Deans' stroke musings: Similarity Metric Learning for 2D to 3D Registration of Brain Vasculature

Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Thursday, December 15, 2016

Similarity Metric Learning for 2D to 3D Registration of Brain Vasculature

Does your doctor give you any idea of the exact location and size of your dead area and penumbra? Or do you get the crap diagnosis of 'you had a large/massive/medium/small stroke' Did it impact your white matter?
http://link.springer.com/chapter/10.1007/978-3-319-50835-1_1

Alice Tang
, Fabien Scalzo

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Abstract

2D to 3D image registration techniques are useful in the treatment of neurological diseases such as stroke. Image registration can aid physicians and neurosurgeons in the visualization of the brain for treatment planning, provide 3D information during treatment, and enable serial comparisons. In the context of stroke, image registration is challenged by the occluded vessels and deformed anatomy due to the ischemic process. In this paper, we present an algorithm to register 2D digital subtraction angiography (DSA) with 3D magnetic resonance angiography (MRA) based upon local point cloud descriptors. The similarity between these local descriptors is learned using a machine learning algorithm, allowing flexibility in the matching process. In our experiments, the error rate of 2D/3D registration using our machine learning similarity metric (52.29) shows significant improvement when compared to a Euclidean metric (152.54). The proposed similarity metric is versatile and could be applied to a wide range of 2D/3D registration.