Medical Imaging Toolbox


Medical Imaging Toolbox

Visualize, register, segment, and label 2D and 3D medical images

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 image data and metadata from specialized medical file formats, such as DICOM, NIfTI, and NRRD, that store data describing the patient, imaging procedure, and spatial referencing.

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, 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 multimodal medical images, volumes, or surfaces using image registration to align them to a common coordinate system.


Segment 2D images or 3D volumes into regions such as bones, tumors, or organs using traditional or deep learning techniques, and evaluate the accuracy of the regions.


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

“Diagnosis of Thyroid Nodules from Medical Ultrasound Images with Deep Learning ”

By Eunjung Lee, School of Mathematics and Computing (CSE), Yonsei University

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