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Related Experiment Video

Updated: Jun 29, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

An efficient, scalable, and adaptable plug-and-play temporal attention module for motion-guided cardiac segmentation

Md Kamrul Hasan¹, Guang Yang², Choon Hwai Yap¹

¹Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK.

Medical Image Analysis

|February 15, 2026

Summary

This study introduces a Temporal Attention Module (TAM) to improve cardiac anatomy segmentation in deep learning (DL) models. TAM enhances motion-aware segmentation efficiency and accuracy across various cardiac imaging datasets and networks.

Keywords:

2D/3D + time cardiac imaging Deep learning Motion-guided segmentation Temporal attention module

Related Experiment Videos

Last Updated: Jun 29, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Area of Science:

Medical imaging analysis
Artificial intelligence in healthcare
Cardiovascular imaging

Background:

Cardiac anatomy segmentation is crucial for assessing cardiac function and diagnosing diseases.
Deep learning (DL) models have improved segmentation accuracy, especially when incorporating cardiac motion dynamics.
Existing methods for integrating motion information are computationally expensive or prone to registration errors.

Purpose of the Study:

To develop a computationally efficient and robust Temporal Attention Module (TAM) for enhancing deep learning-based cardiac anatomy segmentation.
To create a plug-and-play module adaptable to various segmentation network architectures (CNN, transformer, hybrid).
To improve the accuracy and efficiency of cardiac motion-aware segmentation.

Main Methods:

Proposed a novel Temporal Attention Module (TAM) using a multi-headed, cross-temporal attention mechanism.
Integrated TAM into diverse segmentation networks including UNet, FCN8s, UNetR, SwinUNetR, I²UNet, and DT-VNet.
Validated TAM on multiple cardiac imaging datasets: 2D echocardiography (CAMUS, EchoNet-Dynamic), 3D echocardiography (MITEA), and 3D cardiac MRI (ACDC).

Main Results:

TAM consistently improved segmentation performance across all tested datasets and networks.
Integration of TAM into SAM and MedSAM reduced Hausdorff distance (HD) significantly.
TAM-UNet and TAM-DT-VNet demonstrated substantial HD reductions on the ACDC 3D dataset.
TAM training requires only sparse temporal annotation, not full ground truth segmentation for all frames.

Conclusions:

The Temporal Attention Module (TAM) offers a robust, generalizable, and adaptable solution for motion-aware cardiac image segmentation.
TAM enhances segmentation accuracy and efficiency without drastic architectural modifications, scaling from 2D to 3D.
The proposed method reduces computational costs and reliance on complex registration techniques, making it a practical advancement for clinical applications.