Musculoskeletal diseases are responsible for more years lived with disability than any other kind of chronic condition. We develop image analysis algorithms that assist radiologists and orthopedic surgeons in recognizing musculoskeletal conditions early and in making well-informed treatment decisions based on quantitative imaging data. This research line was led by Nikolas Lessmann.
We develop AI-based MRI analysis software for treatment decision support in patients with chronic degenerative low back pain.
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Developing image analysis algorithms that automatically detect osteoporotic vertebral fractures.
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Automatic quantification of muscle and fat tissue in 3D CT and MR images
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Modeling of deformities in adolescent idiopathic scoliosis to improve segmentation
Read more →Several algorithms that this group has developed can be tried on the grand-challenge.org platform:
Segmentation and labeling of the vertebrae in CT scans with arbitrary field of view.
A neural network that assesses vertebral fractures according to the Genant classification
Score from 0 to 100 that expresses how abnormal the shape of a vertebra is
Segments and labels the ribs in CT images
Segments the left and right femur in CT images
Segments the left and right hip bones in CT images
Segments the left and right tibia in CT images
Segments the carpal bones and radius and ulna in (dynamic) CT scans of the wrist.
Locates L3 in a CT scan and measures the area of subcutaneous, visceral, and intermuscular adipose tissue and smooth muscle. The average Hounsfield Units of each area are also computed.
