Download MSc project proposal "Atlas-based Micro-MRI whole-body

MSc project proposal "Atlas-based Micro-MRI whole-body mouse segmentation"
Contact: Boudewijn Lelieveldt ([email protected]) or Artem Khmelinskii ([email protected])
Background:
In pre-clinical research, different imaging modalities are used for the in vivo visualization of functional and
anatomical information in small animals (mice, rats). Structural imaging modalities such as magnetic resonance
imaging (MRI), computed tomography (CT), and ultrasound provide detailed depictions of anatomy; positron
emission tomography (PET), single photon emission computed tomography (SPECT), and specialized MRI
protocols add functional information. In addition, optical imaging modalities, such as bioluminescence imaging
(BLI) and near-infrared (NIR) fluorescence imaging, offer a high sensitivity in visualizing molecular processes invivo. In combination these modalities enable the visualization of the cellular function and the follow-up of
molecular processes in living organisms without perturbing them.
Due to the high number of existing imaging modalities a new, different challenge emerged:
How to best combine and analyze all this data?
One other very important factor is the postural variability: there is no standardized protocol for imaging. If a subject
is imaged using different imaging modalities and protocols, during follow-up studies or if different animals are used,
the subject is positioned in different ways and postural variations occur (e.g.: of the head, back and front limbs, etc.)
making it difficult for further data analysis and exploration. See Figure 1.
(a)
(b)
(c)
S1, T0
S1, T1
S2, T0
Fig. 1 Illustration of the postural variability (limbs,
head) that occurs in follow-up and cross-sectional
molecular imaging studies: top and middle mouse same subject S1, 2 time steps T0 and T1; bottom
mouse - different subject S2
Fig. 2 Illustration of the 3 segmented skeletons for
each atlas: (a) – MOBY, (b) – Digimouse and (c) – SD
Rat
In our section we addressed the above mentioned problems by introducing articulations in 3 existing whole-body
atlases. A kinematic model was built for each atlas where bones in each skeleton are manually segmented and
labeled and the corresponding degrees of freedom for each joint are defined [Khmelinskii and Baiker et al. 2010]
(Figure 2). Mapping the data to this articulated atlases has the advantage that all the different imaging modalities can
be automatically registered to a common anatomical reference, postural variations can be corrected and the different
animals (according to strain, size, age, body fat percentage) can be scaled properly.
For Micro-CT data an automatic algorithm to segment an entire animal into bones and soft tissues for Micro-CT
data was developed [Baiker et al. 2010]. Currently we are focusing on extending the proposed approach to other
modalities like SPECT, PET and MRI
MSc. assignment:
The challenge for the student is to:
Using the articulated anatomical model of a mouse, automatically segment the entire MRI data of the animal into
skin, bones and soft tissue - organs. This is a very interesting problem, since the contrast in MRI data is completely
different than the one present in CT data. In MRI we see mostly soft tissue - organs while in CT we see mostly bone
(Figure 3).
Vs.
Fig. 3 MRI data (left) versus CT data (right): high soft-tissue contrast versus high Bone contrast
The student should:
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Review the existing work about whole-body image segmentation and MRI image registration/segmentation
in particular
Extend the atlas-based algorithm developed for CT data [Baiker et al. 2010] to MRI, by looking for new,
significant features
The implementation will be done using for e.g. MATLAB, combined with elastiX (an easy to use but
versatile rigid and non-rigid registration platform)
Perform validation experiments of the algorithm and finalize the assignment with the writing of an
international conference/journal paper
References:
[Baiker et al. 2010], Atlas-based whole-body segmentation of mice from low-contrast Micro-CT data, Medical
Image Analysis, pp 723-737
[Khmelinskii et al. 2010], Articulated Whole-Body Atlases for Small Animal Image Analysis: Construction and
Applications, Molecular Imaging and Biology