Clinical-Grade Full-Body 3D CT Scan Annotation for Anatomical Structure Segmentation (2,500+ Studies)

Medical Image Annotation, 3D CT Segmentation, Anatomical Structure Labeling, Quality Control for Medical AI Datasets

The goal of the project was to build a high-precision 3D segmentation dataset for training and validating an AI model capable of identifying and isolating major anatomical structures in full-body CT scans.

The dataset consisted of complete 3D CT volumes with 150–400 slices per study, provided in DICOM and NIfTI formats. The scans represented a wide range of patient anatomies and imaging variations, requiring consistent, expert-level annotation across large volumes of data.

Accurate anatomical segmentation in CT imaging is critical for downstream clinical applications, including diagnostics, treatment planning, and future pathology detection. The client faced several key challenges:

• Complex volumetric data requiring slice-by-slice precision across hundreds of images per scan

• High clinical accuracy requirements, where even small segmentation errors could impact model performance

• Tight delivery timelines while maintaining consistent annotation quality at scale

• The need for certified medical expertise, not generic image annotators

The client needed a partner who could combine medical knowledge, scalable operations, and advanced annotation tooling.

Mindy Support was selected due to our proven expertise in medical image annotation and healthcare AI projects, particularly in complex 3D imaging tasks.

Our strengths included:

• A dedicated team of certified radiology annotators with hands-on experience in CT imaging

• Deep understanding of anatomical structures and clinical relevance

• Established workflows for volumetric segmentation and quality assurance

• Ability to scale rapidly while maintaining strict quality benchmarks

Mindy Support delivered an end-to-end medical annotation solution tailored to the client’s AI training needs.

Key Workstreams

• Expert segmentation of major anatomical structures, including: liver, spleen, kidneys, pancreas, lungs, heart, brain, sinuses

• Layer-by-layer polygon segmentation across all CT slices

• 3D mask reconstruction to ensure volumetric consistency

• Annotation performed using advanced medical imaging tools optimized for CT data

• Clear scope control and multi-level QC, ensuring alignment with clinical standards

All annotations were reviewed under a structured quality framework to ensure consistency, accuracy, and usability for AI model training.

• 2,500+ fully annotated 3D CT studies delivered in just 8 weeks

• 95% Dice coefficient achieved on segmentation benchmarks

• Creation of a robust, organ-level dataset suitable for: model training and validation, future pathology-specific segmentation, expansion into additional clinical use cases