Automatic lung nodules detection and segmentation can assist doctors in better diagnosis and treatment for lung cancer. However, precise detection and segmentation are still challenging, because lung nodules can have different contours or locations and may be attached to other tissues, such as neighboring blood vessel and pleural surface. In this study, an automatic detection and segmentation method for lung nodules in different locations has been developed. First, we apply Otsu thresholding to segment lung parenchyma. Next, a morphological opening operation is carried out to remove blood vessels. Then α-hull operation is proposed to correct lung contours and optimal α values can be acquired adaptively. Finally, DenseNet convolutional network is applied to classify true lung nodules from all nodule candidates. We select the intersection area of at least three radiologists' annotations as ground truth and validate our method on 466 nodules including well-circumscribed, juxta-vascular, juxta-pleural, and pleural tail. Our study not only concentrates on false positive reduction but also evaluates segmentation performance. To give a more comprehensive quantitative evaluation of nodule segmentation, we use evaluation metrics including Jaccard index (JI), dice similar coefficient (DSC), Hausdorff distance, under-segmentation rate, over-segmentation rate, sensitivity, specificity, accuracy, and false positive rate. Overall results are 0.6385 ± 0.1309, 0.7710 ± 0.1057, 3.5123 ± 3.1251, 0.1769 ± 0.1308, 0.1848 ± 0.1463, 0.7936 ± 0.1417, 0.9998 ± 0.0003, 0.9997 ± 0.0003, and 0.0002 ± 0.0003, respectively. These metrics can demonstrate segmentation performance in multiple dimensions. As a general and automatic detection and segmentation framework for lung nodules in different locations, this study achieves better performance than previous approaches in JI and DSC.

中文翻译：

---

基于自适应α-hull算法和DenseNet卷积网络的CT图像不同位置肺结节自动检测与分割

自动肺结节检测和分割可以帮助医生更好地诊断和治疗肺癌。然而，精确检测和分割仍然具有挑战性，因为肺结节可能具有不同的轮廓或位置，并且可能附着在其他组织上，例如相邻的血管和胸膜表面。在这项研究中，已经开发了一种针对不同位置的肺结节的自动检测和分割方法。首先，我们应用 Otsu 阈值来分割肺实质。接下来，进行形态学开放操作以去除血管。然后提出α- hull操作来校正肺轮廓和最优α可以自适应地获取值。最后，应用 DenseNet 卷积网络从所有结节候选中对真正的肺结节进行分类。我们选择至少三位放射科医生注释的交叉区域作为基本事实，并在 466 个结节上验证我们的方法，包括边界清楚的、血管旁、胸膜旁和胸膜尾。我们的研究不仅集中在减少误报上，而且还评估了分割性能。为了对结节分割进行更全面的定量评估，我们使用的评估指标包括 Jaccard 指数 (JI)、骰子相似系数 (DSC)、Hausdorff 距离、欠分割率、过分割率、敏感性、特异性、准确性和错误率。阳性率。总体结果为 0.6385 ± 0.1309、0.7710 ± 0.1057、3.5123 ± 3.1251、0.1769 ± 0.1308、0.1848 ± 0。分别为 1463、0.7936 ± 0.1417、0.9998 ± 0.0003、0.9997 ± 0.0003 和 0.0002 ± 0.0003。这些指标可以在多个维度上展示细分性能。作为针对不同位置的肺结节的通用自动检测和分割框架，本研究在 JI 和 DSC 中取得了比以前方法更好的性能。