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Abstract #3984

No Annotate Again (NAA): Realistic Image and Annotation Synthesis for Multi-Contrast MRI through Diffusion without Paired Data

Xiao Chen1, Chen Li1,2, Eric Zhang Chen1, Yikang Liu1, Lin Zhao1, Terrence Chen1, and Shanhui Sun1
1UII America Inc., Boston, MA, United States, 2Stony Brook University, Stony Brook, NY, United States

Synopsis

Keywords: AI Diffusion Models, Machine Learning/Artificial Intelligence, Multi Contrast, Annotation, Synthesis, Diffusion

Motivation: The variability of multi contrast MRI presents challenges when developing neural networks, as annotations are performed separately for each contrast despite their visual similarity.

Goal(s): We aim to achieve scalable and automated annotation solutions for diverse MRI contrasts.

Approach: We propose No Annotate Again (NAA), a novel approach that synthesizes realistic images for a new contrast using given anatomical masks, without requiring paired images or manual annotations, by designing a unique diffusionb-based framework.

Results: Tested on cardiac MRI cine images and T1 maps, NAA generated realistic T1 maps, which largely improved the segmentation downstream task performance.

Impact: NAA enables scalable, annotation-free neural network developments for medical image analysis. This approach reduces dependency on annotated datasets and can benefit a wide range of applications.

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Keywords