Digital Pathology & Imaging Analysis

Digital Pathology & Imaging Analysis

Digital pathology is a general term that refers to the combination of digital workflow and imaging solutions. Digital images can maintain long-term stability, and can be quickly transmitted through computer networks, which are widely used in pathology research, pathology teaching, tissue morphology analysis, fluorescence analysis, immunohistochemical mathematical imaging, etc.

Digital pathology.Figure 1. Digital pathology.

Digital Pathology & Imaging Analysis

Digital pathology (DP) is an image-based platform, which involves image management, analysis and interpretation of digital information reflected on digital slides. The whole digital pathology system consists of four key parts: capturing digital slice information (images) with digital scanner, image storage and archiving, editing and other operations on the captured images, reading and sharing images with other researchers or institutions. For digital pathology and imaging analysis projects, CD BioSciences can provide the services including embedding, slicing and staining the fixed tissues received from customers, scanning and analyzing the microscope slides, or processing and analyzing the image data provided by USB or cloud.

Our Services

Our Advantages

  • Easy to save and manage
  • Easy to browse and transfer
  • Faster and more efficient workflow
  • Reasonable price and short turnaround time

CD BioSciences has professional knowledge in pathology. We can provide you with a full set of digital pathology services, including tissue embedding, tissue section, tissue staining, slide scanning, etc., which enables us to quantitatively analyze the image data obtained from tissue sections to evaluate tumor area, tissue biomarker co localization, etc. If you have any needs, please feel free to contact us. We will determine the best digital pathology scheme according to specific research problems.

  1. Barisoni L, Lafata K J, Hewitt S M, et al. Digital pathology and computational image analysis in nephropathology[J]. Nature Reviews Nephrology, 2020, 16(11): 669-685.
  2. Madabhushi A, Lee G. Image analysis and machine learning in digital pathology: Challenges and opportunities[J]. Medical image analysis, 2016, 33: 170-175.

*If your organization requires the signing of a confidentiality agreement, please contact us by email.

Please note: Our services can only be used for research purposes. Do not use in diagnostic or therapeutic procedures!

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