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Uncertainty maps for segmentation in the presence of metal artifacts

机译:在存在金属伪影的情况下进行细分的不确定度图

摘要

When performing model-based segmentation on a 3D patient image (80), metal artifacts in the patient image (80), caused by metal in the patient's body, are detected, and a metal artifact reduction technique is performed to reduce the artifact(s) by interpolation projection data in the region of the artifact(s). The interpolated data is used to generate an uncertainty map for artifact-affected voxels in the image, and a mesh model (78) is conformed to the image to facilitate segmentation thereof. Internal and external energies applied to push and pull the model (78) are weighted as a function of the uncertainty associated with one or more voxels in the image (80). Iteratively, mathematical representations of the energies and respective weights are solved to describe an updated model shape that more closely aligns to the image (80).
机译:对3D患者图像( 80 )执行基于模型的分割时,会检测到患者图像( 80 )中由患者体内的金属引起的金属伪影,并且执行金属伪影减少技术以通过在伪影的区域中内插投影数据来减少伪影。插值数据用于为图像中受伪影影响的体素生成不确定性图,并且网格模型( 78 )符合图像以利于对其进行分割。根据与图像中一个或多个体素相关的不确定性( 80 )对用于推拉模型的内部和外部能量( 78 )进行加权。迭代地,求解能量和各个权重的数学表示形式,以描述更新的模型形状,该形状与图像更紧密地对齐( 80 )。

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