의료 영상

의료 영상 처리 응용 분야의 딥러닝 워크플로 확장

Deep Learning Toolbox™를 Medical Imaging Toolbox™와 함께 사용하여 의료 영상 응용 분야에 딥러닝을 적용합니다.

앱

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

함수

`cellpose`	Configure Cellpose model for cell segmentation (R2023b 이후)
`segmentCells2D`	Segment 2-D image using Cellpose (R2023b 이후)
`segmentCells3D`	Segment 3-D image volume using Cellpose (R2023b 이후)

도움말 항목

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.
딥러닝 계층 목록
MATLAB^®에서 제공하는 딥러닝 계층에 대해 알아봅니다.

추천 예제

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).

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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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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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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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딥러닝을 사용한 3차원 뇌종양 분할

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

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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).

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Detect Nuclei in Large Whole Slide Images Using Cellpose

Detect cell nuclei in whole slide images (WSIs) of tissue stained using hemotoxylin and eosin (H&E) by using Cellpose.

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