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计算机辅助激光解剖：一种利用图像分析工具的显微解剖工作流程。

Computer-Aided Laser Dissection: A Microdissection Workflow Leveraging Image Analysis Tools.

作者信息

Hipp Jason D, Johann Donald J, Chen Yun, Madabhushi Anant, Monaco James, Cheng Jerome, Rodriguez-Canales Jaime, Stumpe Martin C, Riedlinger Greg, Rosenberg Avi Z, Hanson Jeffrey C, Kunju Lakshmi P, Emmert-Buck Michael R, Balis Ulysses J, Tangrea Michael A

机构信息

Laboratory of Pathology, National Cancer Institute, Bethesda, MD, USA.

Google Inc., Mountain View, CA, USA.

出版信息

J Pathol Inform. 2018 Dec 11;9:45. doi: 10.4103/jpi.jpi_60_18. eCollection 2018.

DOI:10.4103/jpi.jpi_60_18
PMID:30622835
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6298131/
Abstract

INTRODUCTION

The development and application of new molecular diagnostic assays based on next-generation sequencing and proteomics require improved methodologies for procurement of target cells from histological sections. Laser microdissection can successfully isolate distinct cells from tissue specimens based on visual selection for many research and clinical applications. However, this can be a daunting task when a large number of cells are required for molecular analysis or when a sizeable number of specimens need to be evaluated.

MATERIALS AND METHODS

To improve the efficiency of the cellular identification process, we describe a microdissection workflow that leverages recently developed and open source image analysis algorithms referred to as computer-aided laser dissection (CALD). CALD permits a computer algorithm to identify the cells of interest and drive the dissection process.

RESULTS

We describe several "use cases" that demonstrate the integration of image analytic tools probabilistic pairwise Markov model, ImageJ, spatially invariant vector quantization (SIVQ), and eSeg onto the ThermoFisher Scientific ArcturusXT and Leica LMD7000 microdissection platforms.

CONCLUSIONS

The CALD methodology demonstrates the integration of image analysis tools with the microdissection workflow and shows the potential impact to clinical and life science applications.

摘要

引言

基于新一代测序和蛋白质组学的新型分子诊断检测方法的开发与应用，需要改进从组织切片中获取靶细胞的方法。激光显微切割能够基于视觉选择，成功地从组织标本中分离出不同的细胞，适用于许多研究和临床应用。然而，当分子分析需要大量细胞或需要评估相当数量的标本时，这可能是一项艰巨的任务。

材料与方法

为提高细胞识别过程的效率，我们描述了一种显微切割工作流程，该流程利用了最近开发的开源图像分析算法，即计算机辅助激光切割（CALD）。CALD允许计算机算法识别感兴趣的细胞并驱动切割过程。

结果

我们描述了几个“用例”，展示了图像分析工具概率成对马尔可夫模型、ImageJ、空间不变矢量量化（SIVQ）和eSeg在赛默飞世尔科技的ArcturusXT和徕卡LMD7000显微切割平台上的整合。

结论

CALD方法展示了图像分析工具与显微切割工作流程的整合，并显示了其对临床和生命科学应用的潜在影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e45/6298131/6c814f47a8ae/JPI-9-45-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e45/6298131/19682d9c88dc/JPI-9-45-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e45/6298131/9e26bcad5039/JPI-9-45-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e45/6298131/47f09eb4209e/JPI-9-45-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e45/6298131/dbf3552021f9/JPI-9-45-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e45/6298131/0c119af412b5/JPI-9-45-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e45/6298131/6c814f47a8ae/JPI-9-45-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e45/6298131/19682d9c88dc/JPI-9-45-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e45/6298131/9e26bcad5039/JPI-9-45-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e45/6298131/47f09eb4209e/JPI-9-45-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e45/6298131/dbf3552021f9/JPI-9-45-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e45/6298131/0c119af412b5/JPI-9-45-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e45/6298131/6c814f47a8ae/JPI-9-45-g006.jpg

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