Spatiotemporal Distillation via Recurrent Bottlenecks for Aortic Tracking

• URL: http://arxiv.org/abs/2608.23879v1
• Date: Mon, 24 Aug 2026 22:37:47 GMT
• Title: Spatiotemporal Distillation via Recurrent Bottlenecks for Aortic Tracking
• Abstract summary: We present a knowledge distillation framework exploiting the cardiac cycle.<n>The framework distills a spatial teachers expertise into atemporal atemporal student network.<n>This strategy enables the cinetemporal student model to achieve superior surface tracking accuracy.
• Score: 1.898768451389131
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Self-Supervised Cardiac Phase Detection via Single-Parameter Latent Orbits [44.45620787098836]
  Accurate identification of end-diastole (ED) and end-systole (ES) in echocardiography underpins the quantification of ventricular function.<n>Recent self-supervised methods either prescribe strict periodic trajectories or learn an unconstrained low-dimensional motion subspace from reconstruction or registration objectives.<n>We learn an effective latent orbit, significantly improves upon the previous state of the art in ED localisation and matches it in ES localisation while using a more constrained representation and fewer training epochs.
  arXiv  Detail & Related papers  (2026-09-10T14:56:16Z)
• Distilling Temporal Coherence into 2D Networks for Transrectal Ultrasound Prostate Video Segmentation [2.65131397683555]
  Real-time video segmentation of the prostate in Transrectal Ultrasound (TRUS) is essential for image-guided interventions.<n>We present a Temporally Consistent Learning Framework that distills temporal coherence into a 2D network during training.<n>Our design is driven by a key clinical observation: the prostate exhibits geometric stability, whereas the surrounding acoustic environment fluctuates due to physiological motion and transducer pressure.
  arXiv  Detail & Related papers  (2026-06-30T06:27:02Z)
• EchoTracker2: Enhancing Myocardial Point Tracking by Modeling Local Motion [0.7273358958058737]
  My-cardial point tracking (MPT) has emerged as a promising direction for motion estimation in echocardiography.<n>We propose a fine-stage-only architecture, textEchoTracker2, which enriches pixel-precise features with local context.<n>Compared to the best general-purpose point tracking method, the improvements are $2.0%$ and $5.3%$, respectively.
  arXiv  Detail & Related papers  (2026-05-12T13:59:52Z)
• Continuous-Time Distribution Matching for Few-Step Diffusion Distillation [57.28746398500951]
  We introduce Continuous-Time Distribution Matching (CDM), migrating the DMD framework from discrete anchoring to continuous optimization.<n>CDM achieves this through two continuous-time designs. First, we replace the fixed discrete schedule with a dynamic continuous schedule of random length.<n>Second, we propose a continuous-time alignment objective that performs active off-trajectory matching on latents extrapolated via the student's velocity field.
  arXiv  Detail & Related papers  (2026-05-07T14:56:39Z)
• Forecasting as Rendering: A 2D Gaussian Splatting Framework for Time Series Forecasting [79.37674445572462]
  Time series forecasting (TSF) remains a challenging problem due to the intricate entanglement of intraperiod-fluctuations and interperiod-trends.<n>Treating reshaped tensors as static images results in a topological mismatch.<n> relying on uniform fixed-size representations allocates modeling capacity inefficiently.<n>TimeGS is a novel framework that fundamentally shifts the forecasting paradigm from regression to 2D generative rendering.
  arXiv  Detail & Related papers  (2026-02-10T14:13:36Z)
• Unleashing Temporal Capacity of Spiking Neural Networks through Spatiotemporal Separation [67.69345363409835]
  Spiking Neural Networks (SNNs) are considered naturally suited for temporal processing, with membrane potential propagation widely regarded as the core temporal modeling mechanism.<n>We design Non-Stateful (NS) models progressively removing membrane propagation to its stage-wise role. Experiments reveal a counterintuitive phenomenon: moderate removal in shallow layers improves performance, while excessive removal causes collapse.
  arXiv  Detail & Related papers  (2025-12-05T07:05:53Z)
• TARDis: Time Attenuated Representation Disentanglement for Incomplete Multi-Modal Tumor Segmentation and Classification [10.329406702659123]
  Tumor segmentation and diagnosis in contrast-enhanced Computed Tomography (CT) rely heavily on the physiological dynamics of contrast agents.<n>Existing deep learning approaches typically treat missing phases as absent independent channels, ignoring the inherent temporal continuity of hemodynamics.<n>We propose Time Attenuated Representation Disentanglement (TARDis), a novel physics-aware framework that redefines missing modalities as missing sample points on a continuous Time-Attenuation Curve.
  arXiv  Detail & Related papers  (2025-12-04T08:44:50Z)
• PHASE-Net: Physics-Grounded Harmonic Attention System for Efficient Remote Photoplethysmography Measurement [63.007237197267834]
  Existing deep learning methods are mostly physiological monitoring and lack theoretical robustness.<n>We propose a physics-informed r paradigm derived from the Navier-Stokes equations of hemodynamics, showing that the pulse signal follows a second-order system.<n>This provides a theoretical justification for using a Temporal Conal Network (TCN)<n>Phase-Net achieves state-of-the-art performance with strong efficiency, offering a theoretically grounded and deployment-ready r solution.
  arXiv  Detail & Related papers  (2025-09-29T14:36:45Z)
• ArrhythmiaVision: Resource-Conscious Deep Learning Models with Visual Explanations for ECG Arrhythmia Classification [0.0]
  We propose ArrhythmiNet V1 and V2, optimized for efficient, real-time arrhythmia classification on edge devices.<n>Inspired by MobileNet's depthwise separable convolutional design, these models maintain memory footprints of just 302.18 KB and 157.76 KB, respectively.<n>Our findings demonstrate the feasibility of combining interpretability, predictive accuracy, and computational efficiency in practical, wearable, and embedded ECG monitoring systems.
  arXiv  Detail & Related papers  (2025-04-30T18:22:45Z)
• X$^{2}$-Gaussian: 4D Radiative Gaussian Splatting for Continuous-time Tomographic Reconstruction [64.2059940799033]
  Current methods discretize temporal resolution into fixed phases with respiratory gating devices.<n>X$2$-Gaussian, a novel framework, enables continuous-time 4DCT reconstruction by integrating dynamic radiative splatting with self-supervised respiratory motion learning.
  arXiv  Detail & Related papers  (2025-03-27T17:59:57Z)
• Segmenting Bi-Atrial Structures Using ResNext Based Framework [3.0838948803252904]
  We propose TASSNet, a novel two-stage deep learning framework for fully automated segmentation of both left atrium (LA) and right atrium (RA)<n> TASSNet introduces two main innovations: (i) a ResNeXt-based encoder to enhance feature extraction from limited medical datasets, and (ii) a cyclical learning rate schedule to address convergence instability in highly imbalanced, small-batch 3D segmentation tasks.
  arXiv  Detail & Related papers  (2025-02-28T10:23:12Z)
• CoReEcho: Continuous Representation Learning for 2D+time Echocardiography Analysis [42.810247034149214]
  We propose CoReEcho, a novel training framework emphasizing continuous representations tailored for direct EF regression. CoReEcho: 1) outperforms the current state-of-the-art (SOTA) on the largest echocardiography dataset (EchoNet-Dynamic) with MAE of 3.90 & R2 of 82.44, and 2) provides robust and generalizable features that transfer more effectively in related downstream tasks.
  arXiv  Detail & Related papers  (2024-03-15T10:18:06Z)
• Echocardiography Segmentation with Enforced Temporal Consistency [10.652677452009627]
  We propose a framework to learn the 2D+time long-axis cardiac shape. The identification and correction of cardiac inconsistencies relies on a constrained autoencoder trained to learn a physiologically interpretable embedding of cardiac shapes.
  arXiv  Detail & Related papers  (2021-12-03T16:09:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.