medical-image-analysis-skills

Scan medical imaging directories and generate structured JSON or CSV manifests with file-level metadata for DICOM and NIfTI assets.

Validates DICOM series consistency by checking SeriesInstanceUID, ImageOrientationPatient, slice spacing uniformity, and detecting missing slices.

Normalizes medical image intensities using various methods (z-score, min-max, percentile, window-level). Essential preprocessing step for consistent analysis across different scanners and protocols.

Generate research-oriented HTML or Markdown case reports for volumetric medical images, including summary statistics and slice montages.

Resample NIfTI medical images to target spacing with configurable interpolation methods and optional anti-aliasing for downsampling.

Generates a complete MONAI-based training scaffold for medical image segmentation or classification tasks. Creates training scripts, inference scripts, configuration files, data loading pipelines, and model definitions.

Validates NIfTI file headers for integrity and consistency. Checks header structure, dimension consistency, orientation matrix, data type support, and file size expectations.

Converts NIfTI medical images to RAS+ (Right-Anterior-Superior) orientation. Ensures consistent coordinate system across all images in a dataset.

Validates segmentation masks in NIfTI format by checking label values, detecting empty masks, finding connected components, and identifying small spurious regions.

Prototype scaffold for sliding-window volume inference interfaces. This skill demonstrates window generation, batching, aggregation, and reporting, but it does not provide production-ready checkpoint-to-model loading.