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) |
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)
- 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. (Da R2025a)
- 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)
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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.
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 (Medical Imaging Toolbox)] 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).
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.
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.
3-D Brain Tumor Segmentation Using Deep Learning
Perform semantic segmentation of brain tumors from 3-D medical images.
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.
Choose Pretrained Cellpose Model for Cell Segmentation
Segment cells from microscopy images using a pretrained Cellpose model.
Refine Cellpose Segmentation by Tuning Model Parameters
Explore and tune Cellpose parameters to improve segmentation results.
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.
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.
