Computer-aided laser dissection: A microdissection workflow leveraging image analysis tools

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

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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.

Original languageEnglish (US)
Article number45
JournalJournal of Pathology Informatics
Issue number1
StatePublished - Jan 1 2018


  • Image analysis
  • Microdissection
  • Probabilistic pairwise Markov model
  • SIVQ

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Health Informatics
  • Computer Science Applications


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