Difference between revisions of "Cerebellum CNN"
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{{h2|Cerebellum Parcellation with Convolutional Neural Networks}} | {{h2|Cerebellum Parcellation with Convolutional Neural Networks}} | ||
This work originally appeared at {{pub|conf=spie2019}} It can be downloaded as a Singularity image from {{iacl|~aaron/data/composed_parc.simg|Cerebellar CNN Segmentation Code}}. | This work originally appeared at {{pub|conf=spie2019}} It can be downloaded as a Singularity image from {{iacl|~aaron/data/composed_parc.simg|Cerebellar CNN Segmentation Code}}. | ||
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| + | {{h3|What the Singularity image can do}} | ||
| + | The Singularity performs the following steps: | ||
| + | * [https://www.nitrc.org/projects/robex ROBEX] is used to estimate a brain mask. This mask is then smoothed to generate a brain weight image. | ||
| + | * N4 from [http://stnava.github.io/ANTs/ ANTs] is used to perform the bias field correction. The bias field is estimated using the weight image calculated above. | ||
| + | * The images are rigidly registered to the [http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009 ICBM2009c] nonlinear symmetric template using the ANTs package. The first MPRAGE image from the longitudinal series of a subject is registered to this template and other images with different contrasts/from the following sessions are registered to the first MPRAGE image. | ||
| + | * The cerebellum of an MNI-registered MPRAGE image is parcellated using the method described in "Shuo Han, et al., Cerebellum parcellation with convolutional neural networks, SPIE 2019 Medical Imaging Image Processing". | ||
| + | :* Removing the neck should improve the results, such as using `robustfov` from [https://fsl.fmrib.ox.ac.uk/fslcourse/lectures/practicals/intro2/index.html fsl], but it is not done in this singularity image. | ||
| + | * (OPTIONAL) Generate a HTML report file. To generate the report, the images are converted to .png files which could occupy a large hard disk space. | ||
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| + | * [https://www.gnu.org/software/parallel/ GNU Parallel] is used to run jobs in parallel. | ||
Revision as of 18:00, 23 January 2019
<meta name="title" content="Cerebellum Parcellation with Convolutional Neural Networks"/>
Cerebellum Parcellation with Convolutional Neural Networks
This work originally appeared at Proceedings of SPIE Medical Imaging (SPIE-MI 2019), San Diego, CA, February 16 - 21, 2019. It can be downloaded as a Singularity image from Cerebellar CNN Segmentation Code.
What the Singularity image can do
The Singularity performs the following steps:
- ROBEX is used to estimate a brain mask. This mask is then smoothed to generate a brain weight image.
- N4 from ANTs is used to perform the bias field correction. The bias field is estimated using the weight image calculated above.
- The images are rigidly registered to the ICBM2009c nonlinear symmetric template using the ANTs package. The first MPRAGE image from the longitudinal series of a subject is registered to this template and other images with different contrasts/from the following sessions are registered to the first MPRAGE image.
- The cerebellum of an MNI-registered MPRAGE image is parcellated using the method described in "Shuo Han, et al., Cerebellum parcellation with convolutional neural networks, SPIE 2019 Medical Imaging Image Processing".
- Removing the neck should improve the results, such as using `robustfov` from fsl, but it is not done in this singularity image.
- (OPTIONAL) Generate a HTML report file. To generate the report, the images are converted to .png files which could occupy a large hard disk space.
- GNU Parallel is used to run jobs in parallel.
