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What is Digital and Computational Pathology?
The field of pathology is evolving rapidly. Increasingly, pathologists rely not only on microscopes and stained slides but also on digital imaging, data integration, and algorithmic analysis. In modern labs, digital pathology and computational pathology are no longer buzzwords—they’re central to understanding, diagnosing, and researching disease. In this article, we explore the digital pathology definition, examine the workflows of digital and computational pathology, and explain how digital pathology systems are reshaping clinical practice and biomedical research.
Defining Digital Pathology: From Glass to Pixels
At its core, digital pathology refers to the process of converting conventional glass slides containing tissue sections into digital pathology slides—high-resolution images that can be viewed, stored, shared, and analyzed on a computer rather than via a microscope. When gathering an understanding of what digital pathology is, this conversion from analog to digital is the foundation.
Through scanning the entire tissue section (often referred to as a “whole slide” scan), the resulting digital file preserves cellular architecture, staining gradients, and tissue morphology, making it possible to examine the specimen virtually. These digital slides become a new format that pathologists, researchers, and computational tools can work on, whether for diagnosis, education, or research.
But digital pathology goes beyond simply digitizing slides. It encompasses the digital pathology workflow, which is the sequence of operations from slide preparation and scanning to data storage, image management, and image analysis. Robust digital pathology systems integrate all these steps—linking slide scanners, image servers, annotation tools, and advanced analytics into a seamless pipeline.
Introducing Computational Pathology
If digital pathology is about creating and managing digital slides and images, computational pathology (often referred to as digital computational pathology) is about extracting deeper insights from those images using algorithms, analytics, and machine learning. Computational pathology builds on digital pathology by applying quantitative methods to tissue images—measuring, classifying, predicting, and modeling disease attributes.
In a digital computational pathology setting, algorithms can be trained to recognize patterns that correlate with diagnosis, prognosis, biomarkers, or response to therapy. For example, computational models can detect tumor boundaries, count mitoses, quantify immune cell infiltration, or estimate molecular expression based on morphological cues. The synergy between digital and computational pathology enables more objective, reproducible, and scalable interpretations of tissue samples.
Whereas traditional pathology relies heavily on human visual interpretation, computational pathology leverages data-driven methods to support or augment human judgment—especially in cases where subtle patterns or massive data volumes make manual review impractical.
The Digital Pathology Workflow: From Slide to Insight in 7 Steps
To understand how digital and computational pathology integrate in practice, let’s walk through a typical digital pathology workflow:
1. Slide Preparation
Tissue samples are processed, sectioned, and stained in the same manner as for conventional microscopy, often using hematoxylin and eosin (H&E) or immunohistochemistry (IHC).
2. Whole Slide Scanning
The prepared glass slides are then fed into digital pathology whole-slide scanners. These scanners capture the entire tissue section at high magnification and translate it into a digital image file. This is the step that produces the digital pathology slides central to the workflow.
3. Image Management and Storage
The scanned images are stored in a digital pathology image management system or server, often with metadata about the sample, staining, patient, and clinical history. These digital pathology systems serve as repositories, enabling indexing, retrieval, annotation, and version control.
4. Visualization and Review
Pathologists or researchers access the digital images via viewing software. They may pan, zoom, annotate, or measure features just as they would with a microscope—but with added ease, collaboration, and digital scaling.
5. Computational Analysis
Here’s where computational pathology steps in. Algorithms—often based on machine learning or deep learning—analyze the digital slides to detect features, classify regions, and quantify metrics. These analyses may produce outputs like segmentation maps, cellular counts, or probability heatmaps.
6. Interpretation and Reporting
The results of computational analysis are reviewed, interpreted, and integrated into diagnostic or research reports. Pathologists may make final adjustments, verify algorithmic predictions, or combine them with other clinical data to inform their decisions.
7. Storage, Sharing, and Archival
The final annotated images, metadata, and reports are archived. Because digital images don’t degrade over time like glass slides might, the archive becomes a durable reference for future review, audits, or additional analysis.
This end-to-end digital pathology workflow enables a shift from one-off slide reviews to scalable, data-rich pathology practice.
Key Components of Digital Pathology Systems
Digital pathology systems comprise multiple interconnected components. Some of the critical pieces include:
Whole Slide Scanners: As noted earlier, these are instruments that produce digital pathology slides. Different scanners offer various speeds, magnification levels, and throughput—some are optimized for scanning a few slides, while others are designed for high-volume scanning.
Image Management and Server Platforms: After scanning, the images must be stored, indexed, and accessed efficiently. The image management system handles storage, metadata, versioning, user access controls, and linking to clinical databases.
Viewer and Annotation Software: Viewing software lets users inspect, zoom, pan, measure, annotate, and navigate slides. Annotation tools support marking regions of interest, centroids, line measurements, or even linking pathology reports.
Computational Engines and AI Modules: These are software or algorithms that run feature extraction, segmentation, classification, or predictive modeling on the images. They may be packed modules within pathology suits or external tools integrated into the system.
Integration with Laboratory Information Systems (LIS) and Electronic Health Records (EHR): Seamless interoperability ensures that specimen metadata, patient data, and analysis results are linked to patient records and accessible across platforms.
Together, these digital pathology systems provide a robust infrastructure for managing and analyzing tissue images at scale.
Benefits and Challenges of Digital & Computational Pathology
5 Benefits
1. Scalability and Efficiency
Digital slides can be reviewed remotely and in parallel, reducing turnaround times and logistical delays, especially in distributed or global settings.
2. Objective Quantification
Computational methods reduce subjectivity by applying consistent criteria across cases, enabling reproducible measurements and metrics.
3. Collaboration and Telepathology
Remote sharing of digital slides employs consultation across geographic boundaries, enabling second opinions and networks of expertise.
4. Archival Stability
Unlike glass slides, digital pathology slides do not degrade with repeated handling; digital archives preserve data fidelity over time.
5. Research Anablement
Massive datasets of annotated digital slides support the discovery, validation of biomarkers, development of AI, and retrospective studies.
5 Challenges
1. File Size and Storage
Whole-slide images are enormous, often exceeding multiple gigabytes per slide. Infrastructure must support high-capacity storage, fast retrieval, and scalable networking.
2. Standardization and Variability
Differences in staining protocols, scanner calibration, and slide preparation introduce variability that can confuse computational models. Cross-platform standardization is essential.
3. Validation and Regulation
Algorithmic methods intended for clinical use must undergo thorough validation and regulatory approval to ensure safety and reliability.
4. Workflow Integration
Adapting existing lab workflows to adopt digital systems requires training, change management, and compatibility with legacy processes.
5. Cost
Acquisition of high-end scanners, storage, computational resources, and software licenses can be expensive—especially for smaller institutions.
Use Cases in Digital and Computational Pathology
Digital pathology and computational pathology are already being used in diverse clinical and research settings:
Cancer Diagnostics and Grading
Algorithms can assist in tumor segmentation, mitotic figure counting, and grading of cancers like breast, prostate, or glioma. By combining human judgment with algorithmic consistency, pathology workflows become more robust and reliable.
Biomarker Discovery and Quantification
In research contexts, computational models analyze large image sets to identify morphological patterns correlated with biomarkers or patient outcomes—especially helpful in retrospective studies using archival tissue.
Companion Diagnostics
In drug development, computational pathology aids in assessing how tumors respond to therapies, quantifying the degree of marker expression, and stratifying patients for clinical trials.
Remote Consultation and Telepathology
Digital slides enable pathologists to consult on cases across institutions and borders in real-time, improving access to subspecialist expertise.
Quality Assurance and Error Reduction
Automated checks can flag out-of-focus regions, dropped slides, staining artifacts, or inconsistent sample quality—serving as a safety net for pathologists.
The Relationship Between Digital Pathology and Computational Pathology
While often discussed together, digital pathology and computational pathology are conceptually distinct yet deeply interdependent. The digital foundation (i.e., high-fidelity slides, robust image systems, consistent workflow) is necessary before applying computational tools effectively. Without reliable digital slide acquisition and management, downstream analysis can fail.
Thus, implementing nontrivial digital systems is a prerequisite to developing and deploying computational pathology models. The success of any analytic algorithm depends crucially on the quality of the imaging, sample preparation, scanner calibration, and metadata integrity. In practice, many labs begin with the adoption of digital pathology and gradually layer in computational pathology capabilities as the infrastructure matures.
When done well, digital and computational pathology combine to yield powerful synergy: pathologists viewing virtual slides assisted by algorithmic insights and robust measurement, enabling better diagnoses, more reproducible research, and accelerated discovery.
Trends and the Future of Digital Computational Pathology
The trajectory of digital and computational pathology is toward greater automation, interoperability, and multimodal integration. Future systems will not only analyze tissue architecture but also integrate molecular, genomic, and spatial data to provide holistic diagnoses and predictive models.
Deep learning models will become more generalized, capable of adapting to variations in staining or slide preparation. Cloud-based solutions and federated learning will enable collaborative model training without sharing raw patient data, helping maintain privacy while expanding datasets.
As adoption spreads, digital pathology will become a standard of care—not just for reference labs, but in every hospital and diagnostic center. Computational pathology may one day be recognized as a core clinical tool, helping guide therapeutic decision-making, prognostication, and personalized medicine.
Elevate Your Pathology Research with Reliable Biospecimens
To fully leverage the potential of digital systems and computational pathology, you need access to high-quality, well-characterized tissue samples. That’s where Superior BioDiagnostics comes in.
As a reputable U.S.-based biobank specializing in FFPE human and biospecimens, Superior BioDiagnostics offers both malignant and normal tissue specimens across many tissue types. Our catalog includes blocks, sections, and slides, with associated diagnostics, tumor type, TNM staging, and histologic grading data when available.
Whether you’re building large-scale digital slide libraries, validating image analysis models, or performing biomarker studies, Superior BioDiagnostics supplies the quality specimens you need—often with next-day U.S. delivery.
Contact us today and harness the synergy of digital pathology and computational pathology with reliable, traceable biospecimens from trusted sources.