Getting Started

Installation

You can install the library from PyPi using pip install bio-volumentations.

Importing

Import the library to your project using import bio_volumentations as biovol.

How to Use the Library?

The Bio-Volumentations library processes 3D, 4D, and 5D images. Each image must be represented as numpy.ndarray and must conform to the following conventions:

  • The order of dimensions is [C, Z, Y, X, T], where C is the channel dimension, T is the time dimension, and Z, Y, and X are the spatial dimensions.

  • The three spatial dimensions (Z, Y, X) are compulsory.

  • The channel (C) dimension is optional. If it is not present, the library will automatically create a dummy dimension in its place and output an image of shape (1, Z, Y, X).

  • The time (T) dimension is optional and can only be present if the channel (C) dimension is also present.

Thus, the input images can have these shapes:

  • [Z, Y, X] (a single-channel volumetric image)

  • [C, Z, Y, X] (a multi-channel volumetric image)

  • [C, Z, Y, X, T] (a single-channel as well as multi-channel volumetric image sequence)

It is strongly recommended to use Compose to create and use transformations. The Compose class automatically checks and adjusts image format, datatype, stacks individual transforms to a pipeline, and outputs the image as a contiguous array. Optionally, it can also convert the transformed image to a desired format.

Below, there are several examples of how to use the Bio-Volumentations library.

Example: Transforming a Single Image

import numpy as np
from bio_volumentations import Compose, RandomGamma, RandomRotate90, GaussianBlur

# Create the transformation using Compose from a list of transformations
aug = Compose([
        RandomGamma(gamma_limit = (0.8, 1,2), p = 0.8),
        RandomRotate90(axes = [1, 2, 3], p = 1),
        GaussianBlur(sigma = 1.2, p = 0.8)
      ])

# Generate an image
img = np.random.rand(1, 128, 256, 256)

# Transform the image
# Notice that the image must be passed as a keyword argument to the transformation pipeline
# and extracted from the outputted dictionary.
data = {'image': img}
aug_data = aug(**data)
transformed_img = aug_data['image']

Example: Transforming a Image Pairs

Sometimes, it is necessary to consistently transform a tuple of corresponding images. To that end, Bio-Volumentations define several target types:

  • image for the image data

  • mask for integer-valued label images

  • float_mask for real-valued label images

The mask and float_mask target types are expected to have the same shape as the image target except for the channel (C) dimension which must not be included. For example, for images of shape (150, 300, 300), (1, 150, 300, 300), or (4, 150, 300, 300), the corresponding mask must be of shape (150, 300, 300). If one wants to use a multichannel mask or float_mask, one has to split it into a set of single-channel mask s or float_mask s, respectively, and input them as stand-alone targets (see below).

If a Random... transform receives multiple targets on its input in a single call, the same random numbers are used to transform all of these targets.

However, some transformations might behave slightly differently for the individual target types. For example, RandomCrop works in the same way for all target types, while RandomGaussianNoise only affects the image target and leaves the mask and float_mask targets unchanged. Please consult the documentation of respective transforms for more details.

import numpy as np
from bio_volumentations import Compose, RandomGamma, RandomRotate90, GaussianBlur

# Create the transformation using Compose from a list of transformations
aug = Compose([
        RandomGamma(gamma_limit = (0.8, 1,2), p = 0.8),
        RandomRotate90(axes = [1, 2, 3], p = 1),
        GaussianBlur(sigma = 1.2, p = 0.8)
      ])

# Generate image and a corresponding labeled image
img = np.random.rand(1, 128, 256, 256)
lbl = np.random.randint(0, 1, size=(128, 256, 256), dtype=np.uint8)

# Transform the images
# Notice that the images must be passed as keyword arguments to the transformation pipeline
# and extracted from the outputted dictionary.
data = {'image': img, 'mask': lbl}
aug_data = aug(**data)
transformed_img, transformed_lbl = aug_data['image'], aug_data['mask']

Example: Transforming Multiple Images of the Same Target Type

Although there are only three target types, one input arbitrary number of images to any transformation. To achieve this, one has to define the value of the targets argument when creating a Compose object.

The targets must be a list with 3 items: a list with names of image-type targets, a list with names of mask-type targets, and a list with names of float_mask-type targets. The specified names will then be used to input the images to the transformation call as well as during extracting the transformed images from the outputted dictionary. Please see the code below for a practical example.

import numpy as np
from bio_volumentations import Compose, RandomGamma, RandomRotate90, GaussianBlur

# Create the transformation using Compose from a list of transformations and define targets
aug = Compose([
        RandomGamma( gamma_limit = (0.8, 1,2), p = 0.8),
        RandomRotate90(axes = [1, 2, 3], p = 1),
        GaussianBlur(sigma = 1.2, p = 0.8)
    ],
    targets= [ ['image' , 'image1'] , ['mask'], ['float_mask'] ])

# Generate the image data
img = np.random.rand(1, 128, 256, 256)
img1 = np.random.rand(1, 128, 256, 256)
lbl = np.random.randint(0, 1, size=(128, 256, 256), dtype=np.uint8)

# Transform the images
# Notice that the images must be passed as keyword arguments to the transformation pipeline
# and extracted from the outputted dictionary.
data = {'image': img, 'image1': img1, 'mask': lbl}
aug_data = aug(**data)
transformed_img = aug_data['image']
transformed_img1 = aug_data['image1']
transformed_lbl = aug_data['mask']