Medical Imaging

Extend deep learning workflows for medical image processing applications

Apply deep learning to medical imaging 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 (Since R2022b)

Functions

`medicalSegmentAnythingModel`	Pretrained Medical Segment Anything Model (MedSAM) for medical image segmentation (Since R2024b)
`extractEmbeddings`	Extract image embeddings from Medical Segment Anything Model (MedSAM) encoder (Since R2024b)
`segmentObjectsFromEmbeddings`	Segment objects in medical image using Medical Segment Anything Model (MedSAM) image embeddings (Since R2024b)
`cellpose`	Configure Cellpose model for cell segmentation (Since R2023b)
`segmentCells2D`	Segment 2-D image using Cellpose (Since R2023b)
`segmentCells3D`	Segment 3-D image volume using Cellpose (Since R2023b)

Topics

Get Started with Medical Image Labeler (Medical Imaging Toolbox)
Interactively explore, label, and publish animations of 2-D or 3-D medical image data.
Get Started with MONAI Label in Medical Image Labeler (Medical Imaging Toolbox)
Apply AI models from the MONAI Label library for 3-D medical image segmentation.
Get Started with Medical Segment Anything Model for Medical Image Segmentation (Medical Imaging Toolbox)
Perform interactive medical image segmentation using Medical Segment Anything Model (MedSAM) and deep learning. (Since R2024b)
Getting Started with Cellpose (Medical Imaging Toolbox)
Segment cells from microscopy images using a pretrained Cellpose model, or train a custom model.
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

New

Interactively Segment and Analyze ROI Using MedSAM and Radiomics

Perform interactive segmentation of an object in a region of interest (ROI) of an image using the Medical Segment Anything Model (MedSAM) and analyze the segmented object using radiomics.

(Medical Imaging Toolbox)

Since R2024b

New

Segment and Analyze Brain MRI Scan Using AI

Preprocess, label, postprocess, and analyze a brain MRI image using MONAI Label.

(Medical Imaging Toolbox)

Since R2024a

Cardiac Left Ventricle Segmentation from Cine-MRI Images Using U-Net Network

Segment 2-D cardiac MRI images using U-Net, and explore predictions using Grad-CAM explainability maps.

Open Live Script

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

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

Segment a brain MRI using a deep neural network.

Open Live Script

Breast Tumor Segmentation from Ultrasound Using Deep Learning

Perform semantic segmentation of breast tumors from 2-D ultrasound images using a deep neural network.

Open Live Script

Segment CT Scan Using MONAI Label

Apply deep learning models from the Medical Open Network for AI (MONAI) Label platform to segment a CT scan.

Open Live Script

3-D Brain Tumor Segmentation Using Deep Learning

Perform semantic segmentation of brain tumors from 3-D medical images.

Open Live Script

Unsupervised Medical Image Denoising Using CycleGAN

Generate high-quality high-dose computed tomography (CT) images from noisy low-dose CT images using a CycleGAN neural network.

Open Live Script

Unsupervised Medical Image Denoising Using UNIT

Generate high-quality computed tomography (CT) images from noisy low-dose CT images using a UNIT neural network.

Open Live Script

Choose Pretrained Cellpose Model for Cell Segmentation

Segment cells from microscopy images using a pretrained Cellpose model.

Open Live Script

Refine Cellpose Segmentation by Tuning Model Parameters

Use options in the segmentCells2D function to improve the segmentation results from a Cellpose model. To learn how to select a suitable model, see Choose Pretrained Cellpose Model for Cell Segmentation.

Open Live Script

Detect Nuclei in Large Whole Slide Images Using Cellpose

Detect cell nuclei in whole slide images (WSIs) of tissue stained using hematoxylin and eosin (H&E) by using Cellpose.

Open Live Script