Automated CT-Based Quantification of Pulmonary Fibrosis Using Deep Learning-Based Lung Segmentation.
Authors
Affiliations (9)
Affiliations (9)
- Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan.
- Division of Pulmonary and Critical Care, Department of Internal Medicine, China Medical University Hospital, Taichung 404327, Taiwan.
- School of Medicine, College of Medicine, China Medical University, Taichung 406040, Taiwan.
- Department of Life Science, National Chung Hsing University, Taichung 402204, Taiwan.
- Doctoral Program in Translational Medicine, National Chung Hsing University, Taichung 402204, Taiwan.
- Rong Hsing Research Center for Translational Medicine, National Chung Hsing University, Taichung 402204, Taiwan.
- Department of Computer Science & Information Engineering, Asia University, Taichung 413305, Taiwan.
- Center for Precision Medicine Research, Asia University, Taichung 413305, Taiwan.
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 413305, Taiwan.
Abstract
<b>Background/Objectives:</b> To develop and evaluate an automated CT-based framework for the quantitative assessment of fibrotic interstitial lung disease (ILD), including idiopathic pulmonary fibrosis (IPF), using a standardised six-level anatomical protocol and deep-learning lung segmentation. <b>Methods:</b> The segmentation dataset comprised 3315 manually annotated development slices from 92 patients and a non-overlapping internal holdout of 845 slices from 5 patients. A separate 100-study localisation/scoring set yielded a 97-patient agreement cohort (84 IPF, 13 other ILD; 1164 per-level, per-lung observations) after three DICOM-conversion exclusions. YOLO11n-seg masks underwent vessel- and structure-removal fibrosis detection. The radial spatial score was compared with a non-blind expert-adjudicated reference; the per-level Fibrosis Index was an auxiliary read-out. <b>Results:</b> On the five-patient internal segmentation holdout, mean intersection over union (mIoU) was 0.926 ± 0.017; the in-sample development value was approximately 0.95. Model-only latency was 73.8 ± 9.7 ms/slice at batch size 1, and peak throughput was 1.48 ms/slice at batch size 512. In the separate 97-patient agreement cohort, the expert-adjudicated score was identical to the automated score for 967 of 1164 observations (83.1%) and differed for 197 (16.9%). In the modified-score subset, Pearson r was 0.918, mean absolute error was 3.07, and ICC(2,1) was 0.889 (patient-clustered 95% CI 0.828-0.923). The pooled ICC(2,1) was 0.988 (0.982-0.992), but this value was inflated because the 967 unchanged pairs were identical by construction. Sequential end-to-end processing, measured in seven study patients, took a mean of 22.5 s per patient (median 24.0 s, range 17.3-24.9 s); localisation accounted for 88.1% of this time. <b>Conclusions:</b> The framework combined lung segmentation, anatomically standardised sampling, and automated fibrosis scoring. The radial score showed preliminary analytical concordance under non-blind expert adjudication. The fibrosis detector remains a proof-of-concept implementation based on 8-bit windowed images and has not been compared with independently drawn pixel-level fibrosis masks. The radial partition is an exploratory scoring convention and was not compared with alternative partitions or validated against clinical outcomes. Larger external studies using native Hounsfield-unit data and independent blinded readers are required.