Success when

The user can set either val_percentage - which will lead to the division of the dataset to training and validation fold or supply k which will lead to k-fold cross-validation. In the latter option mean, threshold, and standard deviation of the ensemble will be returned as the final output of the model

Success when

Given the configuration struct supplied by the user the supplied augmentations will be executed with some defined probability after loading the image: Brightness transform, Contrast augmentation transform, Gamma Transform, Gaussian noise transform, Rician noise transform, Mirror transform, Scale transform, Gaussian blur transform, Simulate low-resolution transform, Elastic deformation transform

Success when
Probabilities and hyperparameters of all augmentations, thresholds for binarization of output channels chosen spacing for preprocessing, number and settings of test time augmentations should be available in a hyperparam struct that is the additional argument of the pipeline function and that can be used for hyperparameter tuning

1. Introduction to Medical Imaging Theory and Spatial Metadata
   Provide an introductory overview of medical imaging formats and spatial metadata.
   Explain the fundamental concepts and principles underlying medical imaging.
2. Give a theoretical overview of functionalities under development, excluding usage examples.
3. Describe various image transformation functions, including:
   Brightness transform
   Contrast augmentation transform
   Gamma Transform
   Gaussian noise transform
   Rician noise transform
   Mirror transform
   Scale transform
   Gaussian blur transform
   Simulate low-resolution transform
   Elastic deformation transform
   4.Explain the concept of K-fold cross-validation and its relevance in model evaluation.
4. Discuss the concept of probabilistic oversampling and its application in image segmentation tasks.
   Explain how probabilistic oversampling techniques can enhance the robustness of segmentation models.
5. Describe the importance of standardizing image spacing, origin, and orientation.
6. Explain Largest Component Analysis utility in identifying and analyzing the largest connected components within datasets.
7. Give a brief introduction to hyperparameter tuning techniques.
   Explain the usage of the Hyperopt package for automated hyperparameter optimization.

During the validation epoch the images can be saved into persistent storage and a single random image loaded together with the output mask into MedEye3d for visualization during training

Success when
Enable some transformation to be executed on the model input, then inverse this transform on the model output; execute model inference n times when n is supplied by the user and return mean and standard deviation of segmentation masks produced by the model as the output

Success when

Given a model output and a threshold that will be used for each channel of the output to binarize the output user will have an option to retrieve only n largest components from binarized algorithm output

Success when
given the list of paths to medical images it will load them calculate the mean or median spacing (option selected by the user), and return it. Then during pipeline execution, all images should be resampled to a user-supplied spacing and user-supplied orientation

Success when

given the size of the 3D patch by the user algorithm after data loading will crop or pad the supplied image to meet the set size criterion. The part of the image where the label is present should be selected more frequently than the areas without during cropping, the probability that the area with some label indicated on segmentation mas will be chosen will equal p (0-1) where p is supplied by the user