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Spatially Consistent Multi-Scale Anatomical Landmark Detection in Incomplete 3D-CT Data

机译:不完整的3D-CT数据中空间一致的多尺度解剖地标检测

摘要

A method and system for automated spatially-consistent multi-scale detection of anatomical landmarks in medical images is disclosed. A discrete scale-space representation of a medical image of a patient is generated. A plurality of anatomical landmarks are detected at a coarsest scale-level of the discrete scale-space representation of the medical image using a respective trained search model trained at the coarsest scale-level for each of the plurality of anatomical landmarks. Spatial coherence of the detected anatomical landmarks is enforced by fitting a learned robust shape model of the plurality of anatomical landmarks to the detected anatomical landmarks at the coarsest scale-level to robustly determine a set of the anatomical landmarks within a field-of-view of the medical image. The detected landmark location for each of the landmarks in the set of anatomical landmarks is refined at each remaining scale-level of the discrete scale-space representation of the medical image using, for each landmark, a respective trained search model trained at each remaining scale-level and constrained based on the predicted landmark location at a previous scale-level.
机译:公开了一种用于医学图像中的解剖标志的空间上一致的多尺度自动检测的方法和系统。生成患者医学图像的离散比例空间表示。使用针对多个解剖界标中的每一个在最粗尺度级训练的相应训练的搜索模型,以医学图像的离散尺度空间表示的最粗糙尺度级来检测多个解剖界标。通过将学习到的多个解剖学界标的鲁棒形状模型拟合到最粗尺度级别的检测到的解剖学界标,以在一个视野的范围内稳健地确定一组解剖学界标,可以增强检测到的解剖学界标的空间连贯性。医学图像。在医学图像的离散比例空间表示的每个剩余比例级别上,针对每个地标,使用在每个剩余比例下训练的训练有素的搜索模型,在每个剩余比例级别上细化一组解剖学地标中每个地标的检测到的地标位置级别,并基于先前级别的预测地标位置进行约束。

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