Integration

AI Integration for Sectra Pathology

A technical blueprint for embedding AI-powered whole-slide image analysis and automated quantification directly into the Sectra Pathology PACS workflow, enabling pathologists to focus on complex diagnostic decisions.

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ARCHITECTURE AND INTEGRATION POINTS

Where AI Fits into the Sectra Pathology Workflow

A technical blueprint for embedding AI analysis into the Sectra Digital Pathology PACS, from whole-slide image ingestion to quantified results in the diagnostic report.

AI integration for Sectra Pathology connects at three primary functional layers: the image management layer (Sectra VNA/PACS), the diagnostic workstation (Sectra Pathology Viewer), and the reporting and data export layer. The most common integration pattern uses Sectra's DICOMweb and REST APIs to push whole-slide images (WSI) in DICOM format to a secure, containerized AI inference service. This service, often hosted on-premises or in a compliant cloud, runs algorithms for tasks like automated tumor quantification, mitotic figure counting, or biomarker scoring (e.g., PD-L1, HER2). The AI-generated results—structured as DICOM Structured Reports (SR) or HL7 FHIR Observations—are sent back to the Sectra archive, where they are linked to the original case.

Within the pathologist's workflow, these AI results are surfaced as interactive overlays and measurements directly on the digital slide in the Sectra Pathology Viewer, or as pre-populated fields in the synoptic report template. For example, an AI model for breast cancer can automatically outline invasive tumor regions, calculate the Tumor-Infiltrating Lymphocyte (TIL) score, and suggest a Nottingham grade, all presented for the pathologist's review and final approval. This integration turns the PACS from a passive viewer into an active diagnostic cockpit, reducing manual measurement time from 30-45 minutes per complex case to under 5 minutes for initial AI-assisted review.

A production rollout requires careful governance. AI inferences should be logged with model version, confidence scores, and audit trails for regulatory compliance. Implementing a human-in-the-loop approval step in the Sectra reporting module ensures the pathologist remains the final signatory. Furthermore, integration with the Sectra Workflow Orchestrator can automatically prioritize cases based on AI-flagged urgency (e.g., suspected high-grade tumor) and route them to the appropriate subspecialist. For health systems scaling AI across multiple sites, a centralized AI model management layer can deploy and update algorithms across the Sectra Enterprise Imaging suite, ensuring consistent diagnostic support in both academic and community hospital settings.

DIGITAL PATHOLOGY WORKFLOW BLUEPRINT

Sectra Pathology Integration Surfaces and APIs

Core WSI Integration Points

AI integration for Sectra Pathology begins at the image ingestion and storage layer. When a whole-slide image (WSI) is scanned and stored in the Sectra VNA, a DICOMweb STOW-RS notification or a configured workflow rule can trigger an AI inference pipeline. This pipeline typically:

Pulls the WSI via DICOMweb WADO-RS for a specific series UID.
Processes the image using a containerized AI model (e.g., for tumor detection, grading, or biomarker quantification).
Returns structured results as a DICOM Structured Report (SR) or a JSON payload containing annotations, measurements, and confidence scores.
Stores the SR back into the Sectra archive, linked to the original study, making AI findings a permanent, auditable part of the patient record.

This enables pathologists to open a case in Sectra and see AI-generated heatmaps, annotations, or quantitative data overlaid directly on the WSI within their familiar diagnostic viewer.

SECTRA PATHOLOGY INTEGRATION

High-Value AI Use Cases for Digital Pathology

Integrating AI directly into Sectra's digital pathology workflow automates quantitative analysis, prioritizes critical cases, and enriches diagnostic reports, transforming whole-slide image review from a manual, time-intensive process into an AI-assisted, data-driven practice.

Automated Tumor Quantification & Scoring

Deploy AI models for automated detection and quantification of tumor cells, nuclei, and tissue regions directly within the Sectra viewer. AI calculates metrics like Tumor Proportion Score (TPS) for PD-L1, Ki-67 index, or mitotic count, auto-populating structured report fields and reducing manual counting from hours to minutes.

Hours -> Minutes

Quantification time

Priority Worklist for Critical Findings

Integrate AI triage algorithms that pre-screen whole-slide images as they are ingested into the Sectra VNA. Cases with suspected high-grade dysplasia, micrometastases, or critical margins are flagged and elevated to the top of the pathologist's worklist, ensuring the most urgent slides are reviewed first.

Batch -> Real-time

Case prioritization

AI-Assisted Margin Assessment

For surgical pathology, integrate AI to analyze margin slides for tumor proximity. The algorithm highlights regions of concern on low-power scans, allowing the pathologist to focus review on critical areas. Findings and measurements are embedded as DICOM-SR annotations, streamlining the intraoperative consultation workflow.

Same day

Frozen section support

Automated Quality Control (QC) for Slides

Implement pre-diagnostic AI QC to detect scanning artifacts, tissue folds, staining inconsistencies, or out-of-focus regions. Problematic slides are flagged in Sectra with specific alerts before they reach the pathologist, reducing rescans and diagnostic uncertainty.

Pre-read

QC automation

Structured Report Generation & Coding Support

Connect NLP and coding AI to the Sectra reporting module. As the pathologist dictates or types, the AI suggests relevant SNOMED CT codes, synoptic report templates, and differential diagnoses based on the WSI analysis and clinical history, ensuring comprehensive, billable reports.

1 sprint

Integration timeline

Longitudinal Analysis & Progression Tracking

Leverage the Sectra VNA to link prior and current biopsies for the same patient. AI performs automated comparison, quantifying changes in tumor cellularity, grade, or spatial distribution over time. Trend visualizations and delta metrics are presented within the viewer to support progression assessment.

Batch -> Real-time

Comparative analysis

IMPLEMENTATION PATTERNS

Example AI-Enhanced Pathology Workflows

These concrete workflows illustrate how AI agents and models can be integrated into the Sectra Pathology PACS to automate quantification, support diagnosis, and streamline operational tasks. Each pattern connects via Sectra's APIs and DICOMweb services to interact with whole-slide images (WSIs) and the pathology reporting module.

Trigger: A signed-out colorectal cancer resection case is finalized in Sectra Pathology.

Context Pulled: The workflow agent queries the Sectra API for the case's metadata (specimen type, blocks) and retrieves the associated whole-slide images (WSIs) for the tumor blocks via DICOMweb.

AI Action: A pre-validated AI segmentation model processes the WSIs to:

Identify and segment tumor regions (tumor vs. stroma vs. normal tissue).
Calculate the Tumor-Stroma Ratio (TSR).
Measure the total tumor area and percentage of slide involvement.

System Update: The agent creates a structured DICOM SR (Structured Report) object containing the quantitative measurements and overlays a low-opacity segmentation mask on the original WSI as a new DICOM presentation state.

Human Review Point: The SR and overlay are pushed back to the original case in Sectra as an attached "AI Analysis" series. The pathologist can review, accept, or reject the findings. Accepted metrics can be auto-populated into the synoptic report template using Sectra's reporting macros.

SECURE, SCALABLE PIPELINES FOR WHOLE-SLIDE IMAGE ANALYSIS

Implementation Architecture: Data Flow and System Boundaries

A production-ready architecture for embedding AI into Sectra's digital pathology workflow, from slide ingestion to diagnostic report.

The integration connects at the Sectra Pathology PACS workflow manager, typically via its RESTful APIs and DICOMweb interfaces for whole-slide image (WSI) retrieval. When a new case is registered or a slide is scanned, the system triggers an event. This event is captured by a secure middleware service—often deployed as a container within the hospital's network—which fetches the WSI file (e.g., in .svs or .tiff format) from the Sectra Vendor Neutral Archive (VNA). The service then packages the image and relevant metadata (accession number, stain type, scanner details) into a payload for the AI inference engine.

The AI inference service, which can be a GPU-accelerated cluster on-premises or in a private cloud (e.g., Azure, AWS with a BAA), runs the analysis. Common AI tasks include:

Automated quantification of tumor cellularity, mitotic figures, or biomarker expression (e.g., Ki-67, PD-L1).
Detection and segmentation of regions of interest, such as tumor margins or lymphocytic infiltrates.
Quality control checks for focus, staining artifacts, or tissue folds. Results are formatted as DICOM Structured Reports (SR) or a lightweight JSON schema containing coordinates, confidence scores, and quantitative measurements. This output is pushed back to the Sectra PACS via its API, where it is stored as a linked object to the original slide, and can be displayed as an overlay or a separate finding panel within the Sectra Pathology Viewer.

Governance and rollout require careful planning. A human-in-the-loop review step is typically configured in the Sectra workflow, where AI-generated annotations are presented as a preliminary finding for the pathologist to verify, adjust, or reject. All AI interactions are logged to an audit trail for regulatory compliance and model performance tracking. The architecture supports A/B testing of different AI models by routing a percentage of cases to alternate algorithms, with results compared against ground truth from subsequent sign-outs. This phased approach allows pathology departments to validate clinical utility and build trust before enabling full automation for specific, high-volume tasks like tumor burden scoring.

SECTRA PATHOLOGY INTEGRATION PATTERNS

Code and Payload Examples

Triggering AI Analysis on a WSI

When a new whole-slide image (WSI) is stored in the Sectra VNA for Pathology, a DICOM STOW-RS event can trigger an AI inference pipeline. The system extracts the WSI DICOM series UID and initiates processing via a secure, containerized AI service.

Example Python webhook handler listening for Sectra Storage Commitment events:

python
import requests
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

app = FastAPI()

class StorageCommitment(BaseModel):
    study_instance_uid: str
    series_instance_uid: str
    sop_instance_uid: str
    modality: str

@app.post("/sectra/pathology/wsi-upload")
async def handle_wsi_upload(event: StorageCommitment):
    if event.modality == "SM" or "pathology" in event.series_description.lower():
        # Call AI inference service
        inference_payload = {
            "series_uid": event.series_instance_uid,
            "action": "quantify_tumor_cellularity",
            "priority": "routine"
        }
        response = requests.post(
            "https://ai-service/inference/pathology",
            json=inference_payload,
            headers={"Authorization": f"Bearer {API_KEY}"}
        )
        # Store results back as DICOM SR
        return {"status": "AI analysis queued", "job_id": response.json().get("job_id")}
    raise HTTPException(status_code=400, detail="Not a pathology WSI")

This pattern enables automated quantification (e.g., tumor cellularity, Ki-67 index) as soon as slides are digitized, populating structured reports before pathologist review.

AI-ENHANCED PATHOLOGY WORKFLOW

Realistic Time Savings and Operational Impact

This table illustrates the practical impact of integrating AI into Sectra's digital pathology workflow, focusing on measurable improvements in slide processing, diagnostic support, and operational efficiency.

Workflow Stage	Before AI	After AI	Key Considerations
Whole-slide image (WSI) pre-screening	Manual review of entire slide for region of interest	AI-assisted identification of candidate regions	Pathologist reviews AI-highlighted areas, reducing initial scan time
Quantitative analysis (e.g., mitotic count, Ki-67)	Manual counting across multiple high-power fields	AI-powered automated quantification with heatmap overlay	Pathologist validates counts; audit trail required for regulatory compliance
Tumor margin assessment	Serial section review and manual measurement	AI segmentation for tumor boundary and clearance distance	Integration with Sectra's annotation tools for direct overlay and reporting
Case prioritization and triage	First-in, first-out or manual flagging based on requisition	AI-driven scoring for urgency (e.g., suspected malignancy)	Rules-based routing into Sectra worklist; high-priority cases flagged automatically
Report drafting for standardized elements	Manual entry of measurements, percentages, and grades	AI-generated draft for structured report sections	Pathologist edits and finalizes; ensures consistency with institutional templates
Quality control and secondary review	Random or high-case-load peer review	AI-flagged cases for discrepancy review or rare pattern detection	Used as a decision-support tool for QA workflows; does not replace mandated reviews
Search and retrieval of similar historical cases	Manual keyword search in LIS or file system	Semantic search via AI-powered image similarity	Requires integration with Sectra VNA; enhances diagnostic confidence and training

IMPLEMENTING AI IN A REGULATED PATHOLOGY WORKFLOW

Governance, Security, and Phased Rollout

Deploying AI in a Sectra Pathology PACS requires a controlled approach that prioritizes data integrity, clinical safety, and user adoption.

A production integration for Sectra Pathology must be architected with zero impact on primary diagnostic data. This means AI inference typically runs on a mirrored or dedicated instance of the VNA, with results written back as DICOM Structured Reports (SR) or annotations linked to the original whole-slide image (WSI). Access is governed by the same RBAC and audit trails native to Sectra, ensuring only authorized pathologists can view AI findings. All data in transit and at rest must be encrypted, and any cloud-based AI services should operate under a BAA, with model endpoints secured within the health system's VPN or via private link connections.

A successful rollout follows a phased, use-case-first approach:

Phase 1: Silent Pilot – AI processes retrospective cases in the background. Results are stored but not displayed in the clinical viewer. This validates model performance on local data and establishes baseline metrics.
Phase 2: Assisted Review – AI findings are presented as a non-interruptive sidebar or overlay in the Sectra viewer for a pilot group of pathologists. The primary diagnosis is made by the pathologist, with the AI acting as a consultative tool. All interactions (acceptance, rejection, modification of AI suggestions) are logged for continuous feedback.
Phase 3: Integrated Workflow – AI triggers are embedded into routine workflows. For example, WSIs flagged for high tumor cellularity by an AI quantification model are automatically prioritized in the worklist. Automated measurements for Ki-67 or PD-L1 scoring are pre-populated into structured report templates, saving minutes per case.

Governance is maintained through a Pathology AI Committee—comprising lead pathologists, IT, and compliance—that reviews model validation reports, approves new use cases, and oversees the feedback loop where pathologist corrections are used for model retraining. A clear escalation and fallback protocol is essential: if the AI service is unavailable, the Sectra workflow must default to a standard manual review without disruption. This controlled, phased approach de-risks adoption, builds clinical trust, and delivers incremental value, transforming AI from a novel tool into a reliable component of the diagnostic pipeline.

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AI INTEGRATION FOR SECTRA PATHOLOGY

Frequently Asked Questions (Technical & Commercial)

Practical questions for pathology lab directors, IT managers, and digital pathology leads planning AI integration into Sectra's workflow.

AI results are delivered as DICOM Structured Reports (SR) or via Sectra's API and surfaced directly within the pathology PACS viewer. Typical integration patterns include:

Side Panel Overlay: AI-generated annotations (e.g., tumor regions, mitotic figures) are displayed as semi-transparent overlays on the whole-slide image (WSI). The pathologist can toggle visibility on/off.
Quantitative Dashboard: A separate panel lists quantitative results (e.g., Tumor Cellularity: 45%, Ki-67 Index: 12%) alongside the image.
Worklist Prioritization: Studies with AI-flagged high-risk features (e.g., high-grade morphology) can be automatically elevated in the reading worklist.
Structured Report Pre-population: AI findings auto-populate designated fields in Sectra's reporting module, serving as a draft for the pathologist to verify and finalize.

The key is a non-disruptive integration that provides AI as an assistive layer, preserving the pathologist's primary diagnostic control and workflow.

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.

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