Fastai is a popular deep learning library in Python that provides high-level abstractions for training different types of neural networks. While Fastai is primarily known for its capabilities in traditional 2D computer vision tasks, like image classification and object detection, it also has support for working with 3D images.

In this blog post, we will explore how to use Fastai for 3D image processing tasks using Python. We will cover the following topics:

1. Loading and preprocessing 3D medical images
2. Building a 3D convolutional neural network (CNN) using Fastai
3. Training and evaluating the 3D CNN model

Loading and Preprocessing 3D Medical Images

Fastai provides convenient methods for loading different types of image data, including 3D medical images. To load a 3D image dataset, we can use the ImageDataLoaders.from_folder method and specify the appropriate arguments to indicate the folder containing the images. Additionally, we can apply transforms to the images using aug_transforms to augment the dataset.

Here’s an example of how to load and preprocess 3D medical images using Fastai:

from fastai.vision.all import *

path = Path("3d_images_dataset_folder")
dls = ImageDataLoaders.from_folder(path, valid_pct=0.2, 
                                   item_tfms=Resize(128),
                                   batch_tfms=[*aug_transforms(), Normalize.from_stats(*imagenet_stats)])

In the example above, we are loading the 3D image dataset from a folder specified by path. We also apply transformations to the images, including resizing them to 128x128 pixels, applying random flips and rotations, and normalizing the pixel values using ImageNet statistics.

Building a 3D Convolutional Neural Network (CNN) using Fastai

Fastai makes it easy to build deep learning models, including 3D CNNs. We can use the cnn_learner method to create a 3D CNN model and specify the architecture and the number of classes for classification.

learn = cnn_learner(dls, resnet34, metrics=accuracy)

In the example above, we are using a pre-trained ResNet34 model architecture for the 3D CNN. The metrics argument is used to specify the evaluation metric to track during training.

Training and Evaluating the 3D CNN Model

Training and evaluating a 3D CNN model in Fastai follows the same workflow as with 2D image datasets. We can use the fine_tune method to train the model with transfer learning. Additionally, we can use the lr_find method to find an appropriate learning rate for training.

learn.fine_tune(10, base_lr=1e-3, freeze_epochs=4)

Once the model is trained, we can evaluate its performance using the validation set.

interp = ClassificationInterpretation.from_learner(learn)
interp.plot_confusion_matrix()

The code above shows how to visualize the confusion matrix for the validation set to gain insights into the model’s performance.

Conclusion

Fastai provides a powerful and easy-to-use framework for 3D image processing tasks in Python. With its support for loading and preprocessing 3D medical images, building 3D CNN models, and training and evaluating these models, Fastai simplifies the process of working with 3D image data. Whether you are working on medical imaging tasks or any other 3D image processing problem, Fastai can be a valuable tool in your deep learning toolkit.

Give it a try and start exploring the possibilities of deep learning with 3D images using Fastai in Python!