mlsanityData to 7 Oct 2026

Continuous Spatio-Temporal Memory Networks for 4D Cardiac Cine MRI Segmentation

arXiv 2410.23191 ↗First posted 30 Oct 2024cs.CVcited by 2

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Abstract (arXiv)

Current cardiac cine magnetic resonance image (cMR) studies focus on the end diastole (ED) and end systole (ES) phases, while ignoring the abundant temporal information in the whole image sequence. This is because whole sequence segmentation is currently a tedious process and inaccurate. Conventional whole sequence segmentation approaches first estimate the motion field between frames, which is then used to propagate the mask along the temporal axis. However, the mask propagation results could be prone to error, especially for the basal and apex slices, where through-plane motion leads to significant morphology and structural change during the cardiac cycle. Inspired by recent advances in video object segmentation (VOS), based on spatio-temporal memory (STM) networks, we propose a continuous STM (CSTM) network for semi-supervised whole heart and whole sequence cMR segmentation. Our CSTM network takes full advantage of the spatial, scale, temporal and through-plane continuity prior of the underlying heart anatomy structures, to achieve accurate and fast 4D segmentation. Results of extensive experiments across multiple cMR datasets show that our method can improve the 4D cMR segmentation performance, especially for the hard-to-segment regions.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 29 of its 49 references that are in the map. First posted on arXiv 30 Oct 2024.

Builds on

29 of 49 references.

References · 29

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
  5. 2019Video Object Segmentation using Space-Time Memory Networkscited by 436
  6. 2022XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Modelcited by 436
  7. 2016One-Shot Video Object Segmentationcited by 233
  8. 2021Associating Objects with Transformers for Video Object Segmentationcited by 225
  9. 2021Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentationcited by 200
  10. 2023Putting the Object Back into Video Object Segmentationcited by 182
  11. 2021nnFormer: Interleaved Transformer for Volumetric Segmentationcited by 179
  12. 2016Learning Video Object Segmentation from Static Imagescited by 151
17 more
  1. 2022Decoupling Features in Hierarchical Propagation for Video Object Segmentationcited by 146
  2. 2021A Survey on Deep Learning Technique for Video Segmentationcited by 132
  3. 2021Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusioncited by 129
  4. 2021UTNet: A Hybrid Transformer Architecture for Medical Image Segmentationcited by 116
  5. 2018Efficient Video Object Segmentation via Network Modulationcited by 98
  6. 2018Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learningcited by 74
  7. 2021Hierarchical Memory Matching Network for Video Object Segmentationcited by 72
  8. 2018Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagationcited by 56
  9. 2018CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRFcited by 48
  10. 2018MaskRNN: Instance Level Video Object Segmentationcited by 48
  11. 2022A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmarkcited by 45
  12. 2018Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifierscited by 35
  13. 2022Look Before You Match: Instance Understanding Matters in Video Object Segmentationcited by 33
  14. 2023Scalable Video Object Segmentation with Simplified Frameworkcited by 29
  15. 2021iSegFormer: Interactive Segmentation via Transformers with Application to 3D Knee MR Imagescited by 27
  16. 2021DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Imagescited by 26
  17. 2023Boosting Video Object Segmentation via Space-time Correspondence Learningcited by 21

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluationscited by 0
  2. 2026Cardiac MRI Through-Plane Super-Resolution Guided by Reference and Memorycited by 0