• No results found

Automated morphometry of transgenic mouse brains in MR images Scheenstra, A.E.H.

N/A
N/A
Protected

Academic year: 2021

Share "Automated morphometry of transgenic mouse brains in MR images Scheenstra, A.E.H."

Copied!
2
0
0

Bezig met laden.... (Bekijk nu de volledige tekst)

Hele tekst

(1)

Automated morphometry of transgenic mouse brains in MR images

Scheenstra, A.E.H.

Citation

Scheenstra, A. E. H. (2011, March 24). Automated morphometry of transgenic mouse brains in MR images. Retrieved from https://hdl.handle.net/1887/16649

Version: Corrected Publisher’s Version

License: Licence agreement concerning inclusion of doctoral thesis in the Institutional Repository of the University of Leiden

Downloaded from: https://hdl.handle.net/1887/16649

Note: To cite this publication please use the final published version (if applicable).

(2)

Propositions

1. A mouse brain atlas provides all the a priori knowledge that is required for mouse brain morphometry. This Thesis

2. The 3D Moore-Rayleigh test can be used in deformation-based morphometry to locally detect the inter-group variation between two groups of mice and still provides sufficient power for the multiple-test correction. This Thesis

3. The 3D Moore-Rayleigh test gives an equal performance as permutation testing and is faster when it is applied for testing group differences with deformation-based morphometry.

This Thesis

4. Conclusions made for separate voxels should only be generalized to the whole brain if all possible sources of error are retrospectively excluded. This Thesis

5. Characterization using deformation-based morphometry can be global, pertaining to the entire field as a single observation, or can proceed on a voxel-by-voxel basis to make inferences about regionally specific positional differences. Ashburner and Friston, NeuroImage 11:805-21 (2000)

6. Our knowledge of the brain is increasing in leaps and bounds. Whether the mind can ever finally know itself, or whether it will stay a step ahead of its pursuer, like the tortoise pursued by the Achilles in Zeno’s paradox, remains a question for the philosophers.

A.P. Holmes PhD-thesis

7. A good segmentation result is in the eye of the beholder.

8. The accuracy of an image processing algorithm is upper bound by the sum of the possible errors and uncertainties of all algorithms in the image processing pipeline.

9. Social media owe their rapid growth to those who think others are interested in their amazing lives and those who want others to believe they have fascinating lives.

10. For what do we live, but to make sport for our neighbours, and laugh at them in our turn?

Mr. Bennet in Pride and Prejudice, Jane Austen 11. The unexamined life is not worth living. Socrates

Automated morphometry of transgenic mouse brains in MR images Alize Scheenstra

Referenties

GERELATEERDE DOCUMENTEN

This is more complex because continuous input and continuous output take place simultaneously and an input-output conformance relation defines whether the output allowed by

Testing through TSVs and testing for wafer thinning defects as part of the pre-bond die test requires wafer probe access on thinned wafers, which brings about a whole new set

Components that are part of the ISFTCM are: Total number of testers and navigators with hourly rate, number of test cases, number of test-runs, test environment costs, license and

There needs to be some assurance that this test client works on most RESTful APIs and that it also adheres to the previously mentioned requirements.. It would be undoable to test it

15 The proof of Meinshausen’s method that we have given, is still correct for this adaptation of the method... He obtained better lower bounds for larger n. The tables suggest

License: Licence agreement concerning inclusion of doctoral thesis in the Institutional Repository of the University of Leiden. Downloaded

Automated morphometry of transgenic mouse brains in MR images Scheenstra, Alize Elske Hiltje.. Printed by Ipskamp Drukkers, Enschede,

The main contributions of this work are (a) to investigate the methods currently applied for quantitative morphometry in mouse brain MR images and (b) to provide analytical tools