Meeting Banner
Abstract #3247

Counterfactual MRI Data Augmentation using Conditional Denoising Diffusion Models:training AI for different clinical scenarios

Joao Santinha1,2, Pedro Morão3, Yasna Forghani1, Nuno Loução1, Teresa Correia4,5, Pedro Gouveia1,2, and Mario A. T. Figueiredo6
1Breast Unit/Digital Surgery LAB, Champalimaud Foundation, Lisboa, Portugal, 2Faculty of Medicine University of Lisbon, Lisboa, Portugal, 3Instituto Superior Técnico Universidade de Lisboa, Lisboa, Portugal, 4Centro de Ciências do Mar - CCMAR, Faro, Portugal, 5School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 6Instituto de Telecomunicações Instituto Superior Técnico Universidade de Lisboa, Lisboa, Portugal

Synopsis

Keywords: AI Diffusion Models, Machine Learning/Artificial Intelligence, Generative AI, Data Augmentation, Acquisition Settings

Motivation: Medical deep learning (DL) models struggle with generalizability due to MR image acquisition parameters (IAP) variations, limiting their clinical utility.

Goal(s): This study introduces a novel data augmentation method to enhance DL segmentation generalizability using conditional denoising diffusion models (cDDMs) to generate counterfactual MR images with altered IAPs, while preserving anatomy.

Approach: We trained a cDDM to produce IAP-diverse counterfactual breast MR images. Segmentation models and IAP prediction models were trained to validate the cDDM.

Results: Counterfactual data augmentation improved segmentation accuracy, particularly in out-of-distribution settings, demonstrating the potential of cDDMs to mitigate domain shifts in medical imaging.

Impact: This method can improve the performance of DL models in clinical settings by enabling them to generalize across different acquisition settings. This could lead to more reliable and robust diagnoses, particularly in scenarios with limited access to diverse training data.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords