基于机器学习初始化的组织学切片与显微CT的2D-3D可变形图像配准
2D-3D Deformable Image Registration of Histology Slide and Micro-CT with ML-based Initialization
AI总结：
本文针对组织学切片与μCT配准中软组织变形和图像质量低的问题，提出一种结合机器学习初始化、2D-3D多模态可变形配准和解析面外变形细化的新方法，并在扁桃体和肿瘤组织的两种μCT模态上验证了其优于现有方法。
摘要：
组织学与显微计算机断层扫描（μCT）配准的最新进展拓宽了病理应用的视野，例如基于μCT的虚拟组织学。由于软组织CT图像质量较低，该课题仍然具有挑战性。此外，软组织样本在组织学切片制备过程中通常会发生变形，使得组织学切片与μCT之间的结构对应变得困难。在这项工作中，我们提出了一种新颖的2D-3D多模态可变形图像配准方法。该方法首先使用基于机器学习（ML）的初始化，然后进行配准。配准最终通过解析的面外变形细化完成。该方法在从扁桃体和肿瘤组织获取的数据集上进行了评估。研究了相位衬度和常规吸收两种模态的μCT。将所提出方法的配准结果与基于强度和基于关键点的方法的结果进行了比较。比较采用视觉评估和基于基准点的评估两种方式进行。所提出的方法相较于另外两种方法表现出更优越的性能。
英文摘要：
Recent developments in the registration of histology and micro-computed tomography (μCT) have broadened the perspective of pathological applications such as virtual histology based on μCT. This topic remains challenging because of the low image quality of soft tissue CT. Additionally, soft tissue samples usually deform during the histology slide preparation, making it difficult to correlate the structures between histology slide and μCT. In this work, we propose a novel 2D-3D multi-modal deformable image registration method. The method uses a machine learning (ML) based initialization followed by the registration. The registration is finalized by an analytical out-of-plane deformation refinement. The method is evaluated on datasets acquired from tonsil and tumor tissues. μCTs of both phase-contrast and conventional absorption modalities are investigated. The registration results from the proposed method are compared with those from intensity- and keypoint-based methods. The comparison is conducted using both visual and fiducial-based evaluations. The proposed method demonstrates superior performance compared to the other two methods.
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