Oshinski’s group in a previous research study. The teams worked with image data that has been collected and labeled by Dr. This project aims at exploring the use of machine learning algorithms to automize parts of the image processing pipeline, most critically the segmentation of the image into different brain regions. Automize move manual#This method may be more accurate in diagnosing Chiari, however, the large number of manual processing steps may limit its use as a wide-spread screening tool. of Radiology) collected data about how Chiari patients have more brain movement in the cerebellum and brainstem than controls. Using an MRI technique called DENSE (shown below) that records how the brain moves, Dr. While it can be difficult to diagnose Chari from anatomical images, a promising new direction for diagnosis is by looking at brain movement. Did Somebody Say Chiari Malformation?Ĭhiari malformation is a condition in which brain tissue extends into the spinal canal. In doing so, we investigated two approaches one that segments the given image by aligning and comparing the image to a known atlas and another that segments through deep learning. We created an algorithm that can accurately and efficiently segment the cerebellum and brain stem from a magnitude image and use displacement data to classify whether or not a patient has the Chiari malformation. Collaboration Never Sleeps.ĭuring our summer research at Emory University 2021 REU/RET program, our group focused on the algorithmic diagnosis of Chiari malformation from DENSE MRIs. In addition to this post, the team has also created slides for a midterm presentation, a poster blitz video, code, and a paper. This post was written by Justin Smith, Elle Buser, Emma Hart, and Ben Hueneman and published with minor edits.
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