医学成像
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
| 医学图像标注器 | 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)
- Get Started with MedSAM in Medical Image Labeler (Medical Imaging Toolbox)
This example shows how to interactively segment objects in medical images and in cross-sections of medical volumes using the MedSAM algorithm in the Medical Image Labeler app. (自 R2025a 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
groundTruthMedicalobject 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)
精选示例
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.
Segment and Analyze Brain MRI Scan Using AI
Preprocess, label, postprocess, and analyze a brain MRI image using MONAI Label.
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.
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。
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.
Unsupervised Medical Image Denoising Using UNIT
Generate high-quality computed tomography (CT) images from noisy low-dose CT images using a UNIT neural network.
Classify Tumors in Digital Pathology Images Using Cell Nucleus Morphology
Classify whole slide image (WSI) patches as normal or cancerous using pathomics.
Multilabel Diabetic Retinopathy Fundus Image Classification Using Deep Learning
Classify diabetic retinopathy (DR) fundus images using a ResNet-101 deep neural network with transfer learning.
