What question did this study set out to answer?

The aim is to use four-dimensional MRI and active learning for automatic segmentation of the upper airway during breathing.

February 2, 2026Open Access

Analysis of Upper Airway Morphology Using Four‐Dimensional Dynamic MRI With Active Deep Learning‐Based Automatic Segmentation

Key Points

The aim is to use four-dimensional MRI and active learning for automatic segmentation of the upper airway during breathing.
Conducted a prospective cross-sectional study with 84 adults.
Used active-learning nnU-Net for automatic segmentation of MRI images.
Analyzed dynamic changes in airway morphology under open- and closed-mouth positions.
Computed total airway length, cross-sectional area, and volume; compared metrics across groups.
Achieved a segmentation accuracy of Dice coefficient 0.959 ± 0.019.
Open-mouth breathing significantly lengthened total airway length and reduced retropalatal cross-sectional area.
Males had larger airway volumes than females under both conditions.
Symptomatic individuals exhibited longer airways and greater variability in airway dimensions.

Abstract

ABSTRACT Background Upper‐airway morphology changes during breathing can be captured with cine 4D MRI. Active‐learning nnU‐Net reduces manual labeling while maintaining accuracy. Purpose For automatic upper airway segmentation on free‐breathing cine 4D MRI using active learning and quantifying dynamic changes under two mouth positions. Study Type Prospective cross‐sectional study. Population Eighty‐four OSA (obstructive sleep apnea)‐free adults (28 M/56F; 18–80 years; 33 with sleep‐related breathing symptoms). Segmentation performance was evaluated on an internal test set ( n = 18). Fieldstrength/Sequence 3T, free‐breathing time‐resolved imaging with interleaved stochastic trajectories (TWIST) sequence under closed‐ and open‐mouth positions. Assessment Manual annotations by a technologist (radiologist‐verified) served as reference standard and training labels for an active‐learning nnU‐Net (68 training; four fixed validation). Total airway length, cross‐sectional area (CSA), and total airway volume were computed at each anatomical level and compared across mouth positions, sex, and sleep‐related symptom status, and independent predictors were identified. Statistical Tests Paired/unpaired t or Mann–Whitney U test (two‐sided p = 0.05). Predictor selection by 10‐fold LASSO; effects estimated via ordinary least squares with cluster‐robust standard errors. Results Segmentation achieved a dice 0.959 ± 0.019 (test set). Open‐mouth breathing significantly lengthened the total airway (7.92 ± 1.07 vs. 7.41 ± 0.93 cm) and reduced retropalatal CSA (1.51 ± 0.68 vs. 1.80 ± 0.69 cm 2 ). Coefficients of variation (CVs) for CSA and volume were significantly higher with 20‐s open‐mouth breathing. Males ( n = 28) exhibited significantly larger airway volumes than females (closed 27.94 ± 4.87 vs. 19.82 ± 3.26 cm 3 ; open 30.26 ± 5.94 vs. 20.94 ± 3.85 cm 3 ). Symptomatic individuals ( n = 33) had significantly longer airways (closed 7.96 ± 0.96 vs. 7.04 ± 0.70 cm; open 8.54 ± 1.01 vs. 7.52 ± 0.91 cm), narrower open‐mouth retropalatal CSA (1.24 ± 0.51 vs. 1.68 ± 0.72 cm 2 ), and greater retropalatal CSA dynamic variability. Multivariable regression confirmed mouth position, symptoms, and sex as independent predictors. Data Conclusion Four‐dimensional cine MRI with active‐learning nnU‐Net can automatically quantify dynamic upper airway morphology, demonstrating systematic differences and dynamic variability. Evidence Level 2. Technical Efficacy Stage 2.

Analysis of Upper Airway Morphology Using Four‐Dimensional Dynamic MRI With Active Deep Learning‐Based Automatic Segmentation

Key Points

Abstract

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