Cardiac Magnetic Resonance 2D+T Short- and Long-axis Segmentation via
  Spatio-temporal SAM Adaptation
Zhennong Chen Sekeun Kim Hui Ren Quanzheng Li Xiang Li
Accurate 2D+T myocardium segmentation in cine cardiac magnetic resonance
(CMR) scans is essential to analyze LV motion throughout the cardiac cycle
comprehensively. The Segment Anything Model (SAM), known for its accurate
segmentation and zero-shot generalization, has not yet been tailored for CMR
2D+T segmentation. We therefore introduce CMR2D+T-SAM, a novel approach to
adapt SAM for CMR 2D+T segmentation using spatio-temporal adaption. This
approach also incorporates a U-Net framework for multi-scale feature
extraction, as well as text prompts for accurate segmentation on both
short-axis (SAX) and long-axis (LAX) views using a single model. CMR2D+T-SAM
outperforms existing deep learning methods on the STACOM2011 dataset, achieving
a myocardium Dice score of 0.885 and a Hausdorff distance (HD) of 2.900 pixels.
It also demonstrates superior zero-shot generalization on the ACDC dataset with
a Dice score of 0.840 and a HD of 4.076 pixels.