Medical Imaging Toolbox

 

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.
What Is Medical Imaging Toolbox?

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.

First frame of an echocardiogram ultrasound image series.

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.

Documentation | Examples

CT scan of a human chest.

Visualizing 2D Images and 3D Volumes

Use interactive tools to visualize 2D and 3D medical imaging data. Generate and render 3D surfaces and volumes.

Documentation | Examples

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.

Documentation | Examples

Low-dose original CT scan of human chest next to a denoised version of the same image.

Preprocessing and Augmentation

Improve image quality using preprocessing techniques and improve the effectiveness of deep learning networks using augmentation to expand the training dataset.

Documentation | Examples

Before and after deformable registration of two CT scan images taken at peak inhalation and exhalation during a breathing cycle.

Medical Image Registration

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

Documentation | Examples

Segmentation

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.

Documentation | Examples

Features selected to classify malignant tumors

Analysis

Analyze medical imaging data using techniques such as radiomics and high level feature descriptors.

Documentation | Examples

Cell nuclei of tissue stained using hemotoxylin and eosin (H&E) detected using Cellpose

Interface for Cellpose Library

Segment cells from microscopy images using the Medical Imaging Toolbox Interface for Cellpose Library support package

Documentation | Examples

Interface for MONAI Library

Segment and label organs and bones in medical images using the Medical Imaging Toolbox Interface for MONAI Library support package

Documentation | Examples

Product Resources:

Documentation Examples Videos Requirements Release notes
Diagnosis of Thyroid Nodules from Medical Ultrasound Images with Deep Learning

“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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