Fluorescence in situ hybridization (FISH) is a technique to visualize specific DNA/RNA sequences within the cell nuclei and provide the presence, location and structural integrity of genes on chromosomes. A confocal Whole Slide Imaging (WSI) scanner technology has superior depth resolution compared to wide-field fluorescence imaging. Confocal WSI has the ability to perform serial optical sections with specimen imaging, which is critical for 3D tissue reconstruction for volumetric spatial analysis. The standard clinical manual scoring for FISH is labor-intensive, time-consuming and subjective. Application of multi-gene FISH analysis alongside 3D imaging, significantly increase the level of complexity required for an accurate 3D analysis. Therefore, the purpose of this study is to establish automated 3D FISH scoring for z-stack images from confocal WSI scanner. The algorithm and the application we developed, SHIMARIS PAFQ, successfully employs 3D calculations for clear individual cell nuclei segmentation, gene signals detection and distribution of break-apart probes signal patterns, including standard break-apart, and variant patterns due to truncation, and deletion, etc. The analysis was accurate and precise when compared with ground truth clinical manual counting and scoring reported in ten lymphoma and solid tumors cases. The algorithm and the application we developed, SHIMARIS PAFQ, is objective and more efficient than the conventional procedure. It enables the automated counting of more nuclei, precisely detecting additional abnormal signal variations in nuclei patterns and analyzes gigabyte multi-layer stacking imaging data of tissue samples from patients. Currently, we are developing a deep learning algorithm for automated tumor area detection to be integrated with SHIMARIS PAFQ.

中文翻译：

---

使用共焦全玻片成像扫描仪对荧光原位杂交 (FISH) 进行自动 3D 评分

荧光原位杂交 (FISH) 是一种可视化细胞核内特定 DNA/RNA 序列并提供染色体上基因的存在、位置和结构完整性的技术。与宽视场荧光成像相比，共焦全玻片成像 (WSI) 扫描仪技术具有出色的深度分辨率。共聚焦 WSI 能够通过样本成像执行串行光学切片，这对于体积空间分析的 3D 组织重建至关重要。FISH 的标准临床手册评分是劳动密集型、耗时且主观的。多基因 FISH 分析与 3D 成像的应用显着提高了准确 3D 分析所需的复杂性。所以，本研究的目的是为来自共聚焦 WSI 扫描仪的 z-stack 图像建立自动 3D FISH 评分。我们开发的算法和应用程序 SHIMARIS PAFQ 成功地使用 3D 计算来实现清晰的单个细胞核分割、基因信号检测和分离探针信号模式的分布，包括标准分离以及由于截断和缺失引起的变异模式等。与在 10 例淋巴瘤和实体瘤病例中报告的基本事实临床手动计数和评分相比，该分析准确无误。我们开发的算法和应用程序 SHIMARIS PAFQ 比传统程序更客观、更有效。它可以自动计数更多的细胞核，精确检测细胞核模式中的额外异常信号变化，并分析患者组织样本的千兆字节多层堆叠成像数据。目前，我们正在开发一种用于自动肿瘤区域检测的深度学习算法，以与 SHIMARIS PAFQ 集成。