Artificial intelligence-based pulmonary vessel segmentation: an opportunity for automated three-dimensional planning of lung segmentectomy

TL;DR

This paper presents an AI method for automatically segmenting pulmonary vessels in CT scans, which can help surgeons plan lung surgeries more efficiently.

Contribution

The study introduces a fully automated AI-based method for segmenting pulmonary arteries and veins in both lungs using nnU-Net models.

Findings

The AI model achieved high Dice scores (0.91–0.92) for pulmonary vessel segmentation in CT scans.

Automatic segmentation reduced the time from 1.5 hours to under 5 minutes.

The method was validated for clinical applicability in lung segmentectomy procedures.

Abstract

This study aimed to develop an automated method for pulmonary artery and vein segmentation in both left and right lungs from computed tomography (CT) images using artificial intelligence (AI). The segmentations were evaluated using PulmoSR software, which provides 3D visualizations of patient-specific anatomy, potentially enhancing a surgeon’s understanding of the lung structure.

A dataset of 125 CT scans from lung segmentectomy patients at Erasmus MC was used. Manual annotations for pulmonary arteries and veins were created with 3D Slicer. nnU-Net models were trained for both lungs, assessed using Dice score, sensitivity and specificity. Intraoperative recordings demonstrated clinical applicability. A paired t-test evaluated statistical significance of the differences between automatic and manual segmentations.

The nnU-Net model, trained at full 3D resolution, achieved a mean Dice score between 0.91 and 0.92. The mean sensitivity and specificity were: left artery: 0.86 and 0.99, right artery: 0.84 and 0.99, left vein: 0.85 and 0.99, right vein: 0.85 and 0.99. The automatic method reduced segmentation time from ∼1.5 hours to under 5 minutes. Five cases were evaluated to demonstrate how the segmentations support lung segmentectomy procedures. P-values for Dice scores were all below 0.01, indicating statistical significance.

The nnU-Net models successfully performed automatic segmentation of pulmonary arteries and veins in both lungs. When integrated with visualization tools, these automatic segmentations can enhance preoperative and intraoperative planning by providing detailed 3D views of patients anatomy.

According to the World Health Organization (WHO), lung cancer is the leading cause of cancer-related death worldwide, with an estimated 2 million deaths in 2023 [1].

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