Detect Brain Tumors: MRI Image Segmentation with U-Net in TensorFlow

Table of Contents

1. Introduction
2. Understanding MRI Image Segmentation
3. Image Specifications and Voxel Concept
4. The Unit Architecture and Dissimilarity Metric
5. Implementing MRI Image Segmentation in Python
6. Visualizing MRI Images and Labeling
7. Preprocessing and Generating Sub-volumes
8. Normalization and Standardization of Data
9. Creating the Unit Model Architecture
10. Training on Large Data Sets
11. Evaluation Metrics: Dice Similarity Coefficient
12. Sensitivity and Specificity
13. Patch-level Prediction and Results
14. Running on Entire Scans
15. Conclusion
16. Additional Resources

Introduction

Deep learning with TensorFlow is a powerful tool for implementing and understanding various deep learning algorithms and their applications. This video series aims to provide a quick explanation of the logic and implementation of deep learning algorithms using TensorFlow. In this particular video, we will focus on MRI image segmentation using the U-Net architecture and a dissimilarity metric.

Understanding MRI Image Segmentation

MRI images are three-dimensional images known as volumes, composed of voxels. In segmentation tasks, we classify each pixel in the image. While simple classification involves predicting a single output, segmentation requires predicting an output per pixel, which makes it more challenging. MRI images contain information about brain structures and various sequences such as flare, T1 weighted, T2 weighted, etc.

Image Specifications and Voxel Concept

MRI images have Dimensions of Height, width, and channels, where channels represent different color components. For black and white images, the channel value is one, while colorful images have three channels representing red, green, and blue intensities. MRI images, being three-dimensional, also have an additional dimension for different sequences. Each voxel in the image represents a point in the 3D volume.

The Unit Architecture and Dissimilarity Metric

The U-Net architecture is commonly used for image segmentation tasks. It involves a downward path of convolutional layers followed by pooling, and then an upward path where layers are upscaled and concatenated. This architecture allows for information sharing between different levels. The dissimilarity metric used in this segmentation task is the Dice Similarity Coefficient, which measures the similarity between predicted and actual segmentations.

Implementing MRI Image Segmentation in Python

In this section, we will jump into coding and explore the steps involved in implementing MRI image segmentation using Python and TensorFlow. We will load the MRI dataset, Visualize the images, preprocess the data, and create sub-volumes for training. The U-Net model will be defined and compiled using the TensorFlow library.

Visualizing MRI Images and Labeling

To gain a better understanding of MRI images, we will visualize different sections of the brain, such as sagittal, coronal, and transverse planes. These sections give us insights into the structure and presence of tumors. We will also label the images to distinguish regions containing edema, non-enhancing tumor, and enhancing tumor.

Preprocessing and Generating Sub-volumes

Preparing the data for training involves preprocessing steps such as normalization and standardization. We will normalize the voxel intensity values and generate random sub-volumes for efficient computation. This helps in avoiding resource exhaustion and allows for effective training.

Creating the Unit Model Architecture

The U-Net architecture will be implemented in Python using TensorFlow. We will define the convolutional and deconvolutional blocks, as well as the entire model architecture. The model will be compiled and saved for training and evaluation.

Training on Large Data Sets

Training the U-Net model on large data sets can be computationally expensive, requiring significant memory resources. To overcome this limitation, we will implement a data generator that randomly samples sub-volumes from the data set. This data generator will efficiently feed the model during training, preventing memory constraints.

Evaluation Metrics: Dice Similarity Coefficient

The Dice Similarity Coefficient (DSC) is an evaluation metric used for comparing the similarity between predicted and actual segmentations. We will implement the DSC and calculate it for each class, including edema, non-enhancing tumor, and enhancing tumor. The DSC values will provide insights into the accuracy of our segmentation model.

Sensitivity and Specificity

In addition to the DSC, we will calculate the sensitivity and specificity of our model. Sensitivity measures the true positive rate, while specificity measures the true negative rate. These metrics help evaluate the performance of the model in correctly identifying regions of interest.

Patch-level Prediction and Results

To assess the performance of our model, we will predict segmentations at the patch level. We will normalize the data and make predictions for different classes. By visualizing the predicted segmentations alongside the ground truth, we can analyze the accuracy and effectiveness of the model.

Running on Entire Scans

Finally, we will apply our model to predict segmentations for entire MRI scans. By visualizing the predicted segmentations in different sections of the brain, such as sagittal, coronal, and transverse planes, we can evaluate the overall performance and identify any areas for improvement.

Conclusion

In this video, we have explored the implementation of MRI image segmentation using the U-Net architecture and a dissimilarity metric in Python with TensorFlow. We discussed the concepts of MRI images, voxels, and the unit model architecture. By applying the discussed techniques, we can accurately segment brain images and identify regions of interest, such as edema and tumors. The evaluation metrics, such as the DSC, sensitivity, and specificity, provide insights into the model's performance. With further optimization and tuning of hyperparameters, we can improve the accuracy of the model in segmenting MRI images.

Additional Resources

• TensorFlow Documentation
• U-Net: Convolutional Networks for Biomedical Image Segmentation