=Paper=
{{Paper
|id=None
|storemode=property
|title=Learning and Discovery of Clinically Useful Information from Images
|pdfUrl=https://ceur-ws.org/Vol-715/bvm2011_1.pdf
|volume=Vol-715
}}
==Learning and Discovery of Clinically Useful Information from Images==
<pdf width="1500px">https://ceur-ws.org/Vol-715/bvm2011_1.pdf</pdf>
<pre>
    Learning and Discovery of Clinically Useful
             Information from Images

                                 Daniel Rueckert

                            Department of Computing
                    Imperial College London, United Kingdom
                          d.rueckert@imperial.ac.uk




Three-dimensional (3D) and four-dimensional (4D) imaging plays an increas-
ingly important role in computer-assisted diagnosis, intervention and therapy.
However, in many cases the interpretation of these images is heavily dependent
on the subjective assessment of the imaging data by clinicians. Over the last
decades image registration has transformed the clinical workflow in many areas
of medical imaging. At the same time, advances in machine learning have trans-
formed many of the classical problems in computer vision into machine learning
problems. This talk will focus on the convergence of image registration and ma-
chine learning techniques for the discovery and quantification of clinically useful
information from medical images. In the first part of part of this talk I will
give an overview of recent advances in image registration. The second part will
focus on the how the combination of machine learning and image registration
can be used to address a wide range of challenges in medical image analysis
such as segmentation and shape analysis. To illustrate this I will show several
examples such as the segmentation of neuro-anatomical structures, the discov-
ery of biomarkers for neurodegenerative diseases such as Alzheimer’s and the
quantification of temporal changes such as growth in the developing brain.

</pre>