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 (2D + 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 (2D to 2D + 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 (DSC ≥ 0.50) prior to selecting the epoch that maximizes anatomical consistency. This strategy enables the spatiotemporal student model to achieve superior surface tracking accuracy (NSD@1mm = 92.3% ± 0.2%) and high struc- tural reliability (Frac2CC = 99.2% ± 0.6%), reducing population-wide structural anomalies by over 56% compared to a 2D nnU-Net baseline.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/STACOM2026_042.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/profile?id=~Dexter_Wen_Jie_Teo1
BibTex
@InProceedings{TeoDex_Spatiotemporal_MICCAISAT2026,
author = { Teo, Dexter Wen Jie AND Shehata, Nairouz AND Lombaert, Herve},
title = { { Spatiotemporal Distillation via Recurrent Bottlenecks for Aortic Tracking } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17267},
month = {pending},
page = {pending}
}