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Registration of whole-mount histology and tomography of the prostate using particle filtering

机译:使用粒子滤波记录前列腺的整个组织结构和层析成像

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Registration of histological slices to volumetric imaging of the prostate is an important task that can be used to optimize imaging for cancer detection. Such registration is challenging due to change in volume of the specimen during fixation, and misalignment of the histological slices during preparation and digital scanning. In this work we propose a multiple-slice to volume registration method in which a stack of equispaced, uniaxial but unaligned 2D contours, extracted from digitally scanned whole-mount histological slices, is registered to a 3D surface, extracted from a volumetric image of the prostate. Initially, the stack of unaligned contours is coarsely aligned to the surface as a whole. Then, each contour is finely registered to the surface while being confined to its plane along the sectioning axis. We incorporate the method in a particle filtering framework to compensate for the high dimensionality of the search space and multi-modal nature of the problem. Moreover, such framework allows modeling the uncertainty in the segmentation of the contours and surface, in order to derive optimal registration parameters in a Bayesian approach. The proposed algorithm is demonstrated and evaluated on both synthetic and clinical data. The mean area overlap of the registered gland and the segmented histology was found to be 90.2%, with a mean registration error of 1.8mm between visible landmarks.
机译:组织切片对前列腺体积成像的配准是一项重要任务,可用于优化成像以检测癌症。由于固定过程中样品体积的变化,以及制备和数字扫描过程中组织切片的未对准,因此这种配准具有挑战性。在这项工作中,我们提出了一种多层到体积的配准方法,其中将从数字扫描的整个组织切片中提取的等距,单轴但未对齐的2D轮廓堆栈配准到3D表面,该表面是从三维图像的体积图像中提取的前列腺。最初,未对齐轮廓的堆栈与整个表面大致对齐。然后,将每个轮廓精细地对齐到表面,同时将它们限制在沿剖切轴的平面内。我们将该方法合并到粒子过滤框架中,以补偿搜索空间的高维性和问题的多峰性质。此外,这样的框架允许对轮廓和表面的分割中的不确定性进行建模,以便以贝叶斯方法得出最佳配准参数。所提出的算法在综合和临床数据上均得到了论证和评估。发现登记的腺和分割的组织学的平均面积重叠为90.2%,可见标志之间的平均登记误差为1.8mm。

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