Difference between revisions of "Brain ventricle parcellation instructions"
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− | <meta name="title" content="Brain Ventricle Parcellation with Convolutional Neural Network"/> | + | <!-- <meta name="title" content="Brain Ventricle Parcellation with Convolutional Neural Network"/> --> |
{{h2|Brain Ventricle Parcellation with Convolutional Neural Network}} | {{h2|Brain Ventricle Parcellation with Convolutional Neural Network}} | ||
− | This singularity image contains the code and trained model for brain ventricle parcellation using a convolutional neural network. If you use this work, please cite: | + | {{TOCright}} |
+ | This singularity image contains the code and trained model for brain ventricle parcellation using a convolutional neural network. The singularity image can be downloaded in the following link (~1.7GB): | ||
+ | {{iacl|~muhan/ventricle-parcellation_v4.simg|Singularity image for brain ventricle parcellation (v4)}}. | ||
+ | |||
+ | If you use this work, please cite: | ||
+ | *Shao, M., Han, S., Carass, A., Li, X., Blitz, A.M., Shin, J., Prince, J.L. and Ellingsen, L.M., 2019. '''Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly'''. NeuroImage: Clinical, p.101871. | ||
+ | |||
+ | If you have any questions, please email Muhan Shao at <code>muhan@jhu.edu</code>. | ||
+ | |||
+ | {{h3|Processing steps}} | ||
+ | The singularity takes T1-w RAW brain MRI (nifti file) as input and performs the following steps: | ||
+ | * N4 bias field correction from [http://stnava.github.io/ANTs/ ANTs]. The bias field is estimated using a weight image calculated from a brain mask generated by [https://www.nitrc.org/projects/robex ROBEX]. | ||
+ | * Rigid registration to [http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009 ICBM2009c] nonlinear symmetric template using the ANTs package. The template images were resampled to have resolution of 0.8x0.8x0.8mm. | ||
+ | * Skull-stripping using ROBEX. | ||
+ | * Brain ventricle parcellation on the skull-stripped MNI-registered T1-w MRI using the method described in "Shao, M., et al., 2019. Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly. NeuroImage: Clinical, p.101871". | ||
+ | * The parcellation is transformed back to the original space using ANTs with the "MultiLabel" interpolation. | ||
+ | |||
+ | {{h3|Output file structures}} | ||
+ | The processing will create subfolders '''<code>n4/</code>''', '''<code>mni/</code>''', and '''<code>parc/</code>''' under the output folder. The final parcellation result is directly under the output folder. | ||
+ | * '''<code>*n4.nii.gz</code>''' is the bias field corrected image | ||
+ | * '''<code>*n4_mni.nii.gz</code>''' is the image in the MNI space | ||
+ | * '''<code>*n4_mni_strip.nii.gz</code>''' is the skull-stripped image | ||
+ | * '''<code>*n4_mni_strip_seg.nii.gz</code>''' is the parcellation from the convolutional neural networks | ||
+ | * '''<code>*n4_mni_strip_seg_inverse.nii.gz</code>''' is the final parcellation result in the original image space. | ||
+ | |||
+ | {{h3|Installation}} | ||
+ | * Install [https://sylabs.io/guides/3.7/user-guide/quick_start.html#quick-installation-steps Singularity 3.7] | ||
+ | |||
+ | {{h3|Usage}} | ||
+ | * The Singularity image can only run on '''CPU''' although [https://www.tensorflow.org TensorFlow] and [https://keras.io Keras] are used, because the Singularity image only contains the CPU version of TensorFlow. | ||
+ | |||
+ | * Assume that the Singularity image is <code>/path/to/simg/ventricle-parcellation.simg</code>, the image to parcellate is <code>~/image.nii.gz</code>, and the output folder is <code>~/output</code> | ||
+ | |||
+ | singularity run /path/to/simg/ventricle-parcellation.simg -i ~/image.nii.gz -o ~/output | ||
+ | |||
+ | * If the <code>image.nii.gz</code> is under <code>/path/to/image</code> which is not under your home directory | ||
+ | |||
+ | singularity run -B /path/to/image:/mnt /path/to/simg/ventricle-parcellation.simg -i /mnt/image.nii.gz -o /mnt/output | ||
+ | |||
+ | * Print help | ||
+ | singularity run ~/ventricle-parcellation.simg -h | ||
+ | |||
+ | {{h3|Brain ventricle labels}} | ||
+ | "51": "Right lateral ventricle", | ||
+ | "52": "Left lateral ventricle", | ||
+ | "4": "third ventricle", | ||
+ | "11": "fourth ventricle" |
Latest revision as of 00:47, 3 July 2022
Brain Ventricle Parcellation with Convolutional Neural Network
This singularity image contains the code and trained model for brain ventricle parcellation using a convolutional neural network. The singularity image can be downloaded in the following link (~1.7GB): Singularity image for brain ventricle parcellation (v4).
If you use this work, please cite:
- Shao, M., Han, S., Carass, A., Li, X., Blitz, A.M., Shin, J., Prince, J.L. and Ellingsen, L.M., 2019. Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly. NeuroImage: Clinical, p.101871.
If you have any questions, please email Muhan Shao at muhan@jhu.edu
.
Processing steps
The singularity takes T1-w RAW brain MRI (nifti file) as input and performs the following steps:
- N4 bias field correction from ANTs. The bias field is estimated using a weight image calculated from a brain mask generated by ROBEX.
- Rigid registration to ICBM2009c nonlinear symmetric template using the ANTs package. The template images were resampled to have resolution of 0.8x0.8x0.8mm.
- Skull-stripping using ROBEX.
- Brain ventricle parcellation on the skull-stripped MNI-registered T1-w MRI using the method described in "Shao, M., et al., 2019. Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly. NeuroImage: Clinical, p.101871".
- The parcellation is transformed back to the original space using ANTs with the "MultiLabel" interpolation.
Output file structures
The processing will create subfolders n4/
, mni/
, and parc/
under the output folder. The final parcellation result is directly under the output folder.
*n4.nii.gz
is the bias field corrected image*n4_mni.nii.gz
is the image in the MNI space*n4_mni_strip.nii.gz
is the skull-stripped image*n4_mni_strip_seg.nii.gz
is the parcellation from the convolutional neural networks*n4_mni_strip_seg_inverse.nii.gz
is the final parcellation result in the original image space.
Installation
- Install Singularity 3.7
Usage
- The Singularity image can only run on CPU although TensorFlow and Keras are used, because the Singularity image only contains the CPU version of TensorFlow.
- Assume that the Singularity image is
/path/to/simg/ventricle-parcellation.simg
, the image to parcellate is~/image.nii.gz
, and the output folder is~/output
singularity run /path/to/simg/ventricle-parcellation.simg -i ~/image.nii.gz -o ~/output
- If the
image.nii.gz
is under/path/to/image
which is not under your home directory
singularity run -B /path/to/image:/mnt /path/to/simg/ventricle-parcellation.simg -i /mnt/image.nii.gz -o /mnt/output
- Print help
singularity run ~/ventricle-parcellation.simg -h
Brain ventricle labels
"51": "Right lateral ventricle", "52": "Left lateral ventricle", "4": "third ventricle", "11": "fourth ventricle"