Update README.md

README.md

@@ -7,9 +7,317 @@ language:
 - en
 - zh
 tags:
-- PET
-- CT
-- medical
-size_categories:
-- 100K<n<1M
----
+- medical-imaging
+- PET/CT
+- 3D vision-language model
+- multimodal learning
+- anatomical reasoning
+- metabolic reasoning
+- radiology
+- nuclear medicine
+pretty_name: MetaStructAtlas
+---
+
+# 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:
+
+1. Anatomical entities
+
+2. Morphological descriptions
+
+   Examples:
+
+   - normal morphology
+   - abnormal density
+   - lesion characteristics
+   - structural changes
+
+3. 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**.
+
+
+The benchmark evaluates progressively increasing reasoning abilities.
+
+
+## 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:
+
+1. Organ-level grounding
+
+The dataset focuses on anatomical structures rather than lesion-level segmentation.
+
+2. Single PET tracer
+
+The dataset mainly includes 18F-FDG PET/CT.
+
+3. Spatial alignment
+
+Segmentation masks are derived from CT and transferred to PET space through registration.
+
+
+Future versions may include:
+
+- lesion-level annotations
+- additional PET tracers
+- larger multi-center cohorts
+
+
+## Citation
+
+If you use MetaStructAtlas or MetaStructVQA, please cite:
+
+@article{zheng2026metastructatlas,
+title={MetaStructAtlas: A Grounded 3D Vision-Language Dataset and Benchmark for Functional and Structural Reasoning in Whole-Body PET/CT},
+author={Zheng, Chenguang and Xue, Le and Zhang, Yichi and others},
+journal={},
+year={2026}
+}