Abstract #1807

Deep Learning Automated Segmentation of the Left Ventricle for Spin-Echo Cardiac Diffusion Tensor Imaging (cDTI)

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

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

Synopsis

Keywords: Myocardium, Diffusion Tensor Imaging

Motivation: Segmentation is central to cDTI post-processing, but remains subjective, time-intensive, and observer-dependent. Faster methods are needed.

Goal(s): To develop and validate a U-Net for automating and standardizing left ventricle segmentations for cDTI. Our target was for U-net generated masks to yield cDTI metric maps within 5% of ground-truth and Dice scores comparable to a human reader.

Approach: We developed a U-Net to automatically segment cDTI data then compared generated masks to expert annotations.

Results: Median Dice score was 0.79 with cDTI metrics within 5% of ground truth. A multiple-reader study demonstrated the need for further generalization of datasets at different resolutions.

Impact: An automated U-Net approach to cardiac DTI segmentation of the left ventricle minimizes segmentation variability, reduces processing time, and preserves cDTI metric measurement accuracy.

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