Imagerie médicale

Enrichir des workflows de Deep Learning pour les applications de traitement d’images médicales

Appliquez le Deep Learning à des applications d’imagerie médicale en utilisant Deep Learning Toolbox™ avec Medical Imaging Toolbox™.

Applications

Medical Image Labeler

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

Fonctions

`cellpose`	Configure Cellpose model for cell segmentation (depuis R2023b)
`segmentCells2D`	Segment 2-D image using Cellpose (depuis R2023b)
`segmentCells3D`	Segment 3-D image volume using Cellpose (depuis R2023b)

Rubriques

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.
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^®.

Exemples présentés

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.

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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 (Computer Vision Toolbox).

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

Segment a brain MRI using a deep neural network.

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Breast Tumor Segmentation from Ultrasound Using Deep Learning

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

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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.

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3-D Brain Tumor Segmentation Using Deep Learning

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

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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.

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Unsupervised Medical Image Denoising Using UNIT

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

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Choose Pretrained Cellpose Model for Cell Segmentation

Segment cells from microscopy images using a pretrained Cellpose model.

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Refine Cellpose Segmentation by Tuning Model Parameters

Use options in the segmentCells2D (Medical Imaging Toolbox) 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 (Medical Imaging Toolbox).

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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.

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