3-D Deep Learning : Lung Tumor Segmentation

Version 1.1 (2.02 MB) by Kei Otsuka
How to create and train a V-Net neural network and perform semantic segmentation of lung tumors from 3-D medical images


Updated 26 Nov 2019

View License

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

Cite As

Kei Otsuka (2023). 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. Retrieved .

MATLAB Release Compatibility
Created with R2019b
Compatible with R2019b
Platform Compatibility
Windows macOS Linux

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!
Version Published Release Notes

Added small changes to be compatible with 19b release.