license: apache-2.0
task_categories:
- visual-question-answering
language:
- en
- zh
tags:
- medical-imaging
- PET/CT
- 3D vision-language model
- multimodal learning
- anatomical reasoning
- metabolic reasoning
- radiology
- nuclear medicine
pretty_name: MetaStructAtlas
private: false
MetaStructAtlas: A Grounded 3D Vision-Language Dataset and Benchmark for Functional and Structural Reasoning in Whole-Body PET/CT
Dataset Summary
MetaStructAtlas is a large-scale grounded 3D vision-language dataset designed for functional and structural reasoning in whole-body PET/CT imaging.
Unlike existing medical vision-language datasets that mainly focus on single-modality or regional imaging, MetaStructAtlas integrates:
- 3D PET metabolic information,
- 3D CT anatomical structure,
- organ-level segmentation masks,
- structured radiology reports,
- spatially grounded vision-language annotations.
The dataset provides a unified framework for developing and evaluating foundation models capable of understanding the relationship between anatomical structures and metabolic abnormalities in whole-body PET/CT.
MetaStructAtlas contains:
- 490 paired whole-body PET/CT volumetric scans
- 50,470 organ-level segmentation masks
- 103 anatomical structure classes
- 305 fine-grained anatomical substructures
- 100,565 grounded visual question-answering (VQA) pairs
Each textual description is explicitly associated with corresponding anatomical regions and imaging evidence, enabling interpretable multimodal reasoning.
Dataset Motivation
Whole-body PET/CT combines:
- PET-derived molecular metabolic information
- CT-derived anatomical morphology
and is widely used for oncology screening, staging, treatment response evaluation, and systemic disease assessment.
However, current medical vision-language datasets are mainly limited to:
- 2D images,
- regional CT analysis,
- ungrounded image-report pairs,
- anatomical-only reasoning.
MetaStructAtlas addresses these limitations by providing dense spatial grounding between: PET image | CT image | Segmentation mask | Radiology description | Question-answer reasoning
This enables models to learn clinically meaningful relationships between:
- organ anatomy,
- morphological abnormalities,
- FDG metabolic activity.
Dataset Construction
The dataset construction pipeline contains four major stages.
1. Whole-body PET/CT Collection
MetaStructAtlas was constructed from retrospectively collected whole-body 18F-FDG PET/CT examinations.
Each case contains:
- co-registered PET volume
- CT volume
- clinical radiology reports
All imaging data were anonymized before dataset construction.
2. Anatomical Segmentation and Spatial Grounding
CT volumes were processed using an automated anatomical segmentation pipeline.
The dataset provides:
- 103 standardized anatomical masks per subject
- 305 fine-grained anatomical structures
These masks provide spatial references for grounding textual findings.
The anatomical structures cover:
- brain
- thoracic organs
- abdominal organs
- pelvic organs
- cardiovascular structures
- gastrointestinal tract
- vertebrae and ribs
3. Clinical Report Structuring
Free-text radiology reports were transformed into structured annotations.
The extraction process identifies:
Anatomical entities
Morphological descriptions
Examples:
- normal morphology
- abnormal density
- lesion characteristics
- structural changes
FDG metabolic descriptions
Examples:
- increased FDG uptake
- physiological uptake
- abnormal metabolic activity
The extracted descriptions are spatially linked to corresponding anatomical masks.
4. MetaStructVQA Generation
Based on grounded annotations, we generated MetaStructVQA, a hierarchical 3D VQA benchmark containing:
100,565 QA pairs.
Benchmark Structure
MetaStructVQA contains three reasoning levels.
Level 1: Foundational Grounding
Tasks:
Organ Identification
Input:
- CT volume
- anatomical mask
- question
Output:
- anatomical structure classification
Modality Identification
Input:
- PET or CT volume
Output:
- modality classification
Purpose:
Evaluate basic visual perception and anatomical localization.
Level 2: Targeted Clinical Characterization
Tasks include:
Morphological Description
Based on CT:
- lesion appearance
- density
- structural abnormalities
FDG Metabolism Description
Based on PET/CT:
- FDG uptake pattern
- metabolic abnormalities
Both tasks include:
- mask-guided versions
- mask-free versions
Purpose:
Evaluate structure-aware clinical reasoning.
Level 3: Comprehensive Multimodal Reasoning
Tasks integrate:
- anatomical information
- morphological characteristics
- metabolic activity
- quantitative measurements
Examples:
- SUVmax interpretation
- lesion localization
- anatomical relationship reasoning
- negative finding identification
Purpose:
Evaluate advanced whole-body PET/CT reasoning.
Data Format
Each sample may contain:
{ "pet": "xxx.nii.gz", "ct": "xxx.nii.gz", "mask": "xxx.nii.gz", "report": "...", "question": "...", "options": [], "answer": "" }
Intended Use
MetaStructAtlas can support research in:
- 3D medical vision-language models
- multimodal foundation models
- PET/CT representation learning
- anatomical reasoning
- metabolic reasoning
- medical VQA
- radiology AI
Limitations
Current version limitations:
- Organ-level grounding
The dataset focuses on anatomical structures rather than lesion-level segmentation.
- Single PET tracer
The dataset mainly includes 18F-FDG PET/CT.
- Spatial alignment
Segmentation masks are derived from CT and transferred to PET space through registration.
Current Release Status
🚧 Under preparation
The dataset repository currently provides documentation only.
The full imaging dataset will become available after publication acceptance.