Imaging medico
Applicare il Deep Learning alle applicazioni di imaging medico utilizzando Deep Learning Toolbox™ insieme a Medical Imaging Toolbox™.
App
| Medical Image Labeler | Interactively explore, label, and publish animations of 2-D or 3-D medical image data (Da R2022b) |
Funzioni
medicalSegmentAnythingModel | Pretrained Medical Segment Anything Model (MedSAM) for medical image segmentation (Da R2024b) |
extractEmbeddings | Extract image embeddings from Medical Segment Anything Model (MedSAM) encoder (Da R2024b) |
segmentObjectsFromEmbeddings | Segment objects in medical image using Medical Segment Anything Model (MedSAM) image embeddings (Da R2024b) |
cellpose | Configure Cellpose model for cell segmentation (Da R2023b) |
segmentCells2D | Segment 2-D image using Cellpose (Da R2023b) |
segmentCells3D | Segment 3-D image volume using Cellpose (Da R2023b) |
Argomenti
- 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. (Da R2024b)
- 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)
- 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®.
Esempi in primo piano
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.
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.
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 Segmentazione semantica (Computer Vision Toolbox).
Brain MRI Segmentation Using Pretrained 3-D U-Net Network
Segment a brain MRI using a deep neural network.
Breast Tumor Segmentation from Ultrasound Using Deep Learning
Perform semantic segmentation of breast tumors from 2-D ultrasound images using a deep neural network.
3-D Brain Tumor Segmentation Using Deep Learning
Perform semantic segmentation of brain tumors from 3-D medical images.
Choose Pretrained Cellpose Model for Cell Segmentation
Segment cells from microscopy images using a pretrained Cellpose model.
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).
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.
