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3D Automated Segmentation of Bronchial Abnormalities on Ultrashort Echo Time MRI: A Quantitative MR Outcome in Cystic
Amel Imene Hadj Bouzid1, Ilyes Benlala1,2,3, Baudouin Denis de Senneville4
1Univ. Bordeaux, INSERM, CRCTB, UMR 1045, Bordeaux, France.
Background:
Cystic fibrosis (CF) monitoring relies on computed tomography (CT), but ultra-short echo time MRI (UTE-MRI) offers a radiation-free alternative. However, its clinical adoption is hindered by the laborious and subjective manual analysis, which prevents standardized quantification of bronchial abnormalities.
Purpose:
To develop a deep learning (DL) system for the segmentation of CF bronchial abnormalities on UTE-MRI and assess clinical relevance in patients undergoing cystic fibrosis transmembrane conductance regulator (CFTR) modulator treatment.
Study Type:
Retrospective.
Population:
One-hundred and sixty-six CF patients were included (age = 23 ± 11, 48% male), comprising a training set (n = 97), a test set (n = 25), and an independent clinical validation cohort (n = 44).
Field Strength/Sequence:
1.5T/UTE-MRI 3D gradient-echo Spiral Volume Interpolated Breath-hold Examination (VIBE) sequence.
Assessment:
The RiSeNet architecture was trained using paired UTE-MRI and CT scans. Its technical performance was evaluated against expert-refined segmentations and compared to state-of-the-art segmentation models using topology-aware metrics: Normalized Surface Dice (NSD) and CenterLine Dice (clDice). Clinical validation was performed by correlating automated measurements at baseline (M0) and 1-year post-CFTR modulator treatment (M12) with Bhalla scores and pulmonary function tests (FEV1%p).
Statistical Tests:
Student's t-test, Mann-Whitney, Wilcoxon, and Chi-square tests were used for group comparisons. The Spearman test was used to assess correlations. A p value < 0.05 was considered statistically significant.
Results:
In the test group, RiSeNet achieved significantly superior performance versus state-of-the-art with NSD scores of 0.84 for bronchiectasis, 0.90 for wall thickening, and 0.75 for mucus; and clDice scores of 0.69, 0.61, and 0.64, respectively. In the clinical validation group, significant correlations with Bhalla (ρ = -0.92/-0.85) and FEV1%p (ρ = -0.68/-0.67) were observed pre/post-CFTR modulator. Post-CFTR modulator, FEV1%p improved (69%-92%) with significant reductions in bronchiectasis (3.88-1.25), wall thickening (30.43-3.05), and mucus (53.30-11.80).
Data Conclusion:
RiSeNet may enable semantic segmentation of CF abnormalities on radiation-free UTE-MRI.
Evidence Level:
3 TECHNICAL EFFICACY: 4.
