Abstract #0429

Automated Segmentation and Annotation of Cardiac Diffusion Tensor Images

Sascha W. Stocker^1,2, Ariel J. Hannum^1,2,3,4, Thu Le⁵, Tyler E. Cork^1,2,3,4, and Daniel B. Ennis^1,2,3

¹Radiology, Stanford University, Stanford, CA, United States, ²Cardiovascular Institute, Stanford University, Stanford, CA, United States, ³Division of Radiology, Veterans Administration Health Care System, Palo Alto, CA, United States, ⁴Bioengineering, Stanford University, Stanford, CA, United States, ⁵Computer Science, Stanford University, Stanford, CA, United States

Synopsis

Keywords: Myocardium, Segmentation, Cardiac Diffusion Weighted Imaging, cDWI, Cardiac Diffusion Tensor Imaging, cDTI, Cardiac Diffusion

Motivation: Segmentation of cardiac diffusion tensor images (cDTI) is time-consuming and observer dependent thus increasing the overhead for broader research adoption. Standardized approaches are needed.

Goal(s): To automate deep-learning segmentation in cardiac cDTI that match expert image segmentation.

Approach: We trained three nnUNets with different cDTI parametric maps. Dice similarly coefficients (DSC) and Hausdorff Distances (HD) assessed performance.

Results: High DSC scores (0.903) for LV, low HD for RVIPs (averages <3mm). No significant differences in mean diffusivity (MD) compared to the expert annotated data.

Impact: Automated segmentations of cDTI enable streamlined analysis, reduce manual effort, and improve efficiency of cDTI research.

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