3-D Deep Learning : Lung Tumor Segmentation

Versione 1.1 (2,02 MB) da Kei Otsuka
How to create and train a V-Net neural network and perform semantic segmentation of lung tumors from 3-D medical images
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Aggiornato 26 nov 2019

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Deep Learning is powerful approach to segment complex medical image.
This example shows how to create, train and evaluate a V-Net network to perform 3-D lung tumor segmentation from 3-D medical images. The steps to train the network include:
・Download and preprocess the training data.
・Create a randomPatchExtractionDatastore that feeds training data to the network.
・Define the layers of the V-Net network.
・Specify training options.
・Train the network using the trainNetwork function.

After training the V-Net network, the example performs semantic segmentation. The example evaluates the predicted segmentation by a visual comparison to the ground truth segmentation and by measuring the Dice similarity coefficient between the predicted and ground truth segmentation.

[Japanese] 医用画像処理において、Deep Learningは非常に強力なアプローチの一つです。


[Keyward] 画像処理・セグメンテーション・3次元・3-D・ディープラーニング・DeepLearning・デモ・IPCVデモ

Cita come

Kei Otsuka (2024). 3-D Deep Learning : Lung Tumor Segmentation (https://www.mathworks.com/matlabcentral/fileexchange/71521-3-d-deep-learning-lung-tumor-segmentation), MATLAB Central File Exchange. Recuperato .

Compatibilità della release di MATLAB
Creato con R2019b
Compatibile con R2019b
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Versione Pubblicato Note della release

Added small changes to be compatible with 19b release.