Segmentation

Medical image segmentation using deep learning and image processing algorithms

Image segmentation partitions an image into regions. You can perform medical image segmentation using the Medical Segment Anything Model (MedSAM), other deep learning networks, the interactive Medical Image Labeler app, or image processing algorithms. Deep learning networks require Deep Learning Toolbox™ and Computer Vision Toolbox™.

Apps

Medical Image Labeler

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

Functions

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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

Segmentation Using Medical Segment Anything Model

`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)

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

`unet`	Create U-Net convolutional neural network for semantic segmentation (Since R2024a)
`unet3d`	Create 3-D U-Net convolutional neural network for semantic segmentation of volumetric images (Since R2024a)
`generalizedDice`	Generalized Sørensen-Dice similarity coefficient for image segmentation (Since R2021a)
`dlnetwork`	Deep learning neural network
`trainnet`	Train deep learning neural network (Since R2023b)

Segment Images

semanticseg Semantic image segmentation using deep learning

Visualize and Evaluation Results

`labeloverlay`	Overlay label matrix regions on 2-D image
`volshow`	Display volume
`jaccard`	Jaccard similarity coefficient for image segmentation
`dice`	Sørensen-Dice similarity coefficient for image segmentation
`bfscore`	Contour matching score for image segmentation

Topics

Medical Segment Anything Model

Get Started with Medical Segment Anything Model for Medical Image Segmentation
Perform interactive medical image segmentation using Medical Segment Anything Model (MedSAM) and deep learning. (Since R2024b)

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^®.

Related Information

Image Segmentation

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.

Since R2024b
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.