Diffusion MRI and silver standard masks to improve CNN-based thalamus segmentation

15 February 2021 Diffusion MRI and silver standard masks to improve CNN-based thalamus segmentation

G. R. Pinheiro, L. Brusini, A. Bajrami, F. B. Pizzini, M. Calabrese, F. Reis, S. Appenzeller, G. Menegaz, L. Rittner

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 115962L (2021) https://doi.org/10.1117/12.2581895
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

The thalamus is an internal structure of the brain whose changes are related to diseases such as multiple sclerosis and Parkinson’s disease. Thus, the thalamus segmentation is an important step in studies and applications related to these disorders, for example, for shape measuring and surgical planning. The most used software and tools for brain structures segmentation employ atlas-based algorithms that usually require long processing times and sometimes lead to inaccurate results on sub-cortical structures. New methods, that minimize those problems, using deep learning for segmenting brain structures have been recently proposed. However, for some structures such as the thalamus, these methods still tend to have unsatisfactory results since they rely only on T1w images, where the contrast can be low or absent. Aiming to overcome these issues, we proposed a Convolutional Neural Network (CNN) trained with multi-modal data (structural and diffusion MRI) and the use of silver standard masks created from multiple automatic segmentations. Results on a subset of 190 subjects from the Human Connectome Project (HCP) showed an improvement in segmentation quality, confirming the effectiveness of diffusion data in differentiating tissues due to measured micro-structural properties.

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Citation Download Citation

G. R. Pinheiro, L. Brusini, A. Bajrami, F. B. Pizzini, M. Calabrese, F. Reis, S. Appenzeller, G. Menegaz, and L. Rittner "Diffusion MRI and silver standard masks to improve CNN-based thalamus segmentation", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115962L (15 February 2021); https://doi.org/10.1117/12.2581895

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