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Medical Image Segmentation Using SegNet

version 1.0.0.2 (1.46 MB) by Kei Otsuka
How to create, train and evaluate SegNet for medical image segmentation

2.4K Downloads

Updated 19 Aug 2020

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Deep Learning is powerful approach to segment complex medical image.
This demo shows how to prepare pixel label data for training, and how to create, train and evaluate VGG-16 based
SegNet to segment blood smear image into 3 classes – blood parasites, blood cells and background.
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。
本デモでは、ネットワーク学習のためのラベル画像の準備、SegNetの作成と学習、そして評価までの一連の流れをご紹介します。使用する画像は血液塗抹標本画像で、この画像をSegNetを用いて3クラス(赤血球、病原虫、背景)に分割します。

[Keyward] 画像処理・セグメンテーション・ディープラーニング・DeepLearning・デモ・IPCVデモ
・ニューラルネットワーク・医用画像

Cite As

Kei Otsuka (2021). Medical Image Segmentation Using SegNet (https://www.mathworks.com/matlabcentral/fileexchange/66448-medical-image-segmentation-using-segnet), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2017b
Compatible with R2017b to R2020a
Platform Compatibility
Windows macOS Linux

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medImgSegNet

medImgSegNet