Koudounas Panteleimon, Koniaris Efthymios, Manolis Ioannis, Asvestas Panteleimon, Kostopoulos Spiros, Cavouras Dionisis, Glotsos Dimitris
Medical Image and Signal Processing Laboratory, Department of Biomedical Engineering, University of West Attica, Athens, Greece.
Department of Pathology, Hippocration General Hospital, Athens, Greece.
Microsc Res Tech. 2022 Aug;85(8):2913-2923. doi: 10.1002/jemt.24141. Epub 2022 May 5.
The purpose of the study is to develop and automate a series of steps for enabling digital 3D tissue volume generation in conventional Brightfield microscopy for histopathology applications. Tissue samples were retrieved from the General Hospital of Athens "Hippocration", Greece. Samples were placed on a microtome that produced consecutive 2 μm sections. Each section was stained using Hematoxylin and Eosin and placed on microscope slides. A histopathologist specified the region of interest (ROI) on each slide. A 2D image was created from each ROI using a LEICA DM2500 microscope with a LEICA DFC 420C camera. Τhe 3D volume was created by stacking consecutive 2D images using a deep learning image interpolation method. The reconstructed 3D tissue volumes were evaluated by an expert histopathologist. Results showed that the 3D volumes might reveal information that is not clearly visible or even undetectable in the conventional 2D Brightfield images. In contrast to other 3D tissue imaging technologies, the proposed method (a) does not depend on the distance of the sample from the objectives producing 3D tissue volumes at any desired magnification, (b) does not require a special instrument, it may be implemented with any conventional Brightfield microscope, and (c) can be used for any given routine application, not only for some specialized clinical studies. The proposed study provides the basis for a feasible, cost-less and time-less upgrade of any standard 2D microscope into a 3D imaging instrument that may enhance the quality of diagnostic assessments in histopathology. HIGHLIGHTS: A method for 3D tissue volume generation. 3D volumes reveal information not clearly visible or even undetectable in 2D images. A method for feasible, cost-less and time-less upgrade of any Brightfield 2D microscope into a 3D imaging instrument.
本研究的目的是开发并自动化一系列步骤,以便在用于组织病理学应用的传统明场显微镜中实现数字3D组织体积生成。组织样本取自希腊雅典“希波克拉底”综合医院。样本被放置在切片机上,切成连续的2μm切片。每片切片用苏木精和伊红染色,然后放在载玻片上。一名组织病理学家在每张载玻片上指定感兴趣区域(ROI)。使用配备LEICA DFC 420C相机的LEICA DM2500显微镜从每个ROI创建二维图像。通过使用深度学习图像插值方法堆叠连续的二维图像来创建三维体积。重建的三维组织体积由专业组织病理学家进行评估。结果表明,三维体积可能揭示在传统二维明场图像中不清晰可见甚至无法检测到的信息。与其他三维组织成像技术相比,所提出的方法(a)不依赖于样本与物镜的距离,能够在任何所需放大倍数下生成三维组织体积;(b)不需要特殊仪器,可通过任何传统明场显微镜实现;(c)可用于任何给定的常规应用,而不仅限于某些专门的临床研究。本研究为将任何标准二维显微镜可行、低成本且省时地升级为三维成像仪器提供了基础,这可能会提高组织病理学诊断评估的质量。要点:一种生成三维组织体积的方法。三维体积揭示了二维图像中不清晰可见甚至无法检测到的信息。一种将任何明场二维显微镜可行、低成本且省时地升级为三维成像仪器的方法。