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G-RIPS Projects 2016 - Project - 1000shapes

Hosting Lab

Within the therapy planning group at ZIB we are dealing with a variety of medical data. To tackle the challenges of analyzing an always increasing amount of data and to provide software tools to automatically extract the relevant information out of it, we are investigating model based approaches (statistical shape and appearance models) as well as machine learning techniques (regression, classification, and semi-supervised learning), which can then be used in a number of applications such as scene recognition from photographs, object recognition in images, or automatic diagnosis from medical image data.

Sponsor

The project is in close collaboration with 1000shapes GmbH, a ZIB spin-off that transfers research in life sciences into products for clinical applications. Within the project, algorithms are to be developed within an existing software framework and tested on clinical image data. The successful applicant will have the opportunity to perform research in medical image computing within the ZIB research group therapy planning while obtaining professional support from 1000shapes in software development and implementing algorithms within existing software frameworks. Within the project, students will have the opportunity to experience medical research in combination with industry-strength software development.

Project

Building on a large medical image database, students will investigate new machine-learning techniques, i.e. the application of regression forests, to analyze and classify features or disease patterns in medical image data.

Background:

Osteoarthritis (OA) is the most common form of arthritis and the major cause of activity limitation and physical disability in older people. Today, 35 million people (13 percent of the U.S. population) are 65 and older, and more than half of them have radiological evidence of osteoarthritis in at least one joint. By 2030, 20 percent of Americans (about 70 million people) will have passed their 65th birthday and will be at risk for OA. The Osteoarthritis Initiative (OAI) is a multi-center, longitudinal, prospective observational study of knee osteoarthritis (KOA), providing a database for osteoarthritis that includes clinical evaluation data, radiological (x-ray and magnetic resonance) images, and a bio-specimen repository from over 5000 patients. Radiological images are a rich source of information of both anatomical and physiological properties of the human body. This information has great potential both for developing a better understanding of disease onset and progression, as well as improving future therapeutic concepts. However, such vast amounts of data require automated image processing approaches.

Challenges: The overall goal is to automatically extract anatomical shape and appearance (image intensity) information such as bone, cartilage, tendons and muscle tissues from the radiological images. To this end, machine learning combined with model-based approaches shall be adopted. In particular, the random forest regression voting method has been shown to be very successful in localizing landmarks in 2D x-ray images. However, this approach is computationally quite expensive both in terms of CPU time and particularly memory consumption. Furthermore, 3D magnetic resonance images have a significantly larger range of variation in intensity due to their acquisition process.

Aim: The goal of this project is to evaluate existing implementations of the random forest regression voting methods and to develop and evaluate own implementations of it for 3D shape detection in magnetic resonance tomography data. Results of this work may become a basis for improving existing segmentation, classification, and diagnosis algorithms that are currently under development or even in use by 1000shapes.

Requirements

The prospective participant should:

  • have a background in computer science, bio-engineering, mathematics or physics,
  • have experience with collaborative code development in C++ on Windows or Linux,
  • have attended classes in the area of machine learning, computer vision, image processing or statistics,
  • be acquainted with software for processing large medical image datasets,
  • have fun working in teams, both with colleagues from academia and industry,
  • be able to communicate fluently in English
Last modified onTuesday, 02 February 2016 12:58
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