Spatiotemporal Distillation via Recurrent Bottlenecks for Aortic Tracking

Dexter Wen Jie Teo; Nairouz Shehata; Herve Lombaert

eess.IVAug 24, 2026

Spatiotemporal Distillation via Recurrent Bottlenecks for Aortic Tracking

Authors: Dexter Wen Jie Teo, Nairouz Shehata, Herve Lombaert

Organizations: College of Computing and Data Science, Nanyang Technological University, Singapore · Department of Computer and Software Engineering, Polytechnique Montreal, Canada

Abstract

Cardiac cine-MRI serves as a direct visual indicator of cardiovascular hemodynamics by capturing the continuous wall motion of the aorta. Quantifying these dynamic structural changes across the cardiac cycle is essential for measuring aortic distensibility, a primary marker of arterial stiffness. However, standard 2D segmentation networks focus on each frame independently. Consequently, when rapid systolic flow temporarily obscures the aorta's boundaries, this lack of continuous context results in frame-to-frame tracking dropouts and boundary inconsistencies. Spatiotemporal ( $2\text{D}+t$ ) networks can enforce temporal consistency across the sequence but suffer from a scarcity of expert annotations. To address this, we present a semi-supervised spatiotemporal ( $2\text{D}$ to $2\text{D}+t$ ) knowledge distillation framework exploiting the cardiac cycle. The framework distills a spatial teacher's expertise into a spatiotemporal student network by executing a dynamic latent interception, pairing a recurrent spatiotemporal bottleneck with a residual spatial bypass. Our model selection strategy applies a baseline validation threshold ( $\text{DSC} \ge 0.50$ ) prior to selecting the epoch that maximizes anatomical consistency. This strategy enables the spatiotemporal student model to achieve superior surface tracking accuracy ( $\text{NSD@1mm} = 92.3\% \pm 0.2\%$ ) and high structural reliability ( $\text{Frac}_{2\text{CC}} = 99.2\% \pm 0.6\%$ ), reducing population-wide structural anomalies by over 56% compared to a 2D nnU-Net baseline.

Figures & tables

Figure 1: Overview of the spatiotemporal student architecture. A dynamic hook routes deep features through a bidirectional ConvLSTM to capture sequence context. This temporal stream merges with a residual spatial bypass ( $\oplus$ ) to preserve tracking continuity and enforce anatomical plausibility ( $N=2$ ) across the cardiac cycle.

Parameter	Symbol / Variable	Configured Value
Teacher Validation Floor	$\tau_{\text{dice}}$	$0.50$ (DSC gatekeeper)
Pseudo-Label Binarization Threshold	$\tau_{\text{bin}}$	$0.50$
Teacher Focal Loss Weight	$\lambda_{\text{focal}}$	$0.50$
Temporal Residual Scaling Factor	$\alpha$	$0.10$
Compressed Bottleneck Width	$C_{\text{temp}}$	$128$ channels
Temporal Smoothness Regularizer	$\beta$	$0.10$

Table 1: Summary of framework hyperparameters and operational thresholds.

Architectural Configuration	$\mathbf{\text{Frac}_{2\text{CC}}}$ (%) $\uparrow$	NSD@1mm (%) $\uparrow$	HD95 (mm) $\downarrow$	ASSD (mm) $\downarrow$	DSC (%) $\uparrow$
Static 2D Teacher (Fully-Supervised Base)	$93.0\pm 4.4$	$86.6\pm 5.7$	$2.88\pm 0.78$	$0.58\pm 0.06$	$88.8\pm 2.4$
Static Student	$98.7\pm 0.7$	$90.7\pm 0.4$	$2.18\pm 0.09$	$0.45\pm 0.03$	$90.9\pm 0.3$
Spatiotemporal Student (Ours)	$\mathbf{99.2\pm 0.6}$	$\mathbf{92.3\pm 0.2}$	$2.05\pm 0.07$	$0.41\pm 0.02$	$90.9\pm 0.2$
2D nnU-Net Baseline	$97.0\pm 1.6$	$89.9\pm 1.4$	$\mathbf{1.23\pm 0.36}$	$\mathbf{0.18\pm 0.08}$	$\mathbf{92.9\pm 1.1}$

Table 2: Quantitative evaluation and distillation ablation summary across the independent 25-patient test set (948 total frames). Values represent the mean

\pm

standard deviation across the 5 cross-validation folds. Our model enforces topology.

Figure 2: Qualitative tracking comparison across consecutive frames ( $t-1$ , $t$ , $t+1$ ). 2D nnU-Net baseline mistakes a nearby bright vessel for the aorta during a phase of fading visibility ( $N=3$ ). Our spatiotemporal student leverages sequence context to look past this fading effect, maintaining correct anatomical structure ( $N=2$ ).

Metric / Configuration	Fold 1	Fold 2	Fold 3	Fold 4	Fold 5	Mean Summary
Total Evaluated Frames	17,539	17,539	17,539	17,539	17,539	17,539 Total Frames
Static 2D A-UNet Teacher [ 8 ]
– Structural Anomalies ( $N\neq 2$ )	1,333	2,843	1,243	1,626	1,362	$1,681.4\pm 663.0$
– Low-Confidence Frames	0	837	0	0	0	$167.4\pm 374.3$
2D nnU-Net Baseline
– Structural Anomalies ( $N\neq 2$ )	1,169	1,115	1,089	1,109	702	$1,036.8\pm 190.2$

Table 3: Large-scale inference consistency and structural anomaly summary across all 5 cross-validation folds. Each entry evaluates fold-specific model performance over the entire unannotated cohort (

17,539

total frames per run).

$\beta$	Anomalies ( $N\neq 2$ ) $\downarrow$	Low-Conf. Frames $\downarrow$	$\text{Frac}_{2\text{CC}}$ (%) $\uparrow$
$0.00$ (Static Base)	$492.2\pm 49.9$	$13.2\pm 8.6$	$98.7\%\pm 0.7\%$
$0.05$	$461.2\pm 60.1$	$0.0\pm 0.0$	$98.9\%\pm 0.6\%$
$0.08$	$451.8\pm 64.2$	$0.0\pm 0.0$	$99.0\%\pm 0.7\%$
$\mathbf{0.10}$	$\mathbf{451.2\pm 67.8}$	$\mathbf{0.0\pm 0.0}$	$\mathbf{99.2\%\pm 0.6\%}$
$0.15$	$476.0\pm 59.2$	$0.0\pm 0.0$	$99.1\%\pm 0.8\%$
$0.50$	$478.6\pm 14.7$	$0.0\pm 0.0$	$98.6\%\pm 0.4\%$

Table 4: Population-wide hyperparameter sensitivity analysis varying the temporal smoothness weight

\beta

across all

17,539

unannotated frames.

Medical Image Segmentation

Medical image segmentation delineates organs, lesions, and other structures in scans to support measurement, diagnosis, treatment planning, and research.

See all 786 papers

Segmentation of the aorta in 4D flow MRI using 4D convolutional kernels and learning from sparse annotations
Sep 3, 2026Hinrich Rahlfs, Julio Garcia, Chiara Manini +13Medical Image Segmentation Magnetic Resonance Imaging
Self-Supervised Temporal Regularization for Landmark-Based Cardiac Segmentation with Automatic AHA Regional Mapping
Jun 30, 2026David Montalvo-García, Nicolás Gaggion, María J. Ledesma-Carbayo +1Ultrasound Image Segmentation Self-Supervised Learning
Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain · Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain · APOLO Biotech, Ciudad Autónoma de Buenos Aires, Argentina +2
OSA: Echocardiography Video Segmentation via Orthogonalized State Update and Anatomical Prior-aware Feature Enhancement
Mar 27, 2026Rui Wang, Huisi Wu, Jing QinUltrasound Image Segmentation Echocardiography
College of Computer Science and Software Engineering, Shenzhen University · Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University

Sep 3, 2026J/K move · Enter open · S save

Segmentation of the aorta in 4D flow MRI using 4D convolutional kernels and learning from sparse annotations

Hinrich Rahlfs, Julio Garcia, Chiara Manini, Markus Hüllebrand+12

Medical Image Segmentation Magnetic Resonance Imaging U-Net

Open paper AskPDF arXiv

Automated aortic segmentation in 4D flow MRI is essential for reproducible hemodynamic assessment but is limited by scarce dense annotations and high computational demands. We developed a fully automated 4D (3D+time) U-Net for segmenting the ascending aorta, arch, and proximal descending aorta, using a parameter-efficient hybrid 4D kernel to capture temporal context and sparse 4D labels derived from existing 2D expert contours and centerlines, thereby avoiding the need for dense 4D annotations. Training comprised 268 scans from 8 centers and 2 vendors, with evaluation on an internal test set (32 scans) and an external post-contrast set (30 scans; different site, protocol, and annotator), compared against frame-wise 3D networks and two semi-automatic references. Against time-resolved annotations, the 4D U-Net achieved Dice scores of 0.927 (internal) and 0.911 (external), versus 0.919/0.847 for the 3D U-Net, 0.893 for static PC-MRA, and 0.808 for registration-based propagation; differences were small in systole but pronounced in diastole. Agreement with expert contours for peak velocity, net flow, axial and circumferential wall shear stress, and diameters was excellent (ICC >=0.954 internal, >=0.980 external), while semi-automatic references performed worse. The method thus provides reproducible, time-resolved aortic segmentation for automated hemodynamic analysis and generalizes across multicenter, multivendor, and independent post-contrast data. The model is publicly available.

Abstract

Medical Image Segmentation

Explore similar work

Segmentation of the aorta in 4D flow MRI using 4D convolutional kernels and learning from sparse annotations

Self-Supervised Temporal Regularization for Landmark-Based Cardiac Segmentation with Automatic AHA Regional Mapping

OSA: Echocardiography Video Segmentation via Orthogonalized State Update and Anatomical Prior-aware Feature Enhancement