Get Started with Medical Imaging Toolbox

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.

Tutorials

• Read, Process, and Write 3-D Medical Images
  

  Import and display a medical image volume in patient coordinates, apply a smoothing filter.

• Display 3-D Medical Image Data in Patient Coordinate System
  

  Visualize volumetric CT data with anisotropic voxel dimensions in the patient coordinate system.

• Get Started with Medical Image Labeler
  

  Interactively label 2-D or 3-D medical image data.

About Medical Imaging

• Introduction to Medical Imaging
  

  Learn about common modalities and workflows used in medical image analysis.

• Medical Image Coordinate Systems
  

  Learn how image locations are defined in the intrinsic and patient coordinate systems.

Featured Examples

Label 3-D Medical Image Using Medical Image Labeler

Interactively label pixels in a 3-D medical image volume.

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Segment Lungs from CT Scan Using Pretrained Neural Network

Import a pretrained ONNX™ (Open Neural Network Exchange) 3-D U-Net [1] and use it to perform semantic segmentation of the left and right lungs from a 3-D chest CT scan. Semantic segmentation associates each voxel in a 3-D image with a class label. In this example, you classify each voxel in a test data set as belonging to the left lung or right lung. For more information about semantic segmentation, see Semantic Segmentation.

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Brain MRI Segmentation Using Pretrained 3-D U-Net Network

Segment brain MRI using a deep neural network.

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Create STL Surface Model of Femur Bone for 3-D Printing

Convert a segmentation mask from a CT image into an STL surface model suitable for 3-D printing.

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