医用画像

医用画像処理アプリケーション用の深層学習のワークフローの拡張

Deep Learning Toolbox™ を Medical Imaging Toolbox™ と共に使用して、医用画像アプリケーションに深層学習を適用します。

アプリ

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

関数

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

トピック

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)
深層学習用のデータストア
深層学習アプリケーションでデータストアを使用する方法を学びます。
深層学習層の一覧
MATLAB^® のすべての深層学習層を確認できます。

注目の例

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 セマンティックセグメンテーション (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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深層学習を使用した脳腫瘍の 3 次元セグメンテーション

この例では、3 次元医用画像から脳腫瘍のセマンティックセグメンテーションを実行する方法を説明します。

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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 hematoxylin and eosin (H&E) by using Cellpose.

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