Publications by authors named "Noah Stier"
Detection of Hyperperfusion on Arterial Spin Labeling using Deep Learning.
Proceedings (IEEE Int Conf Bioinformatics Biomed)
November 2015
Hyperperfusion detected on arterial spin labeling (ASL) images acquired after acute stroke onset has been shown to correlate with development of subsequent intracerebral hemorrhage. We present in this study a quantitative hyperperfusion detection model that can provide an objective decision support for the interpretation of ASL cerebral blood flow (CBF) maps and rapidly delineate hyperperfusion regions. The detection problem is solved using Deep Learning such that the model relates ASL image patches to the corresponding label (normal or hyperperfused).
View Article and Find Full Text PDFDeep Learning of Tissue Fate Features in Acute Ischemic Stroke.
Proceedings (IEEE Int Conf Bioinformatics Biomed)
November 2015
Article Synopsis
- The prediction of tissue survival in acute ischemic stroke treatment is crucial for balancing risks and benefits of interventions like clot retrieval.
- A new deep learning model analyzes local patches from MRI hypoperfusion features to predict tissue fate right after stroke symptoms begin.
- Testing on 19 patients demonstrates that this model outperforms traditional single-voxel regression methods, leading to more accurate predictions.