Results for “medical-imaging”

Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.

Runs NV-Segment-CTMR segmentation on CT or MRI NIfTI volumes and records label-map evidence.

Process and manipulate images using ImageMagick: resize, convert formats, batch process, and retrieve metadata.

This skill recommends performing an imaging study to assess tumor size, appearance, and parasellar extent once biochemical diagnosis of acromegaly is confirmed. Trigger when IGF-1 is elevated and GH fails to suppress to <0.4 µg/L during an oral glucose tolerance test.

Create patient intake forms with history and screening. TRIGGERS - Use when user needs help with intake-form related tasks.

Create patient intake forms with history and screening. TRIGGERS - Use when user needs help with intake-form related tasks.

Organize, query, validate, and convert neuroscience and biomedical data using the Brain Imaging Data Structure (BIDS) standard.

Use this skill when working with Brain Imaging Data Structure (BIDS) datasets: organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars, or creating BIDS derivatives.

Read BEFORE troubleshooting or enhancing camera image quality. Diagnoses and enhances image quality from cameras connected via Image Acquisition Toolbox or USB Webcams support package. Discovers camera capabilities at runtime, analyzes captured images for quality issues (brightness, contrast, sharpness, noise, color balance, backlighting), suggests hardware setting adjustments tailored to the specific camera, and applies Image Processing Toolbox enhancement functions. Use when a user wants to improve camera image quality, troubleshoot dark/blurry/noisy/grainy/ overexposed/washed out/color cast images, or optimize camera settings.

Biblioteca Python para trabalhar com arquivos DICOM (Digital Imaging and Communications in Medicine). Use essa skill ao ler, escrever ou modificar dados de imagens médicas em formato DICOM, extrair dados de pixel de imagens médicas (TC, RM, Raio-X, ultrassom), anonimizar arquivos DICOM, trabalhar com metadados e tags DICOM, converter imagens DICOM para outros formatos, processar dados DICOM comprimidos ou processar conjuntos de dados de imagens médicas. Aplica-se a tarefas envolvendo análise de imagens médicas, sistemas PACS, fluxos de trabalho de radiologia e aplicações de imagem médica.

Stitched Image Quality Control

Kit de ferramentas de patologia computacional para análise de imagens de lâminas inteiras (WSI) e dados de imagem multiparamétrica. Use esta habilidade ao trabalhar com lâminas de histopatologia, imagens coradas com H&E, imunofluorescência multiplex (CODEX, Vectra), proteômica espacial, detecção/segmentação de núcleos, construção de gráficos de tecido ou treinamento de modelos ML em dados de patologia. Suporta 160+ formatos de lâmina incluindo Aperio SVS, NDPI, DICOM, OME-TIFF para fluxos de trabalho de patologia digital.

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

Scaling Vision with Sparse Mixture of Experts

Mantis: Interleaved Multi-Image Instruction Tuning

> Use this skill when working with Brain Imaging Data Structure (BIDS) datasets: organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars, or creating BIDS derivatives.

Create medical coding guides for billing accuracy. TRIGGERS - Use when user needs help with coding-guide related tasks.

ImageNet: A Large-Scale Hierarchical Image Database

Extracts selected metadata from a DICOM file and flags standard-tag PHI presence. Not for anonymization or clinical use.

Use to structure and tighten an NEJM Original Article into terse IMRAD — short main text (~2700 words), limited references, claim-first results, and a sober discussion with explicit limitations and calibrated clinical implications. Enforces NEJM's plain, concise house style.

Converts a single CT DICOM series folder into a Hounsfield Unit NIfTI volume with affine metadata.

Segments abdominal organs from CT NIfTI volumes using the NV-Segment-CT VISTA3D model, producing label maps and structured evidence JSON.

Runs chest X-ray reasoning smoke tests using the NV-Reason-CXR-3B model via local inference or a public Hugging Face Space API.

Generates synthetic body MRI volumes using NVIDIA's NV-Generate-CTMR rflow-mr model. Wraps the upstream diffusion inference pipeline with config staging, output validation, and NIfTI volume summarization.