Leave repetitive contouring to AI. Keep the critical judgment with experts.
Manage studies, pre-label, revise, and review in one platform, then export a traceable research or training set.
Each name opens a dedicated English page. 186 is the preset model count (176 organs/structures, 10 lesion models) — not 186 diseases and not inventory. AI output is a draft until review.
Sitemap: sitemap-annotation-models-en.xml · Chinese index: Chinese model index
Hospitals care whether data leaves the network. Labs care about reproducible exports. Model vendors care about training-set versions. Annotation vendors care about review gates. Choose the workflow that fits your team.
Keep data on-site; de-identify on upload; role permissions and audit; single-/multi-blind review configurable per study.
Separate personal and collaborative research. Export DICOM, NIfTI, NRRD, and clinical Excel with review status.
Task-defined capture and labeling; 186-model pre-label + expert refine; freeze versions with guideline and review status.
For teams that already annotate. Crowdsourcing may draft; lesion boundaries, negatives, and disputes need expert review.
Tell us your imaging modality, target anatomy, and deployment needs. Leave an email address or phone number so our team can discuss a suitable sample evaluation.
You can also contact us directly:
Phone +86 137 5502 0164 ｜ Email lk@langhuiai.com
The Langhui medical annotation platform puts DICOM ingest, 186 preset AI auto-label models, human revision, single-/multi-blind review, and versioned export in one traceable workflow. AI produces drafts only. Final labels are confirmed through expert review.
Confirm anatomy and windowing before accepting an AI contour. Then revise pixels, interpolate layers, and check negatives. Resolve disputed labels through independent expert review.
Pre-label outputs Mask or Contour; check 3D continuity in MPR. Training labels must carry model version, guideline version, and review status.
Hospitals verify whether data leaves the network, de-ID, and permissions. Model teams verify coverage by the 186 models; uncovered structures need separate assessment. No diagnostic claims. Performance is evaluated for each project.
High-quality datasets, trusted by researchers.
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.
Manage data and annotation in one system, turning raw clinical images into high-quality, traceable and reusable datasets.
Three connected modules cover the workflow from raw images to standardized datasets.
Manage data ingestion, access permissions and dataset organization.
AI assistance with expert precision.
Review quality and prepare research-ready deliverables.
Quality control throughout the workflow supports consistent research datasets.
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 186 preset 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.
High-quality datasets to support medical research.
Extract imaging features from precisely delineated regions to build radiomics prediction models.
Build training and validation datasets to support research into disease detection and classification models.
VOI-level classification labels are linked to clinical data to support intelligent classification research.
Compare lesion size in pre- and post-treatment images to study changes in treatment response.
Combine imaging phenotypes and clinical data to build disease-prognosis prediction models.
Support cloud collaboration across experts and research projects, with controlled isolation and sharing.
Two review modes and end-to-end status tracking support research dataset verification.
A reviewer independently evaluates annotation results for routine quality control.
For routine research quality controlMultiple experts independently cross-review annotations for research with more demanding quality requirements.
For research with higher review requirementsAn integrated workflow for producing research datasets.
186 preset AI auto-label models support efficient annotation, while medical experts review accuracy and quality.
Collection, de-identification, annotation, review, export and analysis are available in one platform, reducing tool switching and format conversion.
Inclusion and exclusion records, traceable review status and versioned annotation guidelines support datasets that can be explained, reproduced and reused.
De-identification, access isolation and private deployment are used to meet hospital data security and regulatory compliance requirements.
Schedule a demo today to get platform solutions and dataset planning advice for your research scenario.