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Image analysis applications


Charles Caldwell, Ph.D.
Roberto Gianani, M.D.

Last author update: 2 December 2020
Last staff update: 16 August 2023

Copyright: 2020-2024, PathologyOutlines.com, Inc.

PubMed Search: Image analysis applications[TIAB]

Conflict of interest statment: All authors are employed at Flagship Biosciences, an image analysis company. Dr. Gianani is the Medical Director and owns stock options, Dr. Caldwell is the Science Supervisor, Dr. Adisetiyo is a Technical Writer and Dr. Luengo is the Director of Image Analysis.

Charles Caldwell, Ph.D.
Roberto Gianani, M.D.
Page views in 2024 to date: 39
Table of Contents
Definition / general | Essential features | Terminology | Basics | Assisting the pathologist | Pathologist impossible endpoints | Applications in pathology | Diagrams / tables | Videos | Board review style question #1 | Board review style answer #1 | Board review style question #2 | Board review style answer #2
Cite this page: Caldwell C, Adisetiyo V, Luengo C, Gianani R. Image analysis applications. PathologyOutlines.com website. https://www.pathologyoutlines.com/topic/informaticsimganalysisapp.html. Accessed May 13th, 2024.
Definition / general
  • Computerized analysis of digital images is rapidly becoming an important adjuvant tool for the practicing pathologist in the context of diagnosis or clinical trial work (Arch Pathol Lab Med 2019;143:222)
  • Computers can assist the pathologist by providing objective, precise and reproducible measurements that generate difficult or pathologist impossible cellular endpoints, while the pathologist provides the clinical oversight (J Pathol Clin Res 2019;5:81)
Essential features
  • There are 2 styles of image analysis within pathology: pixel based image analysis (provides simples endpoints) and cell based image analysis (provides simple and complex endpoints) (Nat Methods 2017;14:849)
  • Digital methods can assist the pathologist by providing objective, precise and repeatable measurements, generating multiple digital scores and paradigms simultaneously and measuring cellular details that are difficult or impossible for the pathologist to generate (J Pathol Clin Res 2019;5:81)
  • Complex analytical workflows can be validated via pathologist review if the pathologist can distinguish the end results
    • For pathologist impossible endpoints, focused studies which demonstrate the robustness of the analysis must be performed and documented to ensure quality data (Nat Methods 2017;14:849)
  • Image analysis has been successfully applied in the areas of immune oncology, muscular dystrophy and liver disease (J Pathol Clin Res 2019;5:81, Arch Pathol Lab Med 2019;143:197)
Terminology
  • Computational pathology
  • Digital pathology
  • Pixel based image analysis
  • Cell based image analysis
  • Pathologist impossible endpoints
Basics
  • There are 2 styles of image analysis within pathology: pixel based image analysis and cell based image analysis (Nat Methods 2017;14:849)
    • Pixel based image analysis:
      • Each pixel examined by itself or with its neighbors
      • Leads to simple and robust algorithms
      • Provides simple endpoints
      • Example: collagen stained for Masson trichrome
        • Finds pixels that represents tissues
        • Finds pixels within tissue with staining
        • Percentage of stained area
    • Cell based image analysis:
      • Cells are identified in the image
      • Each cell is represented by its coordinates and properties:
        • Morphology (size and shape) properties
          • Nucleus area
          • Cell area
          • Nucleus elongation
          • Cell roundness
        • Staining properties
          • E.g. Strength of Ki67 staining in the nucleus
          • E.g. Strength of PDL1 staining in the membrane
          • E.g. Total amount of PDL1 staining in the cytoplasm
          • Localization of staining within the cytoplasm
        • Structural properties
          • Locating cells in the tumor region
          • Size of tumor region cell is in
          • Locating cells in an epithelium layer
          • Local density of cells
      • Provides simple (e.g. cell counts) and complex (e.g. identify different cell types) endpoints
Assisting the pathologist
  • Computers can optimally perform repetitive and tedious tasks (e.g. cell counting) but are suboptimal at higher level functions (e.g. reasoning, understanding, clinical context)
  • Humans outperform computers in higher level functions but struggle with objective quantification
  • Computers can assist the pathologist by providing objective, precise and repeatable measurements, for example (J Pathol Clin Res 2019;5:81):
    • Estimating the percentage of positive cells across a tissue leads to large variation between pathologists and for the same pathologist on different days; the computer will produce the same count every time
    • Intra and interpathologist discrepancies increase as the complexity of the scoring criteria increases (e.g. H scoring in only the tumor compartment or in specific cellular subtypes)
  • Digital methods can generate multiple digital scores and paradigms simultaneously, allowing for review of multiple assay parameters, which a pathologist would have to continuously rescore to create
  • Complex analytical workflows (e.g. machine learning) can be validated via pathologist review if the pathologist can distinguish the end results (e.g. identifying tumor versus stromal tissue compartments)
Pathologist impossible endpoints
  • Computers can measure cellular details that are difficult or impossible for the pathologist to generate (Nat Methods 2017;14:849)
    • Examples of difficult measures:
      • Cellular density in an area
      • Quantification of biomarker heterogeneity
      • Quantification of staining in and counting of distinguishable cellular subtypes (e.g. lymphocytes)
    • Examples of pathologist impossible measures:
      • Spatial distances between cells (e.g. nearest PD-1 positive cell to nearest PDL1 positive cell)
      • Quantification of fluorescent signal
      • Identification of nearly indistinguishable cellular subtypes (e.g. distinguish tumor and lymphocyte cells from macrophages)
      • Statistical analysis of staining patterns (e.g. staining completeness, staining skewness)
  • For pathologist impossible endpoints, focused studies that demonstrate the robustness of analysis are required to ensure data validity since it is challenging for a pathologist to review and accept the data
Applications in pathology
  • Pathologists are often required to provide accurate and precise quantitative information in the context of diagnosis or clinical trial work wherein the scoring schemes are increasing in complexity (JAMA Oncol 2017;3:1051)
  • Immune oncology is a field that has benefited from the application of image analysis
    • Characterization of the immune landscape and checkpoint inhibitor statis is an essential component to plan novel immunotherapy which computational pathology has been able to assist with (JAMA Oncol 2017;3:1051)
  • Digital pathology has recently been applied to multiple disease types including muscular dystrophy and liver disease (Arch Pathol Lab Med 2019;143:197)
Diagrams / tables

Contributed by Charles Caldwell, Ph.D., Vitria Adisetiyo, Ph.D., Cris Luengo, Ph.D., Roberto Gianani, M.D.
Image Analysis of PD-L1 in NSCLC

Image analysis of PDL1 in NSCLC

Image Analysis Pathology Endpoint Measures

Image analysis pathology endpoint measures

Videos

Example of image analysis workflow: evaluation of PDL1 IHC staining in non small cell lung cancer

Board review style question #1
    How can image analysis serve as a pathologist’s tool?

  1. Generate pathology endpoints that do not require clinical oversight
  2. Make clinical diagnosis decisions
  3. Provide objective and reproducible measurements and generate difficult and pathologist impossible cellular endpoints
  4. Provide reasoning, understanding and clinical context
Board review style answer #1
C. Provide objective and reproducible measurements and generate difficult and pathologist impossible cellular endpoints

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Reference: Image analysis applications
Board review style question #2
    What is the main difference between pixel based and cell based image analysis?

  1. Cell based image analysis provides simple endpoints while pixel based image analysis provides complex endpoints
  2. In pixel based image analysis, each pixel is examined by itself or with its neighbors whereas in cell based image analysis, cells are represented by their coordinates and properties
  3. Only cell based image analysis is affected by the image quality
  4. Pixel based image analysis uses pixels to identify cells in an image
Board review style answer #2
B. In pixel based image analysis, each pixel is examined by itself or with its neighbors whereas in cell based image analysis, cells are represented by their coordinates and properties

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Reference: Image analysis applications
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