UNASSIGNED: Our study investigates the potential benefits of incorporating prior anatomical knowledge into a deep learning (DL) method designed for the automated segmentation of lung lobes in chest CT scans.
UNASSIGNED: We introduce an automated DL-based approach that leverages anatomical information from the lung\'s vascular system to guide and enhance the segmentation process. This involves utilizing a lung vessel connectivity (LVC) map, which encodes relevant lung vessel anatomical data. Our study explores the performance of three different neural network architectures within the nnU-Net framework: a standalone U-Net, a multitasking U-Net, and a cascade U-Net.
UNASSIGNED: Experimental findings suggest that the inclusion of LVC information in the DL model can lead to improved segmentation accuracy, particularly, in the challenging boundary regions of expiration chest CT volumes. Furthermore, our study demonstrates the potential for LVC to enhance the model\'s generalization capabilities. Finally, the method\'s robustness is evaluated through the segmentation of lung lobes in 10 cases of COVID-19, demonstrating its applicability in the presence of pulmonary diseases.
UNASSIGNED: Incorporating prior anatomical information, such as LVC, into the DL model shows promise for enhancing segmentation performance, particularly in the boundary regions. However, the extent of this improvement has limitations, prompting further exploration of its practical applicability.

UNASSIGNED: Anatomy-based quantification of emphysema in a lung screening cohort has the potential to improve lung cancer risk stratification and risk communication. Segmenting lung lobes is an essential step in this analysis, but leading lobe segmentation algorithms have not been validated for lung screening computed tomography (CT).
UNASSIGNED: In this work, we develop an automated approach to lobar emphysema quantification and study its association with lung cancer incidence. We combine self-supervised training with level set regularization and finetuning with radiologist annotations on three datasets to develop a lobe segmentation algorithm that is robust for lung screening CT. Using this algorithm, we extract quantitative CT measures for a cohort (n=1189) from the National Lung Screening Trial and analyze the multivariate association with lung cancer incidence.
UNASSIGNED: Our lobe segmentation approach achieved an external validation Dice of 0.93, significantly outperforming a leading algorithm at 0.90 (p<0.01). The percentage of low attenuation volume in the right upper lobe was associated with increased lung cancer incidence (odds ratio: 1.97; 95% CI: [1.06, 3.66]) independent of PLCOm2012 risk factors and diagnosis of whole lung emphysema. Quantitative lobar emphysema improved the goodness-of-fit to lung cancer incidence (χ2=7.48, p=0.02).
UNASSIGNED: We are the first to develop and validate an automated lobe segmentation algorithm that is robust to smoking-related pathology. We discover a quantitative risk factor, lending further evidence that regional emphysema is independently associated with increased lung cancer incidence. The algorithm is provided at https://github.com/MASILab/EmphysemaSeg.

Lobectomy is a curative treatment for localized lung cancer. The study aims to construct an automatic pipeline for segmenting pulmonary lobes before and after lobectomy from CT images.
Six datasets (D1 to D6) of 865 CT scans were collected from two hospitals and public resources. Four nnU-Net-based segmentation models were trained. A lobectomy classification was proposed to automatically recognize the category of the input CT images: before lobectomy or one of five types after lobectomy. Finally, the lobe segmentation before and after lobectomy was realized by integrating the four models and lobectomy classification. The dice similarity coefficient (DSC), 95% Hausdorff distance (HD95) and average symmetric surface distance (ASSD) were used to evaluate the segmentations.
The pre-operative model achieved an average DSC of 0.964, 0.929, 0.934, and 0.891 in the four datasets. In D1 and D2, the average HD95 was 4.18 and 7.74 mm and the average ASSD was 0.86 and 1.32 mm, respectively. The lobectomy classification achieved an accuracy of 100%. After lobectomy, an average DSC of 0.973 and 0.936, an average HD95 of 2.70 and 6.92 mm, an average ASSD of 0.57 and 1.78 mm were obtained in D1 and D2, respectively. The postoperative segmentation pipeline outperformed other counterparts and training strategies.
The proposed pipeline can automatically segment pulmonary lobes before and after lobectomy from CT images and be applied to manage patients with lung cancer after lobectomy.

Segmentation of pulmonary lobes in inspiration and expiration chest CT scan pairs is an important prerequisite for lobe-based quantitative disease assessment. Conventional methods process each CT scan independently, resulting typically in lower segmentation performance at expiration compared to inspiration. To address this issue, we present an approach, which utilizes CT scans at both respiratory states. It consists of two main parts: a base method that processes a single CT scan and an extended method that utilizes the segmentation result obtained on the inspiration scan as a subject-specific prior for segmentation of the expiration scan. We evaluated the methods on a diverse set of 40 CT scan pairs. In addition, we compare the performance of our method to a registration-based approach. On inspiration scans, the base method achieved an average distance error of 0.59, 0.64, and 0.91 mm for the left oblique, right oblique, and right horizontal fissures, respectively, when compared with expert-based reference tracings. On expiration scans, the base method\'s errors were 1.54, 3.24, and 3.34 mm, respectively. In comparison, utilizing proposed subject-specific priors for segmentation of expiration scans allowed decreasing average distance errors to 0.82, 0.79, and 1.04 mm, which represents a significant improvement ([Formula: see text]) compared with all other methods investigated.