Medical Annotator

Brain MRI tumour assessment and CT brain triage datasets

LOCATION- Gurugram, India

WORK MODEL- On site 5 days/ week

EXPERIENCE- 1-3 years

About ImagingIQ

ImagingIQ is building AI-assisted medical-imaging and radiology workflow solutions that help clinical teams analyse studies, review structured findings and integrate AI outputs into real-world reporting workflows. Our current work centres on brain MRI oncology and is expanding into brain CT, trauma, vascular, oncology and volumetric applications. Our systems work with radiologists in the loop: AI outputs support clinical review and do not replace the final clinical decision.

The role

We are looking for a Medical Annotator to prepare image annotations and structured reference labels for AI development and evaluation. Initial work focuses on brain MRI tumour segmentation, with planned responsibilities across Tumor characterisation and CT brain triage. The work includes image review, mask editing, finding labels, reference measurements and annotation quality checks.

You will work with radiologists, data scientists and product teams under approved annotation protocols. We will provide training and calibration for company-specific tools, label definitions and recent tasks. Radiologists adjudicate uncertain findings and clinical labels; you will document supporting evidence and incorporate their feedback.

Core mandate: Produce accurate, consistent and traceable image annotations that clinical and technical teams can review and use with confidence.

What you will own

- Create and refine brain MRI tumour and anatomical masks across T1-weighted, T2-weighted, FLAIR and contrast-enhanced T1-weighted images, keeping enhancing tumour, non-enhancing tumour and peritumoral oedema distinct under the approved definitions.
- Support Tumor annotation through paired DWI/ADC review for diffusion-restriction labels and protocol-defined ADC measurements, and GRE/SWI review for susceptibility-related blooming. Mark regions and structured attributes, escalating equivocal appearances and artefacts.
- Record protocol-defined imaging attributes, including enhancement, intra- or extra-axial location and anatomical involvement. Check reference dimensions, volumes and mass-effect or midline-shift measurements against the relevant masks or landmarks.
- Prepare tumour subclass reference labels from approved radiologist assessments and, where available, linked histopathology records. Record label provenance and distinguish imaging impressions, confirmed diagnoses and indeterminate cases.
- Build CT brain triage datasets using approved finding categories. Record examination-level labels, and slice-level labels, regions or segmentation masks where required; allow multiple findings within a study.
- Record clinician-defined CT severity and priority categories separately from finding labels, with supporting evidence.



Escalate disagreements or missing context for clinical review and follow the agreed escalation procedure for concerning findings.
- Review masks in axial, coronal and sagittal planes for missed regions, incorrect inclusions, laterality and label errors. Check image-to-mask alignment, spacing, coverage and export integrity, flagging geometry problems for technical review.
- Curate positive and negative examples, mimics and challenging cases. Record missing sequences, poor image quality and unsupported or unassessed findings explicitly so incomplete assessment is not treated as a negative result.
- Create independent reference annotations or correct AI-generated outputs as assigned, maintaining separate versions and following any blinding requirements for evaluation datasets.
- Maintain links between source studies, series, reference reports, annotation versions and review status. Document mismatches between images, masks, measurements and structured AI outputs with evidence for review.
- Participate in reviewer calibration, incorporate radiologist feedback and track disagreements, corrections and recurring errors to improve annotation consistency across MRI and CT tasks.
- Follow confidentiality and controlled data-access procedures, maintain accurate workload records and meet quality and turnaround expectations appropriate to each task’s complexity.

Required qualifications
- Bachelor’s or Master’s degree in Medical Imaging Technology, Radiography, Radiology and Imaging Technology or a closely related imaging discipline. MBBS graduates with relevant imaging and annotation skills are also eligible.
- 1–3 years of relevant experience in medical-image annotation, radiography, imaging research or clinical imaging operations, including hands-on medical-image segmentation or mask editing.
- Strong working knowledge of cross-sectional brain anatomy, anatomical planes, laterality and radiological terminology, including the distinction between intra- and extra-axial locations.
- Practical familiarity with T1-weighted, T2-weighted, FLAIR and post-contrast MRI, plus a basic understanding of paired DWI/ADC and GRE/SWI appearances, their limitations and the need for correlation across sequences.
- Working familiarity with CT brain viewing, including brain, blood and bone window settings, and common triage-relevant findings such as haemorrhage, ischaemic changes, trauma and mass effect.
- Hands-on experience with 3D Slicer, ITK-SNAP or an equivalent annotation platform, including manual contouring, mask correction, region-of-interest measurements and review in multiple planes.




- Working familiarity with DICOM studies, image series and volumetric annotation files such as NIfTI, including orientation, voxel spacing and image-to-mask alignment.
- Ability to follow annotation definitions, distinguish finding labels from severity or priority labels, record uncertainty and escalate cases requiring clinical judgement.
- Clear written communication, attention to detail and reliable record keeping, including annotation trackers, label sources, review status and responsible handling of patient data.

Preferred qualifications
- Previous brain tumour annotation experience, particularly DWI/ADC or GRE/SWI findings, tumour characterisation, localisation or radiologist-reviewed subclass labels.
- Prior CT brain annotation experience involving haemorrhage subtypes, stroke-related findings, traumatic injury or mass effect, with study-level or slice-level labels and masks.
- Experience linking imaging annotations with radiology reports or histopathology records and documenting uncertain, conflicting or unavailable reference labels.
- Experience with annotation quality review, reviewer calibration, agreement assessment or maintenance of independent reference datasets.
- Exposure to PACS, DICOM SEG overlays, volumetric measurements or structured AI output review; basic Python or scripting skills for repetitive data checks are advantageous.

What success looks like
- MRI and CT annotations follow the approved definitions and meet reviewer acceptance criteria, with recurring errors and rework tracked and reduced.
- Masks, structured findings and reference measurements are consistent, and missing or unassessed information remains distinguishable from a negative finding.
- Reference labels are traceable to source images and clinical evidence, with annotation versions, independent review and adjudication status recorded.
- New annotation tasks are taken on after training and calibration, with agreed volumes and turnaround achieved in line with case complexity and quality expectations.

Why this role matters Your annotations will support how ImagingIQ develops and evaluates tumour characterisation and CT prioritisation capabilities. You will help clinical and technical teams build reliable reference datasets and identify errors in segmentation, structured findings and triage outputs.

Pay: ₹500,000.00 - ₹700,000.00 per year

Benefits

- Health insurance
- Leave encashment

Application Question(s):
- Do you have experience performing Annotations for Medical Image segmentation tasks?
- Have you previously worked with Annotation for Brain MRI or CT Brain?
- Do you have experience with Annotation tools like 3D Slicer/ITK-Snap?
- Out of the following which pathology tasks have you worked on:

Oncology

Volumetry

Vascular

Trauma

Please mention all.

Experience

- Medical imaging: 1 year (Required)

Work Location: In person

📌 Medical Annotator (Gurugram)
🏢 Imaging IQ
📍 Gurugram