의료 영상

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)

Segment and Analyze Brain MRI Scan Using AI

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

(Medical Imaging Toolbox)

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 의미론적 분할 (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.

(Medical Imaging Toolbox)

딥러닝을 사용한 3차원 뇌종양 분할

이 예제에서는 3차원 의료 영상에서 뇌종양의 의미론적 분할을 수행하는 방법을 다룹니다.

(Image Processing Toolbox)

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.

(Image Processing Toolbox)

Unsupervised Medical Image Denoising Using UNIT

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

(Image Processing Toolbox)

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 세포 분할에 사용할 사전 훈련된 Cellpose 모델 선택하기 (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.

Classify Tumors in Digital Pathology Images Using Cell Nucleus Morphology

Classify whole slide image (WSI) patches as normal or cancerous using pathomics.

(Medical Imaging Toolbox)

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.

(Medical Imaging Toolbox)