What is the difference between brain parcellation and brain segmentation? (question based on Freesurfer that produces parcellation volume and segmentation volume measures)


Brain Parcellation in Freesurfer is splitting images of the brain into their defined partitions, mapping the brain

Brain Parcellation Images Brain Parcellation images from Eickhoff et al. (2018)

Thyreau & Taki (2020) points out:

The parcellation of the human cortex into meaningful anatomical units is a common step of various neuroimaging studies. There have been multiple successful efforts to process magnetic resonance (MR) brain images automatically and identify specific anatomical regions, following atlases defined from cortical landmarks. Those definitions usually rely first on a high-quality brain surface reconstruction. On the other hand, when high accuracy is not a requirement, simpler methods based on warping a probabilistic atlas have been widely adopted.

Brain Segmentation

Brain segmentation initially involves the removal of non-cerebral tissues like skull. But, segmentation is also (Despotović, et al. 2015)

commonly used for measuring and visualizing the brain's anatomical structures, for analyzing brain changes, for delineating pathological regions, and for surgical planning and image-guided interventions. In the last few decades, various segmentation techniques of different accuracy and degree of complexity have been developed and reported in the literature.

Image of segmented brain Image from Lee et al. (2020)

McClure et al. (2019) describes

a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours.


Despotović, I., Goossens, B., & Philips, W. (2015). MRI segmentation of the human brain: challenges, methods, and applications. Computational and mathematical methods in medicine, 2015, 450341. https://doi.org/10.1155/2015/450341

Eickhoff, S. B., Yeo, B. T., & Genon, S. (2018). Imaging-based parcellations of the human brain. Nature Reviews Neuroscience, 19(11), 672-686. https://doi.org/10.1038/s41583-018-0071-7

Lee B, Yamanakkanavar N, Choi JY (2020). Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture. PLoS ONE 15(8): e0236493. https://doi.org/10.1371/journal.pone.0236493

McClure, P., Rho, N., Lee, J. A., Kaczmarzyk, J. R., Zheng, C. Y., Ghosh, S. S., ... & Pereira, F. (2019). Knowing what you know in brain segmentation using Bayesian deep neural networks. Frontiers in neuroinformatics, 13, 67. https://doi.org/10.3389/fninf.2019.00067

Thyreau, B., & Taki, Y. (2020). Learning a cortical parcellation of the brain robust to the MRI segmentation with convolutional neural networks. Medical image analysis, 61, 101639. https://doi.org/10.1016/j.media.2020.101639


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