Harnessing advanced machine learning techniques for microscopic vessel segmentation in pulmonary fibrosis using novel hierarchical phase-contrast tomography (HiP-CT) images
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Background: Fibrotic lung disease is a progressive illness that causes scarring and ultimately respiratory failure, with irreversible damage by the time its diagnosed on computed tomography imaging. Recent research postulates the role of the lung vasculature on the pathogenesis of the disease, and with the recent development of high-resolution hierarchical phase contrast tomography (HiP-CT), we have the potential to understand and detect changes in the lungs long before conventional imaging. However, to gain quantitative insight into vascular changes you first need to be able to segment the vessels before further downstream analysis can be conducted. Aside from this, HiP-CT generates large volume, high resolution data which is time consuming and expensive to label. Objectives: This project aims to qualitatively assess the latest machine learning methods for vessel segmentation in HiP-CT data to enable label propagation as the first step for imaging biomarker discovery, with the goal to identify early-stage interstitial lung disease amenable to treatment, before fibrosis begins. Methods: Semi-supervised learning has become a growing method to tackle sparsely labelled datasets due to its leveraging of unlabelled data. In this study we will compare 2 semi-supervised learning methods; Seg PL, based on pseudo labelling and MisMatch, using consistency regularisation against state of the art supervised learning method, in nnU-Net, on vessel segmentation in sparsely labelled lung HiP-CT data. Results: On initial experimentation, both MisMatch and SegPL showed promising performance on qualitative review. In comparison with supervised learning, both MisMatch and SegPL showed better on out of distribution performance within the same sample (different vessel morphology and texture vessels), though supervised learning provided more consistent segmentations for well represented labels in the limited annotations. Conclusion: Further quantitative research is required to better assess the generalisability of these findings, though they show promising first steps towards leveraging this novel data to tackle fibrotic lung disease.
Date Issued
2025-05-09
Date Acceptance
2025-02-14
Citation
Methods of Information in Medicine: journal of methodology in medical research, information and documentation, 2025, 63 (03/04), pp.97-108
ISSN
0026-1270
Publisher
Thieme Gruppe
Start Page
97
End Page
108
Journal / Book Title
Methods of Information in Medicine: journal of methodology in medical research, information and documentation
Volume
63
Issue
03/04
Copyright Statement
© The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/39965755
Publication Status
Published
Coverage Spatial
Germany
Date Publish Online
2025-02-18