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3-6 month Internship in Neuroimaging
The Neuroimaging Group of the Balgrist Center in Zurich offers a 3-6 month internship in neuroimaging. The Balgrist Center is a fully equipped clinical and academic center for the acute care and rehabilitation of patients with any level and severity of spinal cord injury. It serves as a regional and international referral center for SCI with a focus in neurology and neuroimaging. The student will be tightly integrated in our world-leading lab at University of Zurich, primarily supervised by Dr. Freund.
The spinal cord is often affected by inflammatory and neurodegenerative processes leading to irreversible tissue loss. As the spinal cord is the main pathway for information connecting the brain with the peripheral nervous system, spinal cord atrophy has a major impact on patients’ clinical status. For this reason, quantification of spinal cord atrophy by measuring the gray and white matter area might provide useful surrogate markers to monitor disease progression and improve prognosis.
Gray (GM) and white matter (WM) areas of the spinal cord are computed by means of segmentation of the spinal cord magnetic resonance imaging (MRI) scans. The gold standard technique is manual segmentation, which is very accurate with an experienced rater, but is extremely time-consuming and subject to intra- and inter-rater variability. To make segmentation feasible on large clinical data and to increase reliability and comparability, automatic segmentation methods are highly desirable. While robust and reliable algorithms exist for automatic delineation of the gray and white matter in the brain, these algorithms often fail in the spinal cord due to its small size and reduced GM/WM contrast. In recent years, however, several automatic segmentation algorithms have been developed specifically for the spinal cord.
The spinal cord is often affected by inflammatory and neurodegenerative processes leading to irreversible tissue loss. As the spinal cord is the main pathway for information connecting the brain with the peripheral nervous system, spinal cord atrophy has a major impact on patients’ clinical status. For this reason, quantification of spinal cord atrophy by measuring the gray and white matter area might provide useful surrogate markers to monitor disease progression and improve prognosis. Gray (GM) and white matter (WM) areas of the spinal cord are computed by means of segmentation of the spinal cord magnetic resonance imaging (MRI) scans. The gold standard technique is manual segmentation, which is very accurate with an experienced rater, but is extremely time-consuming and subject to intra- and inter-rater variability. To make segmentation feasible on large clinical data and to increase reliability and comparability, automatic segmentation methods are highly desirable. While robust and reliable algorithms exist for automatic delineation of the gray and white matter in the brain, these algorithms often fail in the spinal cord due to its small size and reduced GM/WM contrast. In recent years, however, several automatic segmentation algorithms have been developed specifically for the spinal cord.
The aim of the project is to implement these existing algorithms and test their performance on the acquired high-resolution spinal cord MRI data. The performance of each algorithm will be evaluated in comparison with the manual segmentation. Finally, the candidate will extend these segmentation algorithms from the cervical to the lumbar spinal cord.
The aim of the project is to implement these existing algorithms and test their performance on the acquired high-resolution spinal cord MRI data. The performance of each algorithm will be evaluated in comparison with the manual segmentation. Finally, the candidate will extend these segmentation algorithms from the cervical to the lumbar spinal cord.
If you are interested please send your CV to gergely.david@balgrist.ch or maryan.seif@balgrist.ch
If you are interested please send your CV to gergely.david@balgrist.ch or maryan.seif@balgrist.ch