# Reproducibility of a semiautomatic lobar lung tissue assignment technique on noncontrast CT scans: a study on swine animal model

## TL;DR

This study shows a reliable method for dividing lung tissue into lobes using CT scans, which could improve treatment planning for lung diseases.

## Contribution

A semiautomatic vessel-specific minimum cost path technique for reproducible lobar lung segmentation is proposed and validated.

## Key findings

- Lobar mass measurements showed strong correlation (r = 0.99) between acquisitions with low error.
- Interobserver agreement exceeded 0.90 intraclass correlation coefficients.
- The technique demonstrated excellent reproducibility for lung lobar segmentation on noncontrast CT scans.

## Abstract

To evaluate the reproducibility of a vessel-specific minimum cost path (MCP) technique used for lobar segmentation on noncontrast computed tomography (CT).

Sixteen Yorkshire swine (49.9 ± 4.7 kg, mean ± standard deviation) underwent a total of 46 noncontrast helical CT scans from November 2020 to May 2022 using a 320-slice scanner. A semiautomatic algorithm was employed by three readers to segment the lung tissue and pulmonary arterial tree. The centerline of the arterial tree was extracted and partitioned into six subtrees for lobar assignment. The MCP technique was implemented to assign lobar territories by assigning lung tissue voxels to the nearest arterial tree segment. MCP-derived lobar mass and volume were then compared between two acquisitions, using linear regression, root mean square error (RMSE), and paired sample t-tests. An interobserver and intraobserver analysis of the lobar measurements was also performed.

The average whole lung mass and volume was 663.7 ± 103.7 g and 1,444.22 ± 309.1 mL, respectively. The lobar mass measurements from the initial (MLobe1) and subsequent (MLobe2) acquisitions were correlated by MLobe1 = 0.99 MLobe2 + 1.76 (r = 0.99, p = 0.120, RMSE = 7.99 g). The lobar volume measurements from the initial (VLobe1) and subsequent (VLobe2) acquisitions were correlated by VLobe1 = 0.98VLobe2 + 2.66 (r = 0.99, p = 0.160, RSME = 15.26 mL).

The lobar mass and volume measurements showed excellent reproducibility through a vessel-specific assignment technique. This technique may serve for automated lung lobar segmentation, facilitating clinical regional pulmonary analysis.

Assessment of lobar mass or volume in the lung lobes using noncontrast CT may allow for efficient region-specific treatment strategies for diseases such as pulmonary embolism and chronic thromboembolic pulmonary hypertension.

• Lobar segmentation is essential for precise disease assessment and treatment planning.

• Current methods for segmentation using fissure lines are problematic.

• The minimum-cost-path technique here is proposed and a swine model showed excellent reproducibility for lobar mass measurements.

• Interobserver agreement was excellent, with intraclass correlation coefficients greater than 0.90.