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Medical image registration guided by target lesion

机译:目标病变引导下的医学图像配准

摘要

Machine logic (for example, software) for registering multiple medical images, each showing a common lesion, with each other. In performing this registration, registration points are chosen to be both: (i) outside of image portion that is potentially compromised by the lesion (in any of the multiple images); and (ii) as close to the lesion as possible. However, in at least one of the images the extent of the lesion is not known—so, in order to accommodate this uncertainty about the lesion boundaries, lesion predicting machine logic rules are used to predict the size, shape and/or location of the lesion. Machine learning is used to intermittently adjust and improve the lesion predicting machine logic rules.
机译:机器逻辑(例如,软件)用于相互注册多个医学图像,每个图像显示一个共同的病变。在执行此配准时,配准点应选择为:(i)在可能受到病变损害的图像部分之外(在多个图像中的任何一个中); (ii)尽可能靠近病变。但是,在至少一张图像中,病变的程度是未知的-因此,为了适应有关病变边界的这种不确定性,病变预测机器逻辑规则用于预测肿瘤的大小,形状和/或位置病变。机器学习用于间歇地调整和改善病变预测机器逻辑规则。

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