All classes Brain Tumor Segmentation with U-Net Syllabus · Episode 2 of 6

Class Brain Tumor Segmentation with U-Net #2: MRI Preprocessing & Image + Mask Augmentation Simultaneously

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Tutorial on MRI preprocessing and segmentation data augmentation: z-score normalization, rotation, scaling, flip applied simultaneously to images and masks, along with PyTorch DataLoader. Episode 2 of the U-Net Brain Tumor series.

Software: Deep Learning (PyTorch U-Net)
Level: Intermediate
Duration: ± 11 min
Series total: 1 h 10 min · 6 videos
Published: October 24, 2026

Episode 2 of 6 · view syllabus

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Introduction

This is the final result, one brain MRI image is rotated, zoomed, shifted, and flipped randomly, and the yellow line tumor boundary moves precisely with the image. This is the correct augmentation for segmentation. Let's start. Hello, welcome to episode two of the brain tumor segmentation series with metaheuristic optimized units. Our data is ready to be loaded. Now we are preparing it for training. MRI intensity normalization, and data augmentation. There is one important rule in segmentation. Every geometric transformation applied to the image must be applied exactly the same to the mask.

Step 1: Image augmentation with 4 masks

Create a dataset file underscore brain dot py. First, the augmentation function. With a 50% chance, the image and mask are flipped horizontally together. Then we create one affine transformation matrix with random rotation plus or minus 15 degrees, scale 90-110%, and small shift. The same matrix is used for the image and mask. The image uses linear interpolation, while the mask uses nearest to avoid fractional values at the tumor edges. Finally, the image brightness is slightly changed, only the image, because brightness does not change the tumor location.

Step 2: Normalization, Dataset & DataLoader

Next, normalization. MRI intensity does not have a standard scale, it varies between machines and patients. Therefore, we use z-score per image, subtract the mean and divide by the standard deviation. The brain data class returns a 3-channel image tensor and a 1-channel mask, with augmentation only for the training data. The loader function creates a data loader for the training, validation, or test part. The portion parameter allows us to use part of the training data, which will be useful during metaheuristic optimization. Now create the preprocessing file underscore brain dot pye to see the results.

Step 3: Checking normalization

We select the slice with the widest tumor. Before normalization, pixel values range from 0 to 188. We then normalize to mean 0 and standard deviation 1.

Step 4: Visualization of augmentation

Next, we display the original image and 4 augmented results. Notice the bottom line, the yellow line tumor boundary always sticks precisely to the tumor area, even if the image is rotated, zoomed, shifted, or flipped. If the image and mask are augmented separately with different random numbers, the label becomes incorrect and the model learns the wrong things.

Step 5: Batch and number of data

Finally, we take one batch from the training loader. Its shape is 32 images, 3 channels, 96x96, and mask 1 channel. In this batch, tumor pixels are only about 1%. The number of augmentations is 2,680 for training, 575 for validation, and 674 for testing.

Done!

Done. We have created Z-score normalization, identical geometric augmentation for both images and masks, as well as PyTorch datasets and data loaders. In the next episode, we will build the UNET architecture from scratch, encoder, decoder, and skip connections. Currently working on a thesis, dissertation, or AI research in the field of medical image segmentation? Cerdas Inovasi Digital helps with deep learning implementation, from UNET segmentation, PSO and Grey Wolf metaheuristic optimization, up to DAIS, IOU evaluation, and result writing, as well as opening deep learning courses with PyTorch. Want to master the entire series? From MRI brain datasets to optimized UNET with metaheuristics? All 6 episodes, complete with PDF step-by-step guides and source code, are available for premium tutorial subscribers. Subscribe starting at Rp69.000 per month, save 28% with the 12-month package, or one-time payment of Rp999.000 for lifetime access. Want a single class only? Buy single classes with lifetime access. Subscribe now on the premium page cerdasinovasidigital.com Contact our WhatsApp, or visit cerdasinovasidigital.com Thank you for watching, see you in the next episode.

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Transcript

0:03Introduction

0:47Step 1: Image augmentation with 4 masks

2:55Step 2: Normalization, Dataset & DataLoader

5:02Step 3: Checking normalization

6:32Step 4: Visualization of augmentation

8:07Step 5: Batch and number of data

9:12Done!

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My notes

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Transcript

0:03Introduction

0:47Step 1: Image augmentation with 4 masks

2:55Step 2: Normalization, Dataset & DataLoader

5:02Step 3: Checking normalization

6:32Step 4: Visualization of augmentation

8:07Step 5: Batch and number of data

9:12Done!

Ask the class material

My notes

Questions & answers

Your instructor

Introduction

Step 1: Image augmentation with 4 masks

Step 2: Normalization, Dataset & DataLoader

Step 3: Checking normalization

Step 4: Visualization of augmentation

Step 5: Batch and number of data

Done!

Introduction

Step 1: Image augmentation with 4 masks

Step 2: Normalization, Dataset & DataLoader

Step 3: Checking normalization

Step 4: Visualization of augmentation

Step 5: Batch and number of data

Done!