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首页> 外文会议>International Conference on Medical Image Computing and Computer Assisted Intervention >Detection and Delineation of Acute Cerebral Infarct on DWI Using Weakly Supervised Machine Learning
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Detection and Delineation of Acute Cerebral Infarct on DWI Using Weakly Supervised Machine Learning

机译：利用虚线监督机学习检测和描绘急性脑梗塞对DWI的影响

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Improved outcome in patients with ischemic stroke is achieved through acute diagnosis and early restoration of cerebral flow in appropriate patients. Diffusion-weighted MR imaging (DWI) plays a central role in these efforts by enabling rapid early localization and quantification of ischemic lesions. Automated detection and quantification can potentially accelerate diagnosis, improve treatment safety and efficacy and reduce costs. However, the manual quantification of acute ischemic stroke volumes for algorithm training is time consuming and imprecise. We present YNet as a novel fully-automated deep learning algorithm for detection and volumetric segmentation and quantification of acute cerebral ischemic lesions from DWI. The algorithm is a semi-supervised multi-tasking deep neural network architecture we developed that enables the combination of both weak labels derived from radiology report classification and manually delineated pixel level training data. The model is trained on a very large dataset of 10000 studies, achieves detection sensitivity 0.981, detection specificity 0.980 and segmentation Dice score 0.623 on a heterogeneous test set.
机译：在缺血性脑卒中患者改善结果通过急性诊断和合适的患者脑流的早日恢复实现。扩散加权成像（DWI）起着通过启用早期快速定位和缺血性损害的定量在这些努力中发挥中心作用。自动检测和定量有可能加速诊断，提高治疗安全性和有效性，降低成本。然而，急性缺血性卒中卷进行算法训练的人工量化是耗时和不准确的。我们提出新消息报作为从DWI急性脑缺血病变的检测和体积分割和量化的新型完全自动化的深的学习算法。该算法是一种半监督多任务，我们开发出了能够从影像报告得出的分类既软弱标签和手动划定像素级别的训练数据的组合深层神经网络结构。该模型是一个非常大的数据集10000个研究的训练，达到检测灵敏度0.981，检测特异性0.980和分割骰子得分0.623上的异质测试集。

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