Evaluating the impact of MR image contrast on whole brain segmentation

Savannah P. Hays; Lianrui Zuo; Aaron Carass; Jerry Prince

doi:10.1117/12.2611503

4 April 2022 Evaluating the impact of MR image contrast on whole brain segmentation

Savannah P. Hays, Lianrui Zuo, Aaron Carass, Jerry Prince

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 120320I (2022) https://doi.org/10.1117/12.2611503
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

The development of automatic whole brain segmentation algorithms has greatly facilitated large-scale multi cohort magnetic resonance (MR) image analyses in recent years. However, the performance of these segmentation algorithms is often affected by image contrast due to the variations in pulse sequences, acquisitions parameters, and manufacturers. Quantitatively evaluating segmentation algorithms on different image contrasts is challenging because manual delineations of the human brain are usually limited. In this study, we tackle the problem by synthesizing new contrast MR images from a small set of images with manual delineations. We quantitatively evaluate the current state-of-the-art whole brain segmentation algorithm, SLANT, on various MR image contrasts. Based on 50 manually delineated T1-weighted MR images acquired from a single site, we synthesize new contrast images using a deep learning-based harmonization algorithm. Two types of contrast synthesis were conducted to simulate both intra- and inter-site contrast variability in MR imaging. SLANT performance is measured using the Dice similarity coefficient (DSC). Experiments show that the average DSC of SLANT varies with image contrast. We also demonstrate the preferred and the least preferred contrast of SLANT based on 11 real MR imaging sites.

Conference Presentation

Citation Download Citation

Savannah P. Hays, Lianrui Zuo, Aaron Carass, and Jerry Prince "Evaluating the impact of MR image contrast on whole brain segmentation", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120320I (4 April 2022); https://doi.org/10.1117/12.2611503

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