scientific-medical-imaging

Medical imaging analysis automates DICOM preprocessing and radiomics feature extraction through an end-to-end pipeline that ingests data, applies modular preprocessing, and uses PyRadiomics to generate reproducible reports.

Whole Slide Images (WSI) for pathology research can be processed using MONAI and PathML, supporting WSI ingestion, patch-based tissue analysis, and deep learning segmentation workflows for clinical pathology evaluation.

The medical imaging pipeline handles DICOM metadata and anonymization during data ingestion and preprocessing, enforcing robust metadata handling to ensure reproducible reporting for clinical evaluation scenarios.

Segmenting lesions in a CT brain scan set is best achieved using integrated deep learning models like U-Net or Swin UNETR, automating ingestion, robust segmentation, and radiomic feature extraction for comprehensive organ and lesion analysis.

NIfTI data is converted into a structured radiomics report by ingesting it into the medical imaging pipeline, running preprocessing, applying segmentation, and executing feature extraction to compile a final reproducible report.

3D Slicer is not required to analyze NIfTI and DICOM medical images, as the pipeline interfaces directly with MONAI, PathML, and OpenSlide, though it provides optional interfaces to integrate with 3D Slicer for streamlined medical image analysis tasks.