Langhui Medical Annotation Platform
From raw images to standardized datasets
Turn raw clinical images into high-quality, traceable and reusable datasets. AI-assisted annotation works alongside expert delineation, with quality control throughout the workflow to support medical research.
Turn large volumes of data
into usable research assets.
High-quality datasets, trusted by researchers.
An annotation and dataset-building platform for medical imaging research
From data collection and de-identification to AI-assisted annotation, expert review and standardized export, Langhui supports the complete dataset workflow for hospitals, research institutions and medical AI companies. The platform is designed to be accessible to users without a technical background.
One platform, three core capabilities
Manage data and annotation in one system, turning raw clinical images into high-quality, traceable and reusable datasets.
Capabilities across the full workflow
- Imaging Data Management• Multimodal image collection • Image de-identification • Dataset inclusion and exclusion
- Image Data Annotation• AI-assisted annotation • Semi-automatic and manual delineation • MPR and 3D visualization
- Review & Export• Standardized dataset delivery
Core functions: management, annotation, review and export
Three connected modules cover the workflow from raw images to standardized datasets.
Imaging data management, from collection to ingestion
Manage data ingestion, access permissions and dataset organization.
- Data collection: Upload local batches or retrieve images through a hospital PACS integration, with automatic DICOM recognition;
- Data De-identification: Built-in de-identification tools and customizable anonymization rules support patient privacy from the point of ingestion;
- Dataset construction: Individual and collaborative research modes, with flexible control of member visibility;
- Image inclusion and exclusion: Screen images against study criteria and retain inclusion and exclusion records throughout the workflow;
- Clinical Data Correlation: Associate clinical variables at the Patient, Study and VOI levels, with batch import using Excel templates.
Image annotation with AI assistance and precise delineation
AI assistance with expert precision.
- AI-assisted annotation: Run batch inference with 180+ pre-trained segmentation models to produce Mask/Contour outputs, without parameter adjustment;
- Manual/semi-automatic: Use 10+ tools, including freehand drawing, contour fitting, lasso selection, automatic expansion, mask painting, interactive segmentation and automatic tracking;
- Pixel-level editing: Edit anchor points, reshape contours, interpolate between slices, and use copy-and-paste and keyboard shortcuts;
- MPR 3-D Visualization: Use multiplanar reconstruction and automatic 3D lesion reconstruction, with measurements of long and short diameters, volume and density;
- Multimodal Fusion: Review fused PET-CT images and validate annotations in one workspace.
Review and export with rigorous quality control and research-tool integration
Review quality and prepare research-ready deliverables.
- Review Mechanism: Use single-blind review for routine quality control and multi-blind cross-review for research with more demanding review requirements;
- Collaboration & audit trail: Assign review tasks flexibly and manage annotation sharing and isolation to support collaboration and blind review;
- Status management: Track the full workflow: Awaiting annotation → Annotated → Pending review → Reviewed;
- Standard export: Download images (DICOM/nii.gz), clinical data (Excel) and annotations (txt/nrrd/nii.gz) as a package;
- Research-tool integration: Export data for radiomics and deep-learning analysis workflows.
Three steps to build high-quality standardized datasets
Quality control throughout the workflow supports consistent research datasets.
Eight technical capabilities for efficient, precise annotation
Capabilities cover the workflow from AI inference and 3D validation to data de-identification and research data organization.
Deep-learning segmentation and GPU batch inference, with 180+ pre-trained models.
Interactive segmentation and automatic tracking work alongside human annotation to reduce manual delineation effort.
Generate axial, coronal and sagittal views alongside 3D lesion reconstructions to validate annotations from multiple perspectives.
Review, annotate and validate fused PET-CT images in one workspace.
Anchor-point editing, contour reshaping and interpolation between slices provide precise control over annotations.
De-identification on upload and customizable rules comply with the Data Security Law and the Personal Information Protection Law.
Manage image inclusion and exclusion, associate clinical variables at three levels, and support multi-center collaboration.
Export in DICOM, nii.gz, nrrd and Excel formats for research analysis workflows.
Six research applications, from data to discovery
High-quality datasets to support medical research.
Radiomics analysis
Extract imaging features from precisely delineated regions to build radiomics prediction models.
AI-assisted diagnostic model research
Build training and validation datasets to support research into disease detection and classification models.
Disease classification studies
VOI-level classification labels are linked to clinical data to support intelligent classification research.
Quantification of treatment response
Compare lesion size in pre- and post-treatment images to study changes in treatment response.
Prognostic model research
Combine imaging phenotypes and clinical data to build disease-prognosis prediction models.
Multi-center collaborative research
Support cloud collaboration across experts and research projects, with controlled isolation and sharing.
Rigorous review, traceability and data security
Two review modes and end-to-end status tracking support research dataset verification.
Single-blind review
A reviewer independently evaluates annotation results for routine quality control.
For routine research quality controlMulti-blind cross-review
Multiple experts independently cross-review annotations for research with more demanding quality requirements.
For research with higher review requirementsFour key platform strengths
An integrated workflow for producing research datasets.
AI assistance and expert review
180+ pre-trained AI models support efficient annotation, while medical experts review accuracy and quality.
An integrated end-to-end workflow
Collection, de-identification, annotation, review, export and analysis are available in one platform, reducing tool switching and format conversion.
Research-grade & Traceable
Inclusion and exclusion records, traceable review status and versioned annotation guidelines support datasets that can be explained, reproduced and reused.
Data security and compliance
De-identification, access isolation and private deployment are used to meet hospital data security and regulatory compliance requirements.
Request a Platform Proposal
Share your requirements and we will respond within 2 hours during working hours, with a platform demonstration and dataset-planning advice for your research.
A proposal tailored to your research
- A 1:1 platform demonstration covering AI-assisted annotation, MPR/3D visualization and quality review;
- Dataset-building recommendations tailored to your department and research topic;
- Private deployment options and data security and compliance information;
- Reference examples of research partnerships and multi-center collaboration.
You can also contact us directly:
Phone 137-5502-0164 ｜ Email lk@langhuiai.com
Put data to work for medical research
Schedule a demo today to get platform solutions and dataset planning advice for your research scenario.