Nolte P, Groger C J, Frey C, Richter H, Will F, Bauerle T, Schilling A F, Alves F, Rußmann C, Dullin C
IEEE Trans Biomed Eng. 2025 Jun;72(6):1931-1940. doi: 10.1109/TBME.2025.3528739.
Histological analysis of hard tissue specimens is widely used in clinical practice and preclinical research, but it remains a labor-intensive and destructive process. In particular, resin-embedded tissues present challenges due to the inability to target regions of interest (ROI), as internal structures are not visible externally. This work proposes a guided sectioning workflow that enables precise targeting of concealed ROIs using a multimodal approach.
By combining microCT imaging with an automated cutting system, and laser microtomy, precise targeted sectioning was achieved. MicroCT imaging enables visualization of internal structures, guiding the automated cutting system for precise sectioning. Laser microtomy then allows thin tissue sections to be prepared while preserving diagnostic features.
Comparing the automated workflow to the conventional cutting-grinding technique showed that the new method improved accuracy by a factor of 7 and reduced material loss by half and processing time by 75%. Validation was performed by comparing the histological sections with in silico target planes generated from the microCT scans, showing precise alignment between the targeted regions and the prepared sections.
We demonstrate that the proposed approach significantly reduces tissue loss and offers a more efficient workflow compared to traditional methods. Additionally, microCT-based targeting enables accurate correlation between histological findings and 3D pathological structures.
The automated guided sectioning workflow provides valuable insights into tissue pathology, enhancing clinical diagnostics and preclinical research. It also facilitates the generation of multimodal datasets, which can be used in future machine learning applications.
硬组织标本的组织学分析在临床实践和临床前研究中被广泛应用，但它仍然是一个劳动密集型且具有破坏性的过程。特别是，树脂包埋组织由于无法定位感兴趣区域（ROI）而带来挑战，因为内部结构从外部不可见。这项工作提出了一种引导切片工作流程，该流程使用多模态方法能够精确靶向隐藏的ROI。
通过将显微CT成像与自动切割系统以及激光显微切割相结合，实现了精确的靶向切片。显微CT成像能够可视化内部结构，引导自动切割系统进行精确切片。然后激光显微切割允许在保留诊断特征的同时制备薄组织切片。
将自动工作流程与传统切割研磨技术进行比较表明，新方法的准确性提高了7倍，材料损失减少了一半，处理时间减少了75%。通过将组织学切片与从显微CT扫描生成的虚拟目标平面进行比较来进行验证，结果显示靶向区域与制备的切片之间精确对齐。
我们证明，与传统方法相比，所提出的方法显著减少了组织损失，并提供了更高效的工作流程。此外，基于显微CT的靶向能够使组织学发现与三维病理结构之间实现准确关联。
自动引导切片工作流程为组织病理学提供了有价值的见解，增强了临床诊断和临床前研究。它还促进了多模态数据集的生成，可用于未来的机器学习应用。
