Výsledky vyhledávání - "Heit, Jeremy"

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Autor: Chang, Yilong, Li, Qi, Wu, Shuai, Pulli, Benjamin, Samli, Darren, Yock, Paul, Heit, Jeremy J., Zhao, Ruike Renee

Blockage of blood flow in arteries or veins by blood clots can lead to serious medical conditions. Mechanical thrombectomy (MT), minimally invasive endovascular procedures that utilize aspiration, stent retriever, or cutting mechanisms for clot remov

Externí odkaz: http://arxiv.org/abs/2407.18495

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Random Expert Sampling for Deep Learning Segmentation of Acute Ischemic Stroke on Non-contrast CT

Autor: Ostmeier, Sophie, Axelrod, Brian, Pulli, Benjamin, Verhaaren, Benjamin F. J., Mahammedi, Abdelkader, Liu, Yongkai, Federau, Christian, Zaharchuk, Greg, Heit, Jeremy J.

Purpose: Multi-expert deep learning training methods to automatically quantify ischemic brain tissue on Non-Contrast CT Materials and Methods: The data set consisted of 260 Non-Contrast CTs from 233 patients of acute ischemic stroke patients recruite

Externí odkaz: http://arxiv.org/abs/2309.03930

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Non-inferiority of Deep Learning Acute Ischemic Stroke Segmentation on Non-Contrast CT Compared to Expert Neuroradiologists

Autor: Ostmeier, Sophie, Axelrod, Brian, Verhaaren, Benjamin F. J., Christensen, Soren, Mahammedi, Abdelkader, Liu, Yongkai, Pulli, Benjamin, Li, Li-Jia, Zaharchuk, Greg, Heit, Jeremy J.

Publikováno v: Sci Rep 13, 16153 (2023)

To determine if a convolutional neural network (CNN) deep learning model can accurately segment acute ischemic changes on non-contrast CT compared to neuroradiologists. Non-contrast CT (NCCT) examinations from 232 acute ischemic stroke patients who w

Externí odkaz: http://arxiv.org/abs/2211.15341

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USE-Evaluator: Performance Metrics for Medical Image Segmentation Models with Uncertain, Small or Empty Reference Annotations

Autor: Ostmeier, Sophie, Axelrod, Brian, Bertels, Jeroen, Isensee, Fabian, Lansberg, Maarten G., Christensen, Soren, Albers, Gregory W., Li, Li-Jia, Heit, Jeremy J.

Performance metrics for medical image segmentation models are used to measure the agreement between the reference annotation and the predicted segmentation. Usually, overlap metrics, such as the Dice, are used as a metric to evaluate the performance

Externí odkaz: http://arxiv.org/abs/2209.13008

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