Automatic de-Skulling
Automatic de-Skulling provides tools that help anonymizing the 3D head image, so that the person cannot be recognized when displayed in a 2D-slice or 3D-volume view. This can be done as a progression from basic face-anonymizing, to de-skulling, to complete brain segmentation and extraction. Fully automatic brain segmentation and extraction is an area of on-going research.
Introduction
Full-scale de-skulling requires removal of all skin, muscle, skull, eyes, exterior blood vessels and nerve tissue. Brain segmentation goes further to define the exact components (white matter, gray matter, cerebellum, brain stem) to include in the final image. Although brain segmentation is not necessary to de-identify a face, several components of brain segmentation algorithms can be used as strategies for removing the identifying features of a face.
Background
In this algorithm we decided to err on the side of leaving too much data rather than removing data that was part of the brain. One of the key steps in the algorithm is segmenting the white-matter of the brain,using a technique described in the Skull-Striping algorithm [Sadananthan]. Given a good white-matter segmentation, the algorithm can reliability find a good skull segmentation without removing portions of the surface of the brain. A modified version of the Brain Extraction Tool (AlgorithmBrainExtractor) in MIPAV is then applied to further refine the brain surface.
The algorithm was tested on nearly two dozen images and on several examples of the different MRI modalities. The algorithm produced good segmentation for all the images except CT images. For the CT images the algorithm provides the option of using an atlas, or reference, image to generate the segmentation. The atlas image is first registered to the target image, then the algorithm is run on the atlas image and the result is applied to the target image. This worked well for the CT images tested, using a T1-weighted MRI image as the atlas.
The goal for this algorithm is to produce fully anonymous results for
- a variety of image types and modalities,
- all sage ranges from children to elderly,
- both healthy and non-healthy patient images.
Input Image Challenges
A significant challenge in creating the algorithm is the variety of input images. Much of the reference work in brain segmentation is done on T1 or T2 images only. Examples of different image types are listed and shown below.
- Computed Tomography (CT)
- Magnetic Resonance Images (MRI) including but not limited to:
- Diffusion-weighted (DWI), Diffusion-tensor (DTI), and High-angular-resolution (HARDI)
- Fluid attenuated inversion recovery MRI (FLAIR)
- Functional MRI (fMRI)
- Proton density MRI (PD)
- T1-weighted MRI
- T2-weighted MRI
