Medical Image Understanding: Advancing the State-of-the-Art through Integration of Medical Knowledge Fast and robust anatomical object detection and tracking
are
fundamental tasks in medical image analysis that support
the
entire clinical imaging workflow, from screening and
diagnosis to patient stratification, therapy planning,
intervention and follow-up. Most of the current solutions
for medical image analysis are, however, based on generic
combinations of machine learning and optimization that do
not include a priori clinical knowledge about the
patient's
body and potential disease. Such solutions are generic,
unconstrained and suboptimal. A strong potential of
improvement, in terms of both the quality of image
interpretation and explanation of the results can be
achieved by constraining the underlying problems
according
to the anatomy and physiology of the human body and the
semantics of the disease space. We propose to formulate
medical image analysis as an image understanding problem
and
solve it by leveraging modern machine learning and
knowledge
representation theories. We target an optimal balance
between the interpretation of new image data and
knowledge-driven models explaining the data generation. The
conjecture
is that by constraining the space of solutions through
the
injection of medical knowledge and reasoning, one can
achieve superior results versus the generic ones, derived
through supervised/unsupervised learning and
optimization. The new paradigm will be tested on relevant
medical image understanding problems, covering multiple
image modalities.
| Projektleitung: Dr. Dorin Comaniciu, Prof. Dr.-Ing. habil. Andreas Maier, Prof. Dr.-Ing. Joachim Hornegger
Beteiligte: Dr. Bogdan Georgescu, Dr.-Ing. Florin Cristian Ghesu
Stichwörter: image understanding; machine learning; prior knowledge modeling; image processing
Laufzeit: 1.6.2015 - 31.5.2018
Förderer: Siemens Healthcare GmbH
Kontakt: Ghesu, Florin Cristian Telefon +49 9131 85 25246, Fax +49 9131 85 27270, E-Mail: florin.c.ghesu@fau.de
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