Non-rigid registration between histological and MR images of the prostate: A joint segmentation and registration framework

Yangming Ou, D. Shen, M. Feldman, J. Tomaszeweski, C. Davatzikos
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引用次数: 43

Abstract

This paper presents a 3D non-rigid registration algorithm between histological and MR images of the prostate with cancer. To compensate for the loss of 3D integrity in the histology sectioning process, series of 2D histological slices are first reconstructed into a 3D histological volume. After that, the 3D histology-MRI registration is obtained by maximizing a) landmark similarity and b) cancer region overlap between the two images. The former aims to capture distortions at prostate boundary and internal blob-like structures; and the latter aims to capture distortions specifically at cancer regions. In particular, landmark similarities, the former, is maximized by an annealing process, where correspondences between the automatically-detected boundary and internal landmarks are iteratively established in a fuzzy-to-deterministic fashion. Cancer region overlap, the latter, is maximized in a joint cancer segmentation and registration framework, where the two interleaved problems - segmentation and registration - inform each other in an iterative fashion. Registration accuracy is established by comparing against human-rater-defined landmarks and by comparing with other methods. The ultimate goal of this registration is to warp the histologically-defined cancer ground truth into MRI, for more thoroughly understanding MRI signal characteristics of the prostate cancerous tissue, which will promote the MRI-based prostate cancer diagnosis in the future studies.
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前列腺组织学和MR图像之间的非刚性配准:一个联合分割和配准框架
本文提出了一种前列腺癌组织与磁共振图像的三维非刚性配准算法。为了弥补组织学切片过程中三维完整性的损失,首先将一系列二维组织学切片重建为三维组织学体积。之后,通过最大化a)标记相似性和b)两幅图像之间的癌区重叠,获得三维组织学- mri配准。前者旨在捕捉前列腺边界和内部斑点状结构的扭曲;后者的目标是捕捉癌症区域的扭曲。特别是,标记相似性,前者,通过退火过程最大化,其中自动检测的边界和内部标记之间的对应关系以模糊到确定性的方式迭代建立。癌症区域重叠,后者,在联合癌症分割和配准框架中最大化,其中两个交错的问题-分割和配准-以迭代的方式相互通知。通过与人类定义的地标和其他方法的比较来确定配准精度。这一登记的最终目的是将组织学上确定的癌症基础真相转化为MRI,从而更透彻地了解前列腺癌组织的MRI信号特征,从而促进未来研究中基于MRI的前列腺癌诊断。
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