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3D U-Net for Semantic Segmentation

DigitalSreeni via YouTube

Overview

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This course demonstrates multiclass semantic segmentation of 3D volumes using a 3D U-Net with TensorFlow and Keras. It covers data preparation, annotation, training, testing, and exporting volumetric and multichannel results for datasets such as CT, MRI, and FIB-SEM.

Syllabus

Introduction
Data types
Why 3D UNet
Unit Architecture
Python libraries
Annotation
Notebook
Running the code
Verify tensorflow and Keras
Import data
Local file location
Number of classes
Image dimensions
Multiclass classification
Learning rate
Preprocessing
Results
Testing
Saving
Multichannel
Basic segmentation
Final segmentation
OEM TIFF
Multichannel image
Summary

Taught by

DigitalSreeni

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