Medical Image Segmentation Using SegNet

버전 1.0.0.2 (1.46 MB) 작성자: Kei Otsuka
How to create, train and evaluate SegNet for medical image segmentation
다운로드 수: 3.2K
업데이트 날짜: 2020/8/19

라이선스 보기

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

인용 양식

Kei Otsuka (2024). Medical Image Segmentation Using SegNet (https://www.mathworks.com/matlabcentral/fileexchange/66448-medical-image-segmentation-using-segnet), MATLAB Central File Exchange. 검색됨 .

MATLAB 릴리스 호환 정보
개발 환경: R2017b
R2017b에서 R2020a까지의 릴리스와 호환
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medImgSegNet

medImgSegNet

버전 게시됨 릴리스 정보
1.0.0.2

Fixed compatibility issue

1.0.0.1

updated to make it compatible with R2018b

1.0.0.0