Segmentation and Analysis

Medical image segmentation using deep learning, labeling app, or image processing algorithms, and radiomics analysis

Image segmentation is the process of partitioning an image into regions. Semantic segmentation associates each pixel or voxel in an image with a class label that describes the meaning of an image region, such as bone, tumor, or background. You can perform medical image semantic segmentation using deep learning, the interactive Medical Image Labeler app, or image processing algorithms. Deep learning workflows require Deep Learning Toolbox™ and Computer Vision Toolbox™. Analyze segmented images using radiomics, which calculates standardized image features related to shape, intensity, and texture.

Apps

Medical Image Labeler

Interactively explore, label, and publish animations of 2-D or 3-D medical image data (Since R2022b)

Functions

expand all

Segmentation Algorithms

`imbinarize`	Binarize 2-D grayscale image or 3-D volume by thresholding
`graythresh`	Global image threshold using Otsu's method
`multithresh`	Multilevel image thresholds using Otsu’s method
`otsuthresh`	Global histogram threshold using Otsu's method
`adaptthresh`	Adaptive image threshold using local first-order statistics
`grayconnected`	Select contiguous image region with similar gray values using flood-fill technique
`watershed`	Watershed transform
`activecontour`	Segment image into foreground and background using active contours (snakes) region growing technique
`imsegfmm`	Binary image segmentation using fast marching method
`gradientweight`	Calculate weights for image pixels based on image gradient
`graydiffweight`	Calculate weights for image pixels based on grayscale intensity difference
`imsegkmeans`	K-means clustering based image segmentation
`imsegkmeans3`	K-means clustering based volume segmentation
`superpixels`	2-D superpixel oversegmentation of images
`superpixels3`	3-D superpixel oversegmentation of 3-D image

Deep Learning

Load and Prepare Training Data

`groundTruthMedical`	Ground truth label data for medical images (Since R2022b)
`changeFilePaths`	Change file paths in ground truth data for medical images (Since R2022b)
`merge`	Merge two or more `groundTruthMedical` objects (Since R2022b)
`imageDatastore`	Datastore for image data
`pixelLabelDatastore`	Datastore for pixel label data
`randomPatchExtractionDatastore`	Datastore for extracting random 2-D or 3-D random patches from images or pixel label images
`combine`	Combine data from multiple datastores
`transform`	Transform datastore

Import Network

`importNetworkFromTensorFlow`	Import TensorFlow network as MATLAB network (Since R2023b)
`importNetworkFromONNX`	Import ONNX network as MATLAB network (Since R2023b)

Design Networks

`fcnLayers`	(To be removed) Create fully convolutional network layer graph for semantic segmentation
`segnetLayers`	(To be removed) Create SegNet layer graph for semantic segmentation
`unet3dLayers`	(To be removed) Create 3-D U-Net layers for semantic segmentation of volumetric images (Since R2019b)
`unetLayers`	(To be removed) Create U-Net layers for semantic segmentation
`pixelClassificationLayer`	(To be removed) Create pixel classification layer for semantic segmentation
`dicePixelClassificationLayer`	(To be removed) Create pixel classification layer using generalized Dice loss for semantic segmentation (Since R2019b)

Segment Images

semanticseg Semantic image segmentation using deep learning

Visualize Segmentation Results

`labeloverlay`	Overlay label matrix regions on 2-D image
`volshow`	Display volume

Evaluate Segmentation Accuracy

`jaccard`	Jaccard similarity coefficient for image segmentation
`dice`	Sørensen-Dice similarity coefficient for image segmentation
`bfscore`	Contour matching score for image segmentation

Radiomics

`radiomics`	Prepare data and ROI for radiomics feature extraction (Since R2023b)
`shapeFeatures`	Radiomics shape features (Since R2023b)
`intensityFeatures`	Radiomics intensity features (Since R2023b)
`textureFeatures`	Radiomics texture features (Since R2023b)

Topics

Segmentation Using Deep Learning

Get Started with Image Preprocessing and Augmentation for Deep Learning
Preprocess data for deep learning applications with deterministic operations such as resizing, or augment training data with randomized operations such as random cropping.
Create Datastores for Medical Image Semantic Segmentation

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
- Create Training Data for 3-D Medical Image Semantic Segmentation
Datastores for Deep Learning (Deep Learning Toolbox)
Learn how to use datastores in deep learning applications.
List of Deep Learning Layers (Deep Learning Toolbox)
Discover all the deep learning layers in MATLAB^®.

Radiomics Analysis

Get Started with Radiomics
Learn about the concepts and uses of radiomics.
IBSI Standard and Radiomics Function Feature Correspondences
Discover the IBSI standards, and their MATLAB correspondences, for radiomics features.

Related Information

Image Segmentation

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

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

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 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.

Classify Breast Tumors from Ultrasound Images Using Radiomics Features

Use radiomics features to classify breast tumors as benign or malignant from breast ultrasound images.

Open Live Script

Compute Functional Connectivity from Brain fMRI

Compute resting state networks from resting-state fMRI (R-fMRI) using seed-based cross-correlation with seeds labeled in the Medical Image Labeler app.

Open Live Script

Analyze Vertebral Bone Density in Segmented CT Scan

Perform quantitative CT analysis to measure bone volume and bone mineral density within a vertebra.

Open Live Script

Estimate Lung Displacement Field During Breathing Using Deformable Image Registration

Uses deformable image registration to estimate the displacement field between two CT scans acquired during inhalation and exhalation for the same patient.

Open Live Script