Deep learning-based bronchial tree-guided semi-automatic segmentation of pulmonary segments in computed tomography images

Abstract

Background: Pulmonary segments are valuable because they can provide more precise localization and intricate details of lung cancer than lung lobes. With advances in precision therapy, there is an increasing demand for the identification and visualization of pulmonary segments in computed tomography (CT) images to aid in the precise treatment of lung cancer. This study aimed to integrate multiple deep-learning models to accurately segment pulmonary segments in CT images using a bronchial tree (BT)-based approach.

Methods: The proposed segmentation method for pulmonary segments using the BT-based approach comprised the following five essential steps: (I) segmentation of the lung using a U-Net (R231) (public access) model; (II) segmentation of the lobes using a V-Net (self-developed) model; (III) segmentation of the airway using a combination of a differential geometric approach method and a BronchiNet (public access) model; (IV) labeling of the BT branches based on anatomical position; and (V) segmentation of the pulmonary segments based on the distance of each voxel to the labeled BT branches. This five-step process was applied to 14 high-resolution breath-hold CT images and compared against manual segmentations for evaluation.

Results: For the lung segmentation, the lung mask had a mean dice similarity coefficient (DSC) of 0.98±0.03. For the lobe segmentation, the V-Net model had a mean DSC of 0.94±0.06. For the airway segmentation, the average total length of the segmented airway trees per image scan was 1,902.8±502.1 mm, and the average number of the maximum airway tree generations was 8.5±1.3. For the segmentation of the pulmonary segments, the proposed method had a DSC of 0.73±0.11 and a mean surface distance of 6.1±2.9 mm.

Conclusions: This study demonstrated the feasibility of combining multiple deep-learning models for the auxiliary segmentation of pulmonary segments on CT images using a BT-based approach. The results highlighted the potential of the BT-based method for the semi-automatic segmentation of the pulmonary segment.

Keywords: Lung cancer; airway segmentation; lobe segmentation; lung segmentation; pulmonary segments segmentation.

Figure 1

The workflow for generating the pulmonary segments using the bronchial tree-based method. The U-Net R231 and BronchiNet models (which are publicly accessible) from other published studies were used without any modifications. The (self-developed) V-Net model was developed for this study.

Figure 2

The five steps of the bronchial tree branches labeling in the right lung. (A) The airway mask. (B) The outcome of the skeletonization of the airway mask. (C) The identification of the main bronchi of the right lung. (D) The consequence of the recognition of the represented branches in the right upper lobe. (E) The result obtained from the classification of the remaining branches to the represented branches. (F) The culminating result of the bronchial tree for the right lung, having executed steps (D) and (E) on the other lobes. In this figure, the X-axis represents the left-right direction, the Y-axis denotes the anterior-posterior direction, and the Z-axis indicates the superior-inferior direction.

Figure 3

The disposition of the representative branch airway within their respective lobes. The red lines represent the representative branch airway, while the dash lines outline the boundary of the segments in the lobes, and the large black dots indicate the dividing nodes. It should be noted that the superior segments of both the right and left lower lobes has been omitted from the figure to avoid confusion. Each representative branch airway can be distinguished based on the position of its endpoints, which have the highest or lowest value along the X-, Y-, or Z-axis. In this figure, the X-axis represents the left-right direction, the Y-axis denotes the anterior-posterior direction, and the Z-axis indicates the superior-inferior direction. RUL, right upper lobe; RML, right middle lobe; RLL, right lower lobe; LUL, left upper lobe; LLL, left lower lobe.

Figure 4

A variant example of the airway distribution in the left upper lobe with airway branch auto-identification (A) and manual correction (B).

Figure 5

An example of the bronchial tree distribution in the left lung without (A) and with (B) the virtual branch.

Figure 6

The coronal view of a patient with the CT image (A), lung masks (B), and lobe masks (C). CT, computed tomography.

Figure 7

The average airway tree length of the 18 segments. The median length is shown as the line inside the box. The bottom and top edges of the box indicate the 25th and 75th percentiles, respectively.

Figure 8

The comparisons of the segmentation for two selected scans (the rows), presented in the sagittal view to display as many segments on each slice as possible. The first row depicts the left lung, while the second row depicts the right lung. (A,B) The CT images with manual segmentation. (C,D) The CT images with BT-based segmentation. The choice of slices ensures that a maximum number of pulmonary segments are visible in each image. CT, computed tomography; BT, bronchial tree.

Figure 9

The evaluation of the semi-automatic bronchial tree-based method for each segment. The median value is shown as the line inside the box. The bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. (A) The DSC for each segment; (B) the MSD for each segment. DSC, dice similarity coefficient; MSD, mean surface distance.

Figure 10

The correlation between the airway length and the DSC value of the segments. DSC, dice similarity coefficient.

Figure 11

The results of the segmentation for the LUL_lingular_sup and LUL_lingular_inf segments, both with and without the virtual branch, are compared to the manual segmentation. 3D view screenshots of the image are provided from different angles to enhance the visibility of the bronchial branches and the boundary between the two pulmonary segments. (A,D) The BT-based method without the virtual branch. (B,E) The BT-based method with the virtual branch. (C,F) The manual segmentation with the virtual branch. LUL, left upper lobe; LUL_lingular_sup, superior of the LUL lingular; LUL_lingular_ inf, inferior of the LUL lingular; LUL, left upper lobe; 3D, three-dimensional; BT, bronchial tree.