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
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. - 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®.