用机器学习初始化，精准配准组织切片与微CT图像。
2D-3D Deformable Image Registration of Histology Slide and Micro-CT with ML-based Initialization
- 先用机器学习生成初始配准，再优化形变
- 在扁桃体和肿瘤组织上实现更优配准效果
- 适合病理图像融合研究者参考
近年来，组织切片与微计算机断层扫描（μCT）的配准技术推动了基于μCT的虚拟组织学发展。然而，软组织CT图像质量低，且制片过程中的形变使切片与μCT结构难以对应，仍具挑战性。本文提出一种新型2D-3D多模态可变形图像配准方法：首先采用机器学习进行初始化，再通过解析的离面形变精修完成配准。在扁桃体和肿瘤组织数据集上评估，涵盖相位衬度与传统吸收模态μCT。与基于强度和关键点的方法相比，本方法在视觉及标记点评价中均表现更优。
原文摘要 · Abstract (English)
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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