性。
URL
https://arxiv.org/abs/2505.13911
