Medical Image Processing Pipeline

What this diagram shows

This diagram represents the sequential flow of medical image data from acquisition through clinical interpretation. Raw imaging data (CT, MRI, X-ray, ultrasound) enters a preprocessing stage where noise reduction, registration, and normalisation occur. The processed images then pass through segmentation and feature extraction, often enhanced by machine learning models trained on annotated datasets. Computer-aided detection or diagnosis algorithms flag regions of interest, which radiologists review alongside probability scores and confidence metrics. Quality assurance checkpoints validate outputs before final archival in PACS systems. The pipeline may loop back if quality thresholds fail or if manual annotation is required for model retraining.

Key components

• Image Acquisition — Captures raw medical imaging data from modalities such as CT, MRI, X-ray, or ultrasound and transmits DICOM files to the pipeline.
• Preprocessing — Applies noise reduction, bias field correction, image registration to atlas templates, and intensity normalisation to standardise input data.
• Segmentation — Automatically delineates anatomical structures or pathological regions using thresholding, region growing, or deep learning models such as U-Net.
• Feature Extraction — Computes quantitative descriptors (texture, shape, intensity statistics) and radiomic features to characterise detected regions.
• AI Model Inference — Applies trained neural networks or classical machine learning classifiers to predict diagnosis, risk scores, or anomaly flags with confidence levels.
• Radiologist Review — Examines algorithm outputs, probability heatmaps, and segmentation overlays to validate findings and make final clinical decisions.
• PACS Export — Archives processed results, structured reports, and metadata into the Picture Archiving and Communication System for clinical workflow integration.

When to use it

Use this diagram when architecting or documenting diagnostic imaging workflows that combine automated image analysis with human expert review. It is essential for radiology departments implementing AI-assisted detection systems, research projects developing computer-aided diagnosis tools, and quality assurance processes for medical imaging software. It clarifies where human oversight gates automated decisions and how feedback loops improve model performance over time.

Common mistakes

• Omitting the radiologist review stage, which creates a false impression that AI output directly drives clinical decisions without human validation.
• Treating segmentation and feature extraction as a single black box rather than showing how anatomical delineation precedes quantitative measurement.
• Failing to include feedback loops from manual annotation or quality failures back to model retraining, which obscures how pipelines improve iteratively.

Adapting it to your system

Replace the acquisition modalities and anatomical structures with those relevant to your institution (cardiac, neuro, oncology). Substitute specific AI frameworks (TensorFlow, PyTorch, commercial products like Aidoc or Zebra Medical Vision) for the generic inference block. Adjust preprocessing steps to match your scanner protocols and image quality standards. Add integration points for your actual PACS vendor (Philips, GE, Siemens) and add domain-specific QA metrics such as sensitivity, specificity, or clinician acceptance rates at the review gate.