Medical Imaging

Extend deep learning workflows for medical image processing applications

Apply deep learning to automated driving applications by using Deep Learning Toolbox™ together with Medical Imaging Toolbox™.

Apps

Medical Image Labeler

Interactively explore, label, and publish animations of 2-D or 3-D medical image data

Topics

Create Datastores for Medical Image Semantic Segmentation (Medical Imaging Toolbox)

Create datastores that contain images and pixel label data from a groundTruthMedical object for training semantic segmentation deep learning networks.
- Convert Ultrasound Image Series into Training Data for 2-D Semantic Segmentation Network (Medical Imaging Toolbox)
- Create Training Data for 3-D Medical Image Semantic Segmentation (Medical Imaging Toolbox)
Datastores for Deep Learning
Learn how to use datastores in deep learning applications.
List of Deep Learning Layers
Discover all the deep learning layers in MATLAB^®.

Featured Examples

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.

Open Live Script