<p>Brain Parcellation in Freesurfer is splitting images of the brain into their defined partitions, mapping the brain</p>
<p><a href="https://i.sstatic.net/717Pi.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/717Pi.jpg" alt="Brain Parcellation Images" /></a>
<sup>Brain Parcellation images from <a href="https://doi.org/10.1038/s41583-018-0071-7" rel="nofollow noreferrer">Eickhoff et al. (2018)</a></sup></p>
<p><a href="https://doi.org/10.1016/j.media.2020.101639" rel="nofollow noreferrer">Thyreau &amp; Taki (2020)</a> points out:</p>
<blockquote>
<p>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.</p>
</blockquote>
<h2>Brain Segmentation</h2>
<p>Brain segmentation initially involves the removal of non-cerebral tissues like skull. But, segmentation is also (<a href="https://doi.org/10.1155/2015/450341" rel="nofollow noreferrer">Despotović, et al. 2015</a>)</p>
<blockquote>
<p>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.</p>
</blockquote>
<p><a href="https://i.sstatic.net/lxggp.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/lxggp.jpg" alt="Image of segmented brain" /></a>
<sup>Image from <a href="https://doi.org/10.1371/journal.pone.0236493" rel="nofollow noreferrer">Lee et al. (2020)</a></sup></p>
<p><a href="https://doi.org/10.3389/fninf.2019.00067" rel="nofollow noreferrer">McClure et al. (2019)</a> describes</p>
<blockquote>
<p>a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours.</p>
</blockquote>
<h2>References</h2>
<p>Despotović, I., Goossens, B., &amp; Philips, W. (2015). MRI segmentation of the human brain: challenges, methods, and applications. <em>Computational and mathematical methods in medicine, 2015</em>, 450341. <a href="https://doi.org/10.1155/2015/450341" rel="nofollow noreferrer">https://doi.org/10.1155/2015/450341</a></p>
<p>Eickhoff, S. B., Yeo, B. T., &amp; Genon, S. (2018). Imaging-based parcellations of the human brain. <em>Nature Reviews Neuroscience, 19</em>(11), 672-686. <a href="https://doi.org/10.1038/s41583-018-0071-7" rel="nofollow noreferrer">https://doi.org/10.1038/s41583-018-0071-7</a></p>
<p>Lee B, Yamanakkanavar N, Choi JY (2020). Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture. <em>PLoS ONE 15</em>(8): e0236493. <a href="https://doi.org/10.1371/journal.pone.0236493" rel="nofollow noreferrer">https://doi.org/10.1371/journal.pone.0236493</a></p>
<p>McClure, P., Rho, N., Lee, J. A., Kaczmarzyk, J. R., Zheng, C. Y., Ghosh, S. S., ... &amp; Pereira, F. (2019). Knowing what you know in brain segmentation using Bayesian deep neural networks. <em>Frontiers in neuroinformatics, 13</em>, 67. <a href="https://doi.org/10.3389/fninf.2019.00067" rel="nofollow noreferrer">https://doi.org/10.3389/fninf.2019.00067</a></p>
<p>Thyreau, B., &amp; Taki, Y. (2020). Learning a cortical parcellation of the brain robust to the MRI segmentation with convolutional neural networks. <em>Medical image analysis, 61</em>, 101639. <a href="https://doi.org/10.1016/j.media.2020.101639" rel="nofollow noreferrer">https://doi.org/10.1016/j.media.2020.101639</a></p>

CC BY-SA 4.0
2021-12-29T06:04:14.663Z
2021-12-29T06:17:45.677Z
2021-12-29T06:17:45.677Z
Chris Rogers
<p>Brain parcellation and brain segmentation are related, but different, approaches to analyzing the structure of the brain.</p>
<p>Brain parcellation refers to the process of dividing the brain into distinct, non-overlapping regions, or parcels, based on functional, structural, or connectivity criteria. This division results in a parcellation map, which assigns a unique label to each parcel, indicating its functional or structural identity. In Freesurfer, the parcellation volume is a map that assigns a unique label to each voxel in the brain, indicating its parcel membership.</p>
<p>Brain segmentation, on the other hand, refers to the process of dividing the brain into distinct, non-overlapping regions based on the tissue type (i.e. gray matter, white matter, or cerebrospinal fluid). The result of segmentation is a segmentation map, which assigns a unique label to each voxel in the brain, indicating its tissue type. In Freesurfer, the segmentation volume is a map that assigns a unique label to each voxel in the brain, indicating its tissue type.</p>
<p>So, in short, brain parcellation aims to divide the brain into functional or structural regions, while brain segmentation aims to divide the brain into tissue types.</p>

CC BY-SA 4.0
2023-02-08T12:37:13.487Z
Welcome. Can you add citations and/or resources so others can background read on your answer?
AliceD
CC BY-SA 4.0
2023-02-08T11:58:38.373Z
2023-02-08T11:58:38.373Z
Ian Hsiao
<p>Segmentation is about <em>tissue type</em> and <em>anatomical structures</em>. It's mainly concerned with identifying the voxels of brain and cerebellum, separating gray matter and white matter, identifying subcortical structures such as the hippocampus, etc. It's typically <em>volumes</em>, rather than surfaces, that come up in this context.</p>
<p>Parcellation is about <em>neuroanatomy</em>. It's mainly concerned with defining regions based on architectonics, connectivity, function, or topography. Think &quot;primary visual cortex,&quot; &quot;Broca's area,&quot; etc. It's typically <em>surfaces</em>, rather than volumes, that come up in this context.</p>
<p>(The other answer is fine -- I am just adding a short and more direct answer to the question.)</p>

CC BY-SA 4.0
2023-02-01T16:42:57.430Z
2023-02-01T17:04:47.587Z
2023-02-01T17:04:47.587Z
ebrahim
<p>What is the difference between brain parcellation and brain segmentation? (question based on Freesurfer that produces parcellation volume and segmentation volume measures)</p>

<neuroscience>
What is the difference between brain parcellation and brain segmentation? (question based on Freesurfer)
SaidB
# What is the difference between brain parcellation and brain segmentation? (question based on Freesurfer)

(Asked by: username_0 on 2021-12-28)

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

**Tags:** neuroscience


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## 3 answers

### Answer by username_1 on 2021-12-29. Score: 2

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

[![Brain Parcellation Images](https://i.sstatic.net/717Pi.jpg)](https://i.sstatic.net/717Pi.jpg)
Brain Parcellation images from [Eickhoff et al. (2018)](https://doi.org/10.1038/s41583-018-0071-7)

[Thyreau & Taki (2020)](https://doi.org/10.1016/j.media.2020.101639) 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](https://doi.org/10.1155/2015/450341))

> 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](https://i.sstatic.net/lxggp.jpg)](https://i.sstatic.net/lxggp.jpg)
Image from [Lee et al. (2020)](https://doi.org/10.1371/journal.pone.0236493)

[McClure et al. (2019)](https://doi.org/10.3389/fninf.2019.00067) describes

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

## References

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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### Answer by username_2 on 2023-02-08. Score: 1

Brain parcellation and brain segmentation are related, but different, approaches to analyzing the structure of the brain.

Brain parcellation refers to the process of dividing the brain into distinct, non-overlapping regions, or parcels, based on functional, structural, or connectivity criteria. This division results in a parcellation map, which assigns a unique label to each parcel, indicating its functional or structural identity. In Freesurfer, the parcellation volume is a map that assigns a unique label to each voxel in the brain, indicating its parcel membership.

Brain segmentation, on the other hand, refers to the process of dividing the brain into distinct, non-overlapping regions based on the tissue type (i.e. gray matter, white matter, or cerebrospinal fluid). The result of segmentation is a segmentation map, which assigns a unique label to each voxel in the brain, indicating its tissue type. In Freesurfer, the segmentation volume is a map that assigns a unique label to each voxel in the brain, indicating its tissue type.

So, in short, brain parcellation aims to divide the brain into functional or structural regions, while brain segmentation aims to divide the brain into tissue types.

> **Comments:**
> **username_3**: Welcome. Can you add citations and/or resources so others can background read on your answer?  

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### Answer by username_4 on 2023-02-01. Score: 1

Segmentation is about *tissue type* and *anatomical structures*. It's mainly concerned with identifying the voxels of brain and cerebellum, separating gray matter and white matter, identifying subcortical structures such as the hippocampus, etc. It's typically *volumes*, rather than surfaces, that come up in this context.

Parcellation is about *neuroanatomy*. It's mainly concerned with defining regions based on architectonics, connectivity, function, or topography. Think "primary visual cortex," "Broca's area," etc. It's typically *surfaces*, rather than volumes, that come up in this context.

(The other answer is fine -- I am just adding a short and more direct answer to the question.)


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