Automatic detection and segmentation of lung nodules in different locations from CT images based on adaptive α‐hull algorithm and DenseNet convolutional network. Issue 4 (7th April 2021)

• Record Type: Journal Article
• Authors: Zhang, Xiaofang; Li, Suxiao; Zhang, Bin; Dong, Jie; Zhao, Shujun; Liu, Xiaomin
• Is Part Of: International journal of imaging systems and technology. Volume 31:Issue 4(2021)
• Page Start: 1882
• Page End: 1893
• Publication Date: 2021-04-07
• Subjects: contour correction -- CT image -- DenseNet -- lung nodules -- segmentation -- α‐hull; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22580
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: 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.