Medical Imaging Toolbox
Visualize, register, segment, and label 2D and 3D medical images
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Medical Imaging Toolbox provides apps, functions, and workflows for designing and testing diagnostic imaging applications. You can perform 3D rendering and visualization, multimodal registration, and segmentation and labeling of radiology images. The toolbox also lets you train predefined deep learning networks (with Deep Learning Toolbox).
You can import, preprocess, and analyze radiology images from various imaging modalities, including projected X-ray imaging, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine (PET, SPECT). The Medical Image Labeler app lets you semi-automate 2D and 3D labeling for use in AI workflows. You can perform multimodal registration of medical images, including 2D images, 3D surfaces, and 3D volumes. The toolbox provides an integrated environment for end-to-end computer-aided diagnosis and medical image analysis.
Capabilities, Documentation, and Examples
Medical imaging is a field of medicine that includes various techniques to image, visualize, and analyze the interior of humans and animals. This enables physicians to visualize organs, bones, cells, and various physiological processes and diagnose, monitor, and treat medical conditions. Images are generated using various radiological modalities such as X-rays, ultrasound, CT, MRI and nuclear imaging, and using microscopes for pathology.
Importing Medical Imaging Data
Read and modify medical image data and metadata from specialized medical file formats, such as DICOM, NIfTI, and NRRD, stored locally, in cloud locations, or on PACS servers.
Visualizing 2D Images and 3D Volumes
Use interactive tools to visualize 2D and 3D medical imaging data. Generate and render 3D surfaces and volumes.
Ground Truth Labeling
Use the Medical Image Labeler app to interactively label ground truth data, semi-automate or automate the labeling process, use custom algorithms or deep learning techniques such as MedSAM and MONAI Label, and export labeled data for AI workflows.
Preprocessing and Augmentation
Improve image quality using preprocessing techniques and improve the effectiveness of deep learning networks using augmentation to expand the training dataset.
Medical Image Registration
Compare and align multimodal medical images, volumes, or surfaces in a common coordinate system using the Medical Registration Estimator app and dedicated functions.
Segmentation
Segment 2D images or 3D volumes into regions such as bones, tumors, or organs using traditional, or deep learning techniques such as MedSAM, and evaluate the accuracy of the regions.
Analysis
Analyze medical imaging data using techniques such as radiomics and high level feature descriptors.
Interface for Cellpose Library
Segment cells from microscopy images using the Medical Imaging Toolbox Interface for Cellpose Library support package.
Interface for MONAI Library
Segment and label organs and bones in medical images using the Medical Imaging Toolbox Interface for MONAI Library support package integration into the Medical Image Labeler app.
Product Resources:
“With Medical Imaging Toolbox, we can load the entire data set and create the three-dimensional rendering with just a few clicks. Having this functionality and the capability to export data is important: It means we don’t start from scratch for each new design. We can rely on something we know works that is standard. This saves weeks for each new design.”
Medical Imaging Toolbox FAQs
Medical Imaging Toolbox provides apps, functions, and workflows in MATLAB for designing and testing diagnostic imaging applications, including 3D rendering, multimodal registration, and segmentation and labeling of radiology images.
The toolbox supports specialized medical file formats such as DICOM, NIfTI, NRRD, and Whole Slide Imaging formats which can be stored locally, in cloud locations, or on PACS servers.
The toolbox works with projected X-ray imaging, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), nuclear medicine (PET, SPECT) and Microscopy.
The Medical Image Labeler app is an interactive tool that lets you visualize, label, semi-automate or automate 2D and 3D labeling using custom algorithms or deep learning techniques such as MedSAM and MONAI Label, and export labeled data for AI workflows.
Yes, the toolbox supports multimodal registration of medical images, including 2D images, 3D surfaces, and 3D volumes, using the Medical Registration Estimator app and dedicated functions.
Yes, the toolbox lets you train predefined deep learning networks with Deep Learning Toolbox, including techniques such as MedSAM for segmentation and integration with MONAI Label.
Yes, you can segment cells from microscopy images using the Medical Imaging Toolbox Interface for Cellpose Library support package.
The toolbox enables analysis of medical imaging data using techniques such as radiomics and high-level feature descriptors for characterizing and classifying tissues or tumors.
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