This release version was used in the experiments of my PhD thesis, and the “Unbiased construction of a temporally consistent morphological atlas of neonatal brain development” (doi:10.1101/251512). It also includes the tools used in the minimal processing pipeline of the dHCP (doi:10.1101/125526).

Axial mid-sections of mean intensity and shape templates at 40 weeks PMA.

After providing some background on brain atlas construction, the talk focuses on our novel group-wise approach for the construction of an unbiased spatio-temporal brain with improved temporal consistency, lower computational cost, and considerably higher cortical detail than previous neonatal atlas construction techniques. It is a summary of our work detailed in Schuh et al., “Unbiased construction of a temporally consistent morphological atlas of neonatal brain development”, preprint available on bioRxiv (doi:10.1101/251512).

3D reconstruction of the cortical surface and calculated features from a seven-month, eight-month and nine-month baby brain MRI.

The first batch of data acquired and processed as part of the Developing Human Connectome Project has been released, with articles about the project published by BBC and The Guardian. These feature colourful renders of cortical surfaces reconstructed from MRI scans of neonatal brains using our approach presented at ISBI 2017.

A deformable model for the reconstruction of the neonatal cortex

Two stages of deformable mesh reconstruction of one hemisphere of a white matter surface. Colour depicts residual distance.

We present a method based on deformable meshes for the reconstruction of the cortical surfaces of the developing human brain at the neonatal period. It employs a brain segmentation for the reconstruction of an initial inner cortical surface mesh. Errors in the segmentation resulting from poor tissue contrast in neonatal MRI and partial volume effects are subsequently accounted for by a local edge-based refinement. We show that the obtained surface models define the cortical boundaries more accurately than the segmentation. The surface meshes are further guaranteed to not intersect and subdivide the brain volume into disjoint regions. The proposed method generates topologically correct surfaces which facilitate both a flattening and spherical mapping of the cortex.

This release gets rid of bugs, runs on Linux, OS X, and Windows, and provides a Python package for execution of MIRTK commands in a pipeline script. The commands now support the NIfTI-2 image file format for large images and the GIFTI file format for cortical surface meshes as used by the HCP project. The build system has further improved with CMake BASIS 3.3 and automated builds help to keep the project stable.

An Ubuntu 14.04 Docker image with MIRTK pre-installed is available from Docker Hub the quickest way to getting to know MIRTK or to perform reproducible experiments in a confined environment!

See Release Notes for a complete list of changes.

See Release Notes for more information.