#KERAS DATA AUGMENTATION 3D CODE#

РІР‚СљAs for our tasks there is very little training data available, we use excessive data augmentation by applying elastic deformations to the available training images. When I first read this transformation in the original Unet paper, I didnРІР‚в„ўt understand a single word from the paragraph: Of course, any other kind of intensity normalization may apply in medical images. There is no point to visualize this transformation as its purpose is to feed the preprocessed data into the deep learning model. This augmentation usually helps the model to learn scale-invariant features. We will randomly zoom in and out of the image. Rescaling can be regarded as an affine transformation. It can be used to bring different images to have the same or similar voxel size. However, you may choose to include it in a previous step in your pipeline. For example to create batches with dataloaders the dimension should be consistent across instances. Honestly, I wouldn't recommend it alone since the resulting images might not have the same shape. It works with nifti files and not with numpy arrays.

Nibabel provides a function called resample_to_output(). Instead of providing the desired output shape, you specify the desired voxel size(i.e. Note that there is another type of resizing. It is very common to downsample the image in a lower dimension for heavy machine learning. Honestly, I am not a big fan of the scipy's terminology to use the word zoom for this functionality.Р”Вownsampled and upsampled image by a factor of 2 This kind of scaling is usually called isometric. Result2 = resize_data_volume_by_scale ( epi_img_numpy, 2 ) LetРІР‚в„ўs write some minimal function to do so: Throughout the whole tutorial, we will extensively use a function that visualizes the three median slices in the sagittal, coronal, and axial planes respectively. The images will be shown in 3 planes: sagittal, coronal, axial looking from left to right throughout this post.

#KERAS DATA AUGMENTATION 3D CODE#

But before that, letРІР‚в„ўs write up some code to visualize the 3D medical volumes. If you want to focus on medical image analysis with deep learning, I highly recommend starting from the Pytorch-based Udemy Course.Р”Вata: We will play with 2 MRI images that are provided from nibabel (python library) for illustration purposes. To dive deeper into how AI is used in Medicine, you canРІР‚в„ўt go wrong with the AI for Medicine online course, offered by Coursera. It performs transformations on medical images, which is simply a 3D structured grid. To this end, I provide a notebook for everyone to play around. In the field of medical imaging, I find some data manipulations, which are heavily used in preprocessing and augmentation in state-of-the-art methods, to be critical in our understanding. As I always say, if you merely understand your data and their particularities, you are probably playing bingo. My experience in the field leads me to continue with data understanding, preprocessing, and some augmentations. We have already discussed medical image segmentation and some initial background on coordinate systems and DICOM files. Why does such functionality not exist? So, I made up this post (plus a notebook) for discouraged individuals who, like me, are interested in solving medical imaging problems. When I realized that I cannot apply common image processing pipelines in medical images, I was completely discouraged.

Keras data augmentation 3d