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New deep learning evaluation methods for brain MRI generation are needed. Current metrics fail to assess anatomical plausibility, leading to unreliable results. This study introduces a framework for robust quality assessment of generated brain MRIs.

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Medical Image Analysis

Background:

  • Deep learning models can generate structural brain MRIs, potentially accelerating neuroscience research.
  • Current quality evaluation metrics for generative models, often adapted from natural image processing, are insufficient for assessing brain MRI anatomical plausibility.
  • This inadequacy leads to inconclusive findings regarding the quality of generated brain MRIs.

Purpose of the Study:

  • To propose and validate a novel framework for evaluating the quality of generative models producing structural brain MRIs.
  • To address the limitations of existing metrics in assessing anatomical plausibility and reliability.
  • To provide a standardized and rigorous method for comparing different generative models for brain MRI synthesis.

Main Methods:

  • A framework was developed involving uniform processing of real MRIs, standardized model implementation, and automated segmentation of generated MRIs.
  • The framework quantifies anatomical plausibility by analyzing segmented brain structures.
  • Reliability checks for segmentation accuracy were rigorously implemented, a critical step often overlooked.
  • Six state-of-the-art generative models were trained and tested on over 3000 MRIs.

Main Results:

  • Existing metrics (e.g., SSIM, FID) were shown to be sensitive to experimental setup and poor indicators of anatomical plausibility.
  • Only 3 out of 6 generative models produced MRIs that met the framework's criteria for high-quality output.
  • At least 95% of the generated MRIs from these 3 models had highly reliable segmentations.
  • The framework's assessments aligned with qualitative evaluations, confirming its validity.

Conclusions:

  • Standard image metrics are inadequate for evaluating generative models of brain MRIs.
  • The proposed framework offers a reliable and valid method for assessing the anatomical plausibility and quality of synthetic brain MRIs.
  • This approach is crucial for advancing the use of generative models in neuroscience discovery.
  • The framework's code is publicly available for broader adoption and research.