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Medical image labeling for healthcare AI teams

Medical Image Annotation
& Data Labeling

Precise BPO Solution provides outsourced medical image annotation services for healthcare AI teams — bounding boxes, polygons, segmentation masks, and keypoints, labeled to the guidelines you give us. Our in-house team of 540+ in Pune, India has labeled 50M+ medical images and 330M+ video frames across all our projects. Our workflows are aligned with ISO 27001, HIPAA and GDPR requirements.

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Brain scan with ventricle, lesion and cerebellum regions segmented
Up to 99.8% Accuracy Target QA-checked
50M+ Medical Images Labeled Since 2017
330M+ Video Frames Processed All annotation types
540+ In-House Team In-house & NDA-bound
24–48 hrs Turnaround Standard batch
18+ Years in Business Est. 2008 · Pune, India
HIPAA-Aligned Security Standard ISO 27001-Aligned · GDPR-Aligned
On This Page
01 What Is Medical Image Annotation 02 Who We Label Medical Data For 03 Bounding Box vs Polygon vs Pixel-Level Segmentation 04 Capabilities 05 Workflow 06 Projects 07 Tools & Platforms 08 Why Precise BPO 09 Three-Stage QA 10 Hiring In-House vs General-Purpose BPO vs Precise BPO 11 Pricing Models 12 Testimonials 13 FAQ 14 Get a Free Pilot

At a Glance

🔒ISO 27001-Aligned
🏥HIPAA-Aligned
🇪🇺GDPR-Aligned
📋NDA on Every Project
🧪Free Pilot Available
🔧Platform-Agnostic

🌍 Serving enterprises across US · UK · Canada · Australia · Europe · Middle East · APAC · LATAM

01
The Fundamentals

What is Medical Image Annotation?

Medical image annotation is the process of labeling images and video — X-rays, CT scans, MRIs, ultrasounds, pathology slides, and surgical footage — so AI models have structured examples to learn from. Our team marks regions with boxes, polygons, masks, or keypoints following the labeling guidelines your own team provides; we do not make clinical judgments about the images.

This is the data labeling work behind computer vision models for radiology automation, pathology AI, surgical robotics, and diagnostic imaging research. The work spans bounding boxes for locating structures, pixel-level segmentation for region boundaries, and keypoints for skeletal and dental landmarks. We choose the annotation type with your team, based on your guidelines and the model you're training. Many projects involve DICOM files, and we format the labels to suit your pipeline.

We typically deliver JSON, XML, CSV, or COCO-style annotation files that load straight into your training pipeline.

Radiology Annotation
X-ray, CT and MRI images labeled with bounding boxes and segmentation to your guidelines, for teams training detection and segmentation models.
Pathology & Histology
Polygon and bounding-box labeling on histology and biopsy slide images, following annotation rules supplied by your pathology team.
Surgical & Medical Video
Frame-by-frame labeling of instruments and visible regions in surgical and endoscopy footage, based on your class definitions.
Output Formats
COCO, JSON, XML, CSV or DICOM-compatible exports.
MD
About the Service

Medical Image Annotation at Precise BPO

18+ Years in Business. 330M+ Video Frames Processed.
18+
Years in business, since 2008
Since 2008
50M+
Medical images labeled
Radiology, pathology & more

Since 2017, Precise BPO has provided data labeling services for radiology, pathology, surgical video and EMR structuring teams, working to the guidelines each client gives us. Our Pune delivery center runs around the clock, so work keeps moving across time zones.

Our annotators are trained on your guidelines, and multi-reviewer checks keep labeling consistent across the dataset. We handle X-ray, CT, MRI, ultrasound, PET, mammography, dental imaging, and pathology slides, and we adapt to your annotation platform and output schema.

For high-volume radiology and detection projects, we deliver reviewed labels as bounding boxes, polygons, keypoints, or pixel-level masks. Most clients start with a pilot batch and move to production volumes once the guidelines are settled. Every delivered batch follows your agreed format and is ready to load into your pipeline.

We label organs, tumors, fractures, dental structures, and skin regions, on both long-running radiology programs and short, time-bound datasets. Healthcare AI teams in 27+ countries use our annotation services. We also offer online data entry, medical claims data entry and data de-identification, and can cover them under the same NDA as your annotation work.

🚀
Dedicated Annotation Teams
Our in-house team of 540+ is trained on each client's guidelines and handles millions of medical annotations a month.
🩺
Guideline-Based Labeling & Reviewer Checks
Every annotation follows your project guidelines and passes reviewer sign-off before delivery.
🔐
Security & Access Control
Access is restricted by role, all staff are under NDA, and activity is logged.
Request a quote
Dental X-ray with landmark points marked on the teeth
Radiology scan with annotation labels drawn over it
Skin image with a lesion outlined by a segmentation mask
X-ray with a fracture marked by a bounding box and segmentation mask
02
Industry Coverage

Who We Label Medical Data For

We label data for teams building imaging and healthcare AI. These are the kinds of organizations we usually work with.

🏥

Hospitals & Diagnostic Centers

Boxes and masks on X-rays, MRIs, CT scans and mammograms, for computer-aided detection tools.

01
🩻

Radiology & Imaging Service Providers

Organ, tumor and region outlines for radiology image segmentation datasets.

02
⚙️

Medical Device & Equipment Manufacturers

Labeled images and video for AI in imaging devices, surgical robotics and diagnostic systems.

03
🧬

Pharmaceutical & Biotech Companies

Labeling support for research teams working with pathology slides and other imaging datasets.

04
🔬

Healthcare AI & Research Organizations

Image and data labeling for predictive models, such as patient monitoring and clinical workflow automation.

05
💻

Telemedicine & Digital Health Platforms

Labeled images and EMR text annotation for AI triage and virtual care tools.

06
🦴

Orthopedic & Dental AI Platforms

Skeletal, dental and joint landmark annotation on X-ray and 3D scan datasets.

07
🧪

Clinical Research Organizations (CROs)

Imaging annotation and data structuring for clinical research datasets.

08
Choosing the Right Annotation Type

Bounding Box vs Polygon vs Pixel-Level Segmentation — When to Use Which

Annotation type affects both model performance and labeling cost. Here is how the three compare for medical work. For a deeper breakdown, see our bounding box annotation guide.

Criteria Bounding Box Polygon Pixel-Level Segmentation
Shape Rectangle around organ or lesion Multi-point shape-following outline Pixel-level mask per tissue class
Best for Quick localization — tumors, fractures, lesions Irregular structures — organs, masses, abnormal regions Detailed region boundaries — radiology & pathology AI
Annotation Speed Fastest — single drag Moderate — path-tracing workflow Slowest — pixel-by-pixel
Cost Efficiency Highest — minimal effort per region High — scales well with volume Lowest — intensive per image
Boundary Precision Object-level (includes background) Follows the outline closely Closest fit to the shape
Video / Temporal Excellent — fast frame tracking Good — frame-by-frame path tracking Very high effort per frame
Common Use Cases Tumor localization, fracture detection, organ counting Organ outlines, irregular masses, surgical planning Radiology AI, pathology slide analysis, tissue mapping
Precise BPO Service Bounding Box Annotation → Polygon Annotation → Semantic Segmentation →

Not sure which annotation type fits your medical AI project? Talk to our team — we'll recommend an approach based on your modality, model architecture, and dataset requirements.

04
What We Do

Medical Image Annotation Capabilities

We label radiology, pathology, surgical video and EMR data for hospitals, medtech companies and AI teams, including large multi-class datasets.

✓
Bounding Boxes & PolygonsLesions, tumors, organs and bones outlined on X-ray, CT and MRI images.
✓
Pixel-Level SegmentationOrgan, lesion and region masks drawn to your class definitions.
✓
Keypoints & Landmark AnnotationPoint labeling for skeletal, dental and soft-tissue landmarks.
✓
Medical Video AnnotationFrame-by-frame labeling of surgical video, endoscopy footage and ultrasound clips, including robotic-surgery footage that needs consistency across frames.
✓
Medical Report & EMR StructuringTagging and structuring of unstructured medical reports and EMR text into organized, AI-ready datasets.
✓
Three-Stage Quality AssuranceEvery batch is checked three times before delivery, against a target of up to 99.8% accuracy. Details below.
✓
Tool-Assisted AnnotationAnnotators use assisted tooling where it suits the project, and a reviewer checks every output.
✓
Guideline CustomizationWe help turn your requirements into a written guideline (class list, rules per modality, how to handle edge cases) and update it as feedback comes in.
Request a quote
Scan with organ segmentation and lesion labels drawn over it
organ_seg lesion landmarks modality: CT/MRI review: three-stage QA: ✓ sign-off QA up to 99.8% target ANNOTATED 3 REGIONS / SCAN SAMPLE

Medical Annotation Workflow

Six steps, from scoping to delivery.

1

Requirement Analysis

We work with your team to understand imaging modalities, use case, volumes and quality expectations, and agree the scope.

Modality scoping Class taxonomy Annotation rules SLA setup
2

Secure Setup & Data Handling

Data arrives over encrypted transfer and is held in access-controlled storage under NDA. If your images contain patient identifiers, they are de-identified before labeling starts, by your team or as a separate service from us.

Encrypted transfer NDA protection Access control De-identification option
3

Annotation & Labeling

Our annotators label images and video with bounding boxes, polygons, segmentation masks, and keypoints, working in your platform or ours. Video is labeled frame by frame so labels stay consistent over time.

Organ & lesion labeling Pixel segmentation Frame-level tracking Class tagging
4

Quality Assurance

Each batch goes through the three-stage QA described below. Automated checks flag inconsistencies before human review.

Label accuracy check Consistency audit Sampling Reviewer sign-off
5

Client Review & Refinement

We run sample reviews with you, apply your feedback and update the guidelines.

Sample delivery Feedback loop Revision cycles
6

Delivery & Ongoing Support

Final datasets are delivered in JSON, XML, CSV, COCO, or custom formats, via secure transfer. We stay available for updates and dataset versioning, and for fixing anything that doesn't load cleanly into your pipeline.

JSON / XML / CSV COCO format Secure delivery Ongoing support
Start With a Pilot
Send a sample of your data and we will label a free pilot batch, covered by an NDA, before you commit to anything.
06
Recent Projects

Medical Annotation Projects We've Delivered

Examples of organ segmentation, surgical video, report structuring, pathology, and radiograph work, all done to the client's own guidelines.

🩻
Radiology AI · France

Abdominal CT Labeling at Volume

Client Need: A radiology AI company needed abdominal CT images labeled at a rate of about 70,000 bounding boxes a day, following guidelines written by its own clinicians.
Solution: We combined box labeling with semantic segmentation of liver and kidney regions, with batch checks and sample sets returned to the client team each cycle.
  • Liver and kidney regions labeled to the client's guideline
  • Client clinicians reviewed samples each cycle
  • Batch-level checks before each delivery
🤖
Surgical Robotics · South Korea

Surgical Video Frame Labeling

Client Need: A surgical robotics team needed endoscopy and robotic-surgery footage labeled frame by frame, with instruments tracked through each video.
Solution: Our annotators labeled instruments and marked regions from the client's class list, one frame at a time, with reviewer checks between batches.
  • Instruments tracked frame by frame
  • Footage labeled in agreed batches
  • Client class list followed as provided
📄
Health Analytics · Australia

Medical Report Structuring

Client Need: A national health analytics program had a large backlog of unstructured medical reports to turn into structured data.
Solution: We pulled out, tagged, and organized key fields across large report volumes, in workflows aligned with GDPR and HIPAA requirements.
  • Reports converted into structured datasets
  • Fields tagged to the client's schema
  • Data handled under NDA at every step
🔬
Cancer Research · Israel

Histology Slide Outlining

Client Need: A cancer research group needed a large set of histology slide images outlined for model training.
Solution: We used polygon and bounding box labeling, following the outlining rules written by the client's pathologists, who spot-checked returned batches.
  • Slide regions outlined to the client's rules
  • Batches spot-checked by the client's pathologists
  • Same rules applied to every returned batch
🦴
Orthopedic & Dental AI · USA & UAE

Radiograph Landmark Labeling

Client Need: Orthopedic and dental AI teams in two regions needed landmark points placed on X-rays using the same reference guide.
Solution: We did high-volume keypoint annotation against one shared guideline, so images from both regions were labeled the same way.
  • Landmarks placed to a common guide
  • Consistent labeling across two regions
  • Files delivered in the client's pipeline format
🩺
Telemedicine · Global

Image and EMR Labeling for an AI Triage Tool

Client Need: A telemedicine platform wanted a labeled image dataset, plus tagged EMR text, for an AI-assisted triage project.
Solution: We combined bounding box and segmentation labeling with EMR text labeling, using the platform's own label definitions.
  • Images and EMR text labeled in one engagement
  • Multi-specialty images covered
  • Labels delivered in the platform's format
Tool & Platform Compatibility

Platforms, Formats and Secure Transfer

We work in the annotation tools you already use, or suggest a setup if you don't have one. Exports are DICOM-compatible for radiology archives, and there is no lock-in.

🖥️Annotation Platforms
CVAT (Computer Vision Annotation Tool) Labelbox 3D Slicer / MITK Roboflow Annotate SuperAnnotate Label Studio V7 Darwin Custom / In-house Tools
📁Export Formats
JSON (annotations & metadata) DICOM-compatible exports COCO-style JSON CSV tabular export XML structured reports NIfTI (volumetric) Custom schema on request
🤖Frameworks We Deliver To
PyTorch / TorchVision TensorFlow / Keras MONAI (medical imaging) MMDetection nnU-Net compatible
🔒Secure Transfer
Encrypted SFTP AWS S3 (private bucket) Google Cloud Storage Azure Blob Storage Secure client portals Encrypted email delivery
08
Why Precise BPO

Why Healthcare AI Teams Work With Us

We're a Pune-based data operations company. We started in 2008 as a data entry business and added AI labeling in 2017. Our in-house team of 540+ handles radiology, pathology, surgical video, and EMR labeling, and we take programs from pilot to production.

Start a free pilot →
⚡
18+ Years in Business

Running since 2008: data entry first, AI labeling from 2017.

👥
In-House Team of 540+

Our own annotators, trained on your guidelines and working under NDA. No crowdsourcing.

🔒
Security & Data Handling

Access control, NDA-bound staff, and automated security monitoring protect your imagery and datasets.

🎯
Target of Up to 99.8% Accuracy

Three-stage QA: annotator peer review, a label consistency check, and a QA audit against your guidelines.

💰
40–60% Below US/UK Rates

Delivery from India keeps pricing 40–60% below typical US/UK rates, and the pilot batch is free.

🔧
Platform-Agnostic & Format-Flexible

We work in your preferred tooling, such as CVAT, Labelbox, V7, or 3D Slicer, and deliver in whatever format your pipeline needs.

Medical scan with organ and lesion labels applied
Quality Assurance

Three-Stage QA: How Every Batch Is Checked

Every batch goes through three stages of checks before delivery. Each stage catches labeling, consistency, and guideline errors early, so they do not compound downstream.

For how we structure review and sign-off, see our annotation governance framework.

S1

Annotator Self-Check & Peer Review

The annotator reviews boundary placement and class assignment against the project guidelines before submitting
Senior annotator cross-check: accuracy and multi-class correctness across the batch
Batches that fail the Stage 1 threshold are returned for correction before moving to Stage 2
Stage 1 Exit Accuracy Target95%+
S2

Label Consistency Check

Labels scored against reference annotations, using tolerance thresholds set for your project
Consistency validation: mislabeled regions, overlapping annotations, and missing classes are flagged and returned for correction
Statistical outlier scan: unusual region sizes, class distributions, or annotation density are flagged for human review
Average consistency score of 0.97 (on a 0–1 scale) across validated batches
Stage 2 Exit Accuracy Target98%+
S3

QA Audit & Client Sample Review

Random sampling audit: the QA lead reviews 10–20% of images per batch
Client sample review: 50–100 annotated images or frames delivered for client acceptance before the full batch proceeds
Iterative feedback: corrections are applied, re-scored in Stage 2, and re-delivered with a full audit trail
Batches that miss the accuracy target are corrected and re-checked before delivery
Final Delivery Accuracy TargetUp to 99.8%

Throughput Capacity

Images / Day (Peak)180K+
Medical Images Labeled (to Date)50M+
How We Compare

Hiring In-House vs General-Purpose BPO vs Precise BPO

If you are weighing outsourcing against building your own team, here is how the options compare. Figures for the other two options are our own estimates.

Criteria Hiring In-House General-Purpose BPO
Precise BPO
Annotation Accuracy Depends on QC setup Varies by vendor Target of up to 99.8% — three-stage QA
Time to First Delivery 6–10 weeks (hire, train, tool) 3–5 weeks 24–48 hours for a standard batch once guidelines are agreed
Scalability for Surge Volumes Fixed headcount, slow ramp Limited, delays common In-house team of 540+, scales with notice
Cost Baseline (salary + infra) Varies by vendor 40–60% below US/UK rates
Security & Compliance Alignment Rarely formal Varies by vendor Aligned with ISO 27001, HIPAA and GDPR
Healthcare Annotation Focus Limited specialization Not specialized Dedicated annotation teams
Video / Frame Medical Annotation Possible but slow Varies by vendor Frame-by-frame tracking
Free Trial / Pilot Not applicable Rarely offered Free pilot batch, no commitment

Evaluating multiple providers? Our annotation vendor comparison covers accuracy claims, security credentials and pricing.

11
Pricing

Medical Annotation Pricing & Engagement Models

Pricing is based on your volume and timeline, with no platform fees and no lock-in. Every engagement starts with a free pilot batch.

🖼️
Per Image

Standard Image Batches

Per-image pricing suits defined radiology datasets, one-off pathology annotation projects, and research teams building initial training sets, with a predictable cost per unit.

🎬
Per Frame

Video Annotation

Per-frame pricing suits surgical robotics datasets, endoscopy review, and ultrasound motion tracking, where frame count is the natural unit of work.

⏱
Per Hour

Complex / Dense Data

We price by the hour for high-complexity annotation such as multi-organ segmentation, dense histology slides and multi-layer pathology features, where per-image pricing doesn't reflect the effort.

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Monthly Retainer

Ongoing Pipelines

We assign a dedicated medical annotation team at fixed monthly capacity, best for hospitals, medtech companies and AI labs with continuous labeling needs or production diagnostic AI pipelines.

📋
Volume discounts available from 50K+ images/month. White-label pricing for BPO partners.
All models include an NDA and a free pilot batch, and we work to a target of up to 99.8% accuracy. For more detail, see our comparison of US and India data labeling pricing.
Start a free pilot →
Global Delivery

Delivery Across Time Zones

Our India-based delivery center runs 24/7, so teams in every region get coverage during their working day.

North America
Overlaps EST/PST hours
United Kingdom
Overlaps GMT hours
Australia & NZ
Overlaps AEST hours
Europe
Germany · France · Netherlands · Nordics
Overlaps CET hours
Asia-Pacific
Singapore · Japan · India · Southeast Asia
Overlaps IST/SGT hours
Middle East & Africa
UAE · Saudi Arabia · South Africa
Overlaps GST hours
Latin America
Brazil · Mexico · Argentina · Colombia
Overlaps EST/CST hours
Remote & Custom
Any region, any time zone
Round-the-clock delivery
Client Feedback

What Our Clients Say

Feedback from the healthcare AI, medtech, and research teams we work with.

★★★★★

"Give them a labeling guide and they stick to it. When we added a new class halfway through, they went back and updated the earlier batches without being asked. Extra volume hasn't been an issue."

S
Sarah M.
ML Lead · Radiology AI Startup, US
★★★★★

"About 8 million histology images. Our pathologists wrote the rules, Precise followed them, and we spot-checked batches as they came back. Everything loaded straight into our pipeline with no reformatting, which saved us a week we hadn't planned for."

D
Daniel R.
Head of Data Science · Biotech Research Lab, EU
★★★★★

"We moved our endoscopy footage to Precise partway through a project. Instrument labels looked the same from the first video to the last, and our feedback was turned around quickly."

J
James K.
Head of Computer Vision · Surgical Robotics Company, UK
★★★★★

"For roughly 5 million CT images, the team asked sensible questions about our guidelines before they started. Every batch then arrived on the date they promised, and we were comfortable with how our data was handled throughout."

A
Anita S.
Data Science Lead · Diagnostic Imaging Company, Canada
★★★★★

"Four years in, Precise still works inside our platform like part of our own team. Batches arrive on time and our reviewers rarely send anything back."

K
Kevin H.
CTO · Healthcare AI Tooling Company, Australia
★★★★★

"Easy to work with. The security setup was clear from day one, and we use them for both EMR structuring and regular radiology labeling updates. Against an in-house US team, the cost works out well."

P
Priya C.
Head of Data · Telemedicine Platform, LATAM
Common Questions

Medical Annotation — FAQs

Answers to the questions we hear most from healthcare AI teams.

It gives your AI models labeled examples of organs, lesions, regions, and instruments to learn from. Our team marks these with bounding boxes, polygons, segmentation, and keypoints, following guidelines your own clinical or data team provides. We do not provide medical interpretation — the labeling rules and any clinical judgment stay with you. See our guide to data labeling for broader context.

Medical annotation is applied to X-ray, CT, MRI, ultrasound, PET, mammography, dental imaging, pathology slides, and surgical or endoscopy video. Our work is labeling to your class definitions: boxes, polygons, masks, and landmark points. Tasks that need a doctor's judgment, such as deciding whether a finding is abnormal, are best kept with your clinical team. If you also need records keyed in, see our online data entry services.

Labels are what a model trains on and is tested against. Your team uses the organs, regions, fractures or cell clusters we mark to train detection and segmentation models, and a separate labeled set to check how well they perform. The clinical decisions behind the guidelines stay with your experts.

We split large datasets into batches and review each one against your guidelines before delivery. Annotators are trained on your class definitions and modality notes, and the team grows as volume does. Datasets can be updated and versioned in stages as your requirements change.

Medical annotation is used in radiology AI (organ and lesion regions), pathology research (slide regions), surgical robotics (instrument positions), dental imaging (landmarks), dermatology AI (skin lesion regions), and EMR-linked platforms (tagged text).

We keep labeling consistent with written guidelines, reference examples from your team, and class definitions for each modality. Reviewers check boundary placement and continuity across samples, and several reviewers look at the same kinds of structures so they are labeled the same way from batch to batch. For more on how we apply these checks, read our governance guide.

We typically deliver DICOM-compatible exports, COCO-style JSON, XML, NIfTI masks, or custom schemas. These work with imaging viewers and ML pipelines, including 3D Slicer, MONAI, PyTorch, and TensorFlow, so your team can review annotations and use the datasets directly for training or research.

Pricing depends on data volume, modality complexity, the number of anatomical structures, whether the data is video, and how much review is needed. Common models include per-image, per-frame, hourly, or monthly retainer. Our India-based delivery is typically 40–60% below US/UK rates. See our data labeling pricing guide or request a tailored quote.

Our workflows are aligned with ISO 27001, HIPAA and GDPR requirements. All annotators sign NDAs before getting project access, access is permission-scoped by role, and automated security checks run across project environments. Patient-identifiable data is de-identified before annotation begins, either by your team or by us as a separate de-identification service.

From the Blog

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⏱ 8 min read →
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⏱ 10 min read →
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⏱ 9 min read →
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— Get Started

Have a dataset to label? Start with a free pilot.

Send us a sample and we'll label a pilot batch for free, under NDA.

Start a free pilot → 📞 Call Us Now
14
— Get in Touch

Start Your Medical Annotation Project

Send us a sample of your data and we will set up a free pilot or quote the project. If you need other labeling types alongside medical, we can cover them in the same engagement: see our data labeling services.

📞
Phone & WhatsApp
+91 7972620994
✉️
🌐
📍
Office
B3, Sant Namdev Marg, Varundavan Colony, Walhekarwadi, Gurudwara Colony, Nigdi, Pimpri-Chinchwad, Maharashtra 411033
🛡️ Standards Aligned
🔒 ISO 27001-Aligned 🏥 HIPAA-Aligned 🇪🇺 GDPR-Aligned

Request a Free Pilot

Get a response within one business day — no commitment required.

Everything you send is covered by an NDA.

🎯

Thank you! Your request has been received.

Our medical annotation team will review your requirements and respond within one business day.