Key Features You Should Look For In Pathology Image Analysis Software

22 Dec, 2021 | Blog posts, Prisma

Advanced pathology image analysis software solutions transform biomedical research practice. To provide you with a brief overview of the bioimage analysis tools in the field of digital pathology, we have reviewed the most popular types of software solutions currently available. Uncover the numerous benefits of integrating these tools into your laboratory workflow.

Understanding current trends in the pathology image analysis software industry

The rise of chronic diseases worldwide and the ever-increasing governmental emphasis on research activities for new drug discovery and disease treatment drive business growth in the digital pathology market. This sector is booming at present, with an expected market growth of 7.5% by 2030.

Pathology image analysis software market
Pathology image analysis software market.

Based on their area of use, pathology software products can be grouped into several major segments: 

  • Lab information management software (LIMS),
  • Laboratory information systems (LIS),
  • Pathology training software,
  • Whole Slide Imaging (WSI) software
  • Pathology image analysis software

The image analysis software segment currently dominates the digital pathology market. The growth in this sector is in line with the increasing demand for advanced integrated solutions and the raising awareness of pathologists on the possibilities of healthcare IT. Expansion possibilities are primarily seen in innovative business models centered around the use of AI technology in bioimage analysis. Let us have a look at how these state-of-the-art solutions will help you arrive at highly precise and reproducible results faster and easier than ever.

Exploring the benefits of pathology image analysis software

Whole slide image analysis has become an integral part of modern life science research. Current digital pathology practice involves the scanning and processing of large and complex WSI datasets. Advanced image analysis solutions allow pathologists to quickly and efficiently analyze digital whole slide images while generating reliable quantitative results.

Enhancing pathology image analysis outcomes with the help of AI

State-of-the-art bioimage analysis solutions apply advanced artificial intelligence methods to significantly accelerate research processes and reduce resources spent. Deep learning bioimage analysis solutions are designed to analyze image data, recognize patterns and generate predictions autonomously based on a variety of input images.

AI-backed applications mimic the image analysis workflow pathologists perform. Object and pattern recognition relies on the consensus among experts (Ground Truth) who have drawn annotations on the input images. This helps standardize the analytical workflow and rule out intra- and interobserver variability. This form of automated analysis is faster and more reliable than manual assessment, meaning that the results generated are also easily reproducible. If you are curious to learn how to improve your research design and analysis workflows for reproducible results check out our article on this topic.

Why use AI-powered software for analyzing bioimage data?

  • Increase productivity,
  • Gather reproducible results,
  • Arrive at results faster,
  • Standardize workflows,
  • Save resources,
  • Collaborate remotely.

Choosing the right pathology image analysis tool

Artificial intelligence capabilities in pathology image analysis can be put into a hierarchical order depending on task complexity. Basic cell and tissue structure quantification tasks normally lie at the bottom of this hierarchy. The automated identification of tissue structures and the spatial relationships between them are viewed as more complex functionalities (Tosun et al. 2020).

Pathology Image Analysis Software Capabilities
Hierarchy of pathology image analysis software capabilities based on Tosun et al. (2020)

We have reviewed the features that modern bioimage analysis solutions support to help pick the right tools for your research project.  

Computational quantification of tissue structures

AI-driven bioimage analysis software allows you to detect and count different morphological objects within digital slides. These can be, for example, particular atypical features of the tumor microenvironment that are viewed as hallmarks of cancer, including tumor cells, stroma cells, and lymphocytes. Besides object counting, automated image analysis will help you quantify pathological processes within tissue samples. Thus, you can count the number of mitoses visible on your pathology images.

Automated cell type recognition and counting within tissue samples can help you make fast assessments about the effectiveness of therapies and cancer-suppressing agents. Apart from feature counting, AI-powered tools allow you to make objective quantitative measurements on several parameters in order to assess the nature of cell morphology.

Such parameters usually depend on your research design but often include the size and roundness of cells and cell nuclei.  

pathology image analysis software
Mouse prostate nuclei (marked blue) in the IKOSA image viewer.

In the field of histopathology, the automated quantification of biomarkers plays a central role. Popular staining techniques used in the field of IHC imaging include simultaneous multimarker staining and virtual multi-staining by computational methods (Koezel et al. 2019).

In IHC studies, AI-driven software is used in order to identify specific biomarkers located by means of fluorescent staining. IHC image analysis software is capable of quantifying signals from dyes such as hematoxylin and eosin (H&E), p16 (a biomarker for neoplasia), or KI-67 (a biomarker for cancer), a task which is a Herculean task for the bare human eye.

Automation of complex detection, segmentation, and classification tasks

State-of-the-art pathology image analysis solutions automate complex analytical tasks like bioimage detection, segmentation, and classification.

Did you know?

Segmentation is a prerequisite to performing more complex spatial modeling.

Pathology image segmentation is a computer vision technique for WSI analysis that allows pathologists to identify and locate tissue components like cells, cell nuclei, glands, or entire tumor regions. Automated WSI segmentation enables researchers to locate cancer-related processes such as inflammation, fibrosis, and mitosis (Wang et al. 2019a). Cell nuclei belong to the most often segmented morphological features in pathology literature. Have a look at our special article on nuclei segmentation to see how this works.

The segmentation of WSI slides with specialized pathology image analysis software requires completing several preprocessing and processing steps. These include image normalization, color augmentation, model selection, and model construction. Properly conducting each of these steps results in improved segmentation performance (Wang et al. 2019a).

pathology image analysis software
Cell nuclei instance segmentation output in IKOSA.

By means of classification, you can also group morphological objects from the same type by different labels. Thus, you can assign individual cell nuclei in histology images to categories such as “epithelial,” “inflammatory” or “fibroblast” nuclei (Madabhushi & Lee 2016).

Did you know?

Automated classification models assist in cancer grading.

The automated classification of tissue structures can help assign WSI images and Region of Interests (ROIs) within them to categories such as “benign,” “atypical,” “high-risk,” “low-risk”, “malignant,” “ADH,” “invasive carcinoma,” depending on the feature pattern detected (Tosun et al. 2020).

Advanced Region-of-Interest (ROI) features

Using the Region-of-Interest (ROI) feature provided in current pathology image analysis applications, you can define specific areas within your slides you want your analysis to focus on.

Did you know?

ROIs are the main diagnostic areas within the pathology images.

Sometimes it makes sense to concentrate only on particular parts of the WSI slides while performing segmentation tasks. These are usually the main malignant or the most diagnostically impactful areas. In some cases, it is reasonable to have a closer look at ambiguous or borderline areas within the images (Wang et al. 2019; Tosun et al. 2020). In such cases, the ROI-feature provided in your analysis tool can help you. 

The versatile ROI-features in IKOSA allows you to focus your analysis on particular sections within pathology images
The versatile ROI-features in IKOSA allows you to focus your analysis on particular sections within pathology images.

Current bioimage analysis software applications support features like ROI segmentation and generating quantitative data about tissue structures within the ROIs. If you are curious to learn how the ROI-feature exactly works, have a look at our article on the topic.

State-of-the-art spatial analysis options

Digital pathology image analysis software allows you to gather high-content data about the spatial organization of tissue samples, which is otherwise very hard to acquire with traditional manual assessment methods. Adding a spatial context to your data can help you identify the distribution of particular cell populations (e.g., T-cells) as well as various inter- and intracellular signaling pathways.

pathology image analysis software
Colocalization of RFP-RPGRORF15 and YFP-RPGRIP1α1 in COS7 cells. Source: Wikimedia Commons

Using AI-driven spatial analysis, you can obtain information about the distribution of cell nuclei in relation to stroma and lumen. By this means, you can derive reliable measurements on spatial metrics like density, coexpression (phenotyping), and colocalization (proximity) (Baxi et al. 2022).

Did you know?

The spatial arrangement of nuclei or glands in tissue images can help you identify cancer grades.

Spatial modeling enables you to locate tumor-infiltrating lymphocytes by identifying intra-tumor lymphocytes (ITLs), adjacent-tumor lymphocytes (ATLs), and distal-tumor lymphocytes (DTL) (Yuan 2015).

Learn how AI-powered image analysis software will help you derive spatial data from your tissue samples in our article on the topic of spatial biology

Open-source pathology image analysis solutions for starters

Over recent years, a number of open-source software tools that allow researchers to automate digital slide analysis have emerged. Most of these applications are desktop tools available for download that can be extended by installing additional plugins depending on the user’s needs. Several of these open-source solutions deserve to be mentioned:

QuPath is the most commonly cited open-source image analysis tool in pathology literature. It was originally developed by the Centre for Cancer Research at the Queen’s University Belfast as a quantitative pathology and bioimage analysis system. QuPath provides capabilities like WSI viewing, cell counting, classification and segmentation of objects and pixels, automated tumor and biomarker detection, estimation of stain intensity, batch processing, and data export. It also offers the possibility to exchange data with other analytical applications like ImageJ and MATLAB. Among the obvious benefits of these open-source image analysis tools are their accessibility, affordability, and constant development (Humphries et al. 2021).

Most open-source pathology image analysis solutions offer advanced scripting features allowing users to develop and share their automated image analysis models. A highly engaged user community usually actively participates in the software development process.

Yet, some of these open-source applications come with a few limitations in their functionalities like little image modification options, the lack of support for annotations and multimodality images, and missing compatibility with other slide scanning devices.

Open-source applications for whole-slide image analysis are primarily intended to let users actively participate in the software development process by drafting their own plugins, scripts, and workflows. Yet, this requires advanced coding skills and the dedication of additional IT resources. This means that if you are intending to use an open-source image analysis solution in your lab, you should be prepared for a steep learning curve and acquiring some new technical skills.

Benefits of commercial pathology image analysis solutions to consider

Using commercial pathology image analysis software brings additional benefits. Commercial cell image analysis products existing on the market support a broader range of imaging modalities. These are generally more user-friendly as they offer a range of practice-proven image analysis modules developed through the joint effort of computer vision- and pathology experts.

Investing in commercial bioimage analysis software is an important decision. However, you need to consider first what features and analytical packages are needed in your lab. Here are a few reasons why you should opt for a commercial pathology image analysis solution.

Why invest in commercial bioimage analysis software?

  • User-friendliness and flexibility,
  • Constant development in dialogue with experts,
  • Integration with slide scanning systems
  • Learning materials and tutorials,
  • User- and technical support.

Integrated pathology image analysis solutions support broad-ranging functionalities such as image data viewing, augmentation, annotation, management, and sharing, which results in a faster discovery process. The IKOSA Platform is a holistic microscopy image analysis solution spanning AI-powered analysis and algorithm training features, a collection of ready-made pathology image analysis applications combined with smart bioimage data management and user collaboration.

pathology image analysis software
Creating your own pathology image analysis app is an easy task with IKOSA AI.

IKOSA AI is a unique software product allowing researchers to develop their own unique AI applications for the automated analysis of pathology slides. All this happens on a user-friendly graphic interface and without having to write any script or code. If you are eager to learn how to create a specialized image analysis application tailored to the needs of your research project, have a look at our collection of tutorials on IKOSA AI.

Get the most out of pathology image analysis

Keep in mind that in order to excel in pathology research, you need a unique software solution that is tailored to your needs. The IKOSA Platform provides the versatility required to answer even the most complex of research questions. Don’t wait to give IKOSA a try!    

Written by Fanny Dobrenova and Elisa Opriessnig


Baxi, V., Edwards, R., Montalto, M., & Saha, S. (2022). Digital pathology and artificial intelligence in translational medicine and clinical practice. Modern Pathology, 35(1), 23-32. 

Humphries, M. P., Maxwell, P., & Salto-Tellez, M. (2021). QuPath: The global impact of an open source digital pathology system. Computational and Structural Biotechnology Journal, 19, 852-859. 

Koelzer, V. H., Sirinukunwattana, K., Rittscher, J., & Mertz, K. D. (2019). Precision immunoprofiling by image analysis and artificial intelligence. Virchows Archiv, 474, 511-522.  

Madabhushi, A., & Lee, G. (2016). Image analysis and machine learning in digital pathology: Challenges and opportunities. Medical image analysis, 33, 170-175. 

Tosun, A. B., Pullara, F., Becich, M. J., Taylor, D., Fine, J. L., & Chennubhotla, S. C. (2020). Explainable AI (xAI) for anatomic pathology. Advances in Anatomic Pathology, 27(4), 241-250.   

Wang, Shidan, et al. “Pathology image analysis using segmentation deep learning algorithms.” The American journal of pathology 189.9 (2019a): 1686-1698.  

Wang, S., Wang, T., Yang, L., Yang, D. M., Fujimoto, J., Yi, F., … & Xiao, G. (2019b). ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network. EBioMedicine, 50, 103-110.

Yuan, Y. (2015). Modelling the spatial heterogeneity and molecular correlates of lymphocytic infiltration in triple-negative breast cancer. Journal of The Royal Society Interface, 12(103), 20141153. 

To see IKOSA in full action launch your trial subscription


Join our newsletter