医学成像

为医学图像处理应用扩展深度学习工作流

通过结合使用 Deep Learning Toolbox™ 和 Medical Imaging Toolbox™，将深度学习应用于医学成像应用。

App

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

函数

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

主题

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. (自 R2024b 起)
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.
深度学习层列表
探索 MATLAB^® 中的所有深度学习层。

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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)

自 R2024b 起

全新

Segment and Analyze Brain MRI Scan Using AI

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

(Medical Imaging Toolbox)

自 R2024a 起

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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使用深度学习进行三维脑肿瘤分割

此示例说明如何基于三维医学图像执行脑肿瘤的语义分割。

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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).

打开实时脚本

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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医学成像

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