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MRI Image Segmentation with U-Net
Aug 11, 2024
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Deep Learning with TensorFlow: MRI Image Segmentation Lecture Notes
Introduction
Part of the Deep Learning with TensorFlow playlist.
Focus on logic and implementation of deep learning algorithms using TensorFlow.
Expectation that audience is familiar with deep learning algorithms.
Code files provided in the description for re-implementation.
MRI Image Segmentation with U-Net
Task
: Segment brain images to identify tumor regions using MRI images.
MRI Images
: 3D images represented as volumes (voxels).
Segmentation Basics
Segmentation
: Classifying each pixel in the image; more complex than standard classification (which gives a single output).
Segmentation outputs a separate label for each pixel.
MRI Image Specifications
Dimensions: Height, Width, Channels.
Black and white images: 1 channel (2D).
Color images: 3 channels (Red, Green, Blue).
MRI images contain 3D volume (X, Y, Z) with voxel information.
The fourth dimension contains different sequences (e.g., FLAIR, T1-weighted, T2-weighted).
Output Labels
For each voxel, output could be:
0: Background
1: Edema
2: Non-enhancing tumor
3: Enhancing tumor
Each voxel visualized with color representation (RGB).
Visualization
Visualization of 3D MRI images involves different planes:
Sagittal Plane
Coronal Plane
Transverse Plane
Generating Sub-Volumes
Instead of using complete MRI images (computationally expensive), use random sub-volumes for training.
Standardize data by making mean zero and standard deviation one.
U-Net architecture will be utilized for segmentation.
U-Net Architecture
The model consists of encoding (downsampling) and decoding (upsampling) layers.
Key processes include convolution, ReLU activation, and max pooling.
Information is shared between corresponding levels during upsampling.
Error Metrics
Dice Similarity Coefficient
: Measures similarity between predicted and actual values.
Calculation involves intersection of predicted and actual values.
Soft Dice Similarity Coefficient is used when outputs are probabilities.
Sensitivity and Specificity
: Important evaluation metrics for classification.
Sensitivity = True Positive / (True Positive + False Negative)
Specificity = True Negative / (True Negative + False Positive)
Implementation Steps
Import necessary libraries and dataset.
Explore dataset to understand shapes and labels.
Normalize data, create labeled images for different planes.
Standardize the input image data.
Define the U-Net model architecture and compile it.
Fit the model with training and validation datasets.
Evaluate model performance using sensitivity and specificity metrics.
Conclusion
Lecture covers the segmentation of MRI images to identify tumors using U-Net architecture and TensorFlow.
Coding and implementation details will be shown in a notebook demonstration.
Future content will include more detailed lectures and live coding sessions.
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