Impact of reconstruction kernel variability on segmentation consistency in low-dose thoracic CT.

Abstract

Structural thoracic segmentation with computed tomography (CT) has become a ubiquitous step in medical image processing. Several tools-including TotalSegmentator-now provide efficient, open methods for labeling regions of interest (ROI) in CT. However, variability in CT reconstruction kernels, especially between soft and hard kernels, can affect image characteristics and potentially impact automatic segmentation, downstream analysis, and clinical decisions. Yet, this variability and its impact on downstream analysis have not been fully investigated in a systematic way. To address this, we examine the consistency of thoracic region segmentations produced by TotalSegmentator when applied to 9,529 CT images reconstructed with paired soft and hard kernels from the National Lung Screening Trial (NLST) dataset. It includes 9,529 CT scan pairs from 9,519 subjects (5,733 men, 3,796 women; ages 43-74), with each subject contributing one scan session, including data from multiple vendors (Siemens, Philips and GE Medical Systems) and reconstruction kernels, drawn from the Lung Screening Study (LSS) (n=6,794), American College of Radiology Imaging Network (ACRIN) with biomarkers (n=1,488), and ACRIN without biomarkers (n=1,247), with 10 duplicated subjects treated as separate individuals. We quantified kernel-induced variability using Dice similarity coefficient, volumetric and intensity differences across 84 thoracic regions. We evaluated the relationship between the average and difference of volumes, Hounsfield Units (HU) and standard deviation of HU of ROI. Additionally, we assessed manufacturer-specific differences in segmentation consistency between Philips, Siemens and GE Medical Systems. Graphical representations with Bland-Altman plots show that some thoracic regions (e.g. heart) are more robust to kernel variation than others (e.g. thyroid gland), and Siemens scanners show the lowest segmentation variability between kernel types. These findings provide early insights into when kernel-induced variability affects automatic segmentation performance differently across the body in clinical and research workflows, therefore emphasizing the need for consistent CT reconstruction or kernel harmonization.