Combining unsupervised and supervised learning for predicting the final stroke lesion
@article{Pinto2020CombiningUA, title={Combining unsupervised and supervised learning for predicting the final stroke lesion}, author={Adriano Pinto and S{\'e}rgio Pereira and Raphael Meier and Roland Wiest and Victor Alves and Mauricio Reyes and Carlos A. Silva}, journal={Medical image analysis}, year={2020}, volume={69}, pages={ 101888 } }
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References
SHOWING 1-10 OF 69 REFERENCES
Stroke Lesion Outcome Prediction Based on MRI Imaging Combined With Clinical Information
- MedicineFront. Neurol.
- 2018
This work proposes an automatic deep learning-based method for stroke lesion outcome prediction, which resides in the combination of multi-modal Magnetic Resonance Imaging maps with non-imaging clinical meta-data: the thrombolysis in cerebral infarction scale, which categorizes the success of recanalization, achieved through mechanical thrombectomy.
Enhancing clinical MRI Perfusion maps with data-driven maps of complementary nature for lesion outcome prediction
- MedicineMICCAI
- 2018
The ability of the proposed approach to improve prediction of tissue at risk before therapy, as compared to only using the standard clinical perfusion maps, is demonstrated, suggesting on the potential benefits of the data-driven raw perfusion data modelling approach.
Fully automated stroke tissue estimation using random forest classifiers (FASTER)
- Medicine, BiologyJournal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
- 2017
It is concluded that prediction of tissue damage in the event of either persistent occlusion or immediate and complete recanalization, from spatial features derived from MRI, provides a substantial improvement beyond predefined thresholds.
Towards automatic MRI volumetry for treatment selection in acute ischemic stroke patients
- Medicine2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
- 2014
This work proposes a novel, more elaborate image analysis approach that is based on supervised classification methods to automatically segment and predict the extent of the tissue compartments of interest (healthy, infarct, penumbra regions).
Prediction of Tissue Outcome and Assessment of Treatment Effect in Acute Ischemic Stroke Using Deep Learning
- MedicineStroke
- 2018
A predictive model capable of automatically identifying and combining acute imaging features to accurately predict final lesion volume and seems to be able to differentiate outcomes based on treatment strategy with the volume of final infarct being significantly different.
ISLES 2015 ‐ A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI
- Medicine, Computer ScienceMedical Image Anal.
- 2017
Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning
- MedicineMedical Image Anal.
- 2020
ISLES 2016 and 2017-Benchmarking Ischemic Stroke Lesion Outcome Prediction Based on Multispectral MRI
- MedicineFront. Neurol.
- 2018
The Ischemic Stroke Lesion Segmentation challenge, which has ran now consecutively for 3 years, aims to address the problem of comparability by providing a uniformly pre-processed data set and allowing new approaches to be compared directly via the online evaluation system.
Enhancing interpretability of automatically extracted machine learning features: application to a RBM‐Random Forest system on brain lesion segmentation
- Computer ScienceMedical Image Anal.
- 2018
Acute Stroke Imaging Part I: Fundamentals
- MedicineCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques
- 2010
The basic principles underlying acquisition and interpretation of these newer imaging modalities in the setting of acute stroke are outlined and how these techniques can be used to rationally select appropriate patients for thrombolysis based on pathophysiological data is discussed.