NiChart_DLMUSE
Citation
Doshi, Jimit, Guray Erus, Yangming Ou, Susan M Resnick, Ruben C Gur, Raquel E Gur, Theodore D Satterthwaite, Susan Furth, Christos Davatzikos, and Alzheimer’s Neuroimaging Initiative. 2016. “MUSE: MUlti-Atlas Region Segmentation Utilizing Ensembles of Registration Algorithms and Parameters, and Locally Optimal Atlas Selection.” NeuroImage 127: 186–95. https://doi.org/10.1016/j.neuroimage.2015.11.073.
Doshi, Jimit, Guray Erus, Yangming Ou, Susan M Resnick, Ruben C Gur, Raquel E Gur, Theodore D Satterthwaite, Susan Furth, Christos Davatzikos, and Alzheimer’s Neuroimaging Initiative. 2016. “MUSE: MUlti-Atlas Region Segmentation Utilizing Ensembles of Registration Algorithms and Parameters, and Locally Optimal Atlas Selection.” NeuroImage 127: 186–95. https://doi.org/10.1016/j.neuroimage.2015.11.073.
NiChart_DLMUSE performs deep-learning based brain extraction and segmentation on T1-weighted images. This is based on the MUSE framework (MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection; Doshi et al. 2016). All shared AI2D data were processed with a BIDS-app wrapper of NiChart_DLMUSE:
Brain extraction is done using DLICV.
Brain segmentation is done using DLMUSE.
The outputs include:
- ICV mask.
- Segmented T1w in native space.
- JSON files containing ROIs’ volumes.
- HTML summary for visual quality control of DLICV and DLMUSE outputs.
This package uses nnU-Net v2 as a basis model architecture for the deep learning parts.