Abstract #1398

Enhancing Deep Learning-Based Liver Vessel Segmentation on MRI with Image Translation Techniques

Yanbo Zhang¹, Ali Bilgin^2,3, Sevgi Gokce Kafali^4,5, Brian Toner³, Timo Delgado^4,5, Eze Ahanonu³, Deniz Karakay³, Wenqi Zhou^4,5, Sabina Mollus⁶, Stephan Kannengießer⁶, Vibhas Deshpande⁷, Sasa Grbic¹, Maria Altbach³, and Holden H. Wu^4,5

¹Siemens Healthineers, Princeton, NJ, United States, ²Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States, ³Department of Medical Imaging, University of Arizona, Tucson, AZ, United States, ⁴Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States, ⁵5Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States, ⁶Siemens Healthineers, Erlangen, Germany, ⁷Siemens Healthineers, Malvern, PA, United States

Synopsis

Keywords: AI Diffusion Models, Segmentation, Liver Vessel Segmentation

Motivation: To improve liver vessel segmentation on MRI under annotation constraints.

Goal(s): Apply an advanced unpaired image translation technique, SynDiff, to create synthetic MR images from CT data.

Approach: By incorporating vessel masks in the translation process, the optimized SynDiff models generated synthetic images that facilitated more effective pretraining of segmentation models.

Results: Validated across multiple pretraining settings, the refined SynDiff approach surpassed the standard nnU-Net and other pretraining-based methods, substantially improving liver vessel segmentation performance.

Impact: This study remarkably advances liver vessel segmentation on MRI, demonstrating that synthetic data can effectively augment limited datasets, leading to improved model performance. It has great potential for broader applications in medical image analysis.

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