anatomy-datasets

Your task is to create a Python-based mini-application that leverages the 'anatomy-datasets' package to facilitate the preparation of datasets for 2D anatomical segmentation tasks. This application will serve as a tool for researchers and medical professionals who need to preprocess and load datasets efficiently. Here’s a detailed plan on how to approach this project:

1. **Project Overview**: Develop a utility named 'AnatomicalSegmentationPrepTool' that simplifies the process of preparing datasets for 2D anatomical segmentation. The tool should support loading, preprocessing, and saving datasets in various formats.

2. **Core Features**:
   - **Dataset Loading**: Implement functionality to load datasets from common file formats such as PNG, JPG, and NIFTI. Utilize 'anatomy-datasets' to handle these operations seamlessly.
   - **Data Preprocessing**: Include options for data augmentation, normalization, and resizing. Ensure that these operations are optimized for performance using 'anatomy-datasets'.
   - **Data Visualization**: Provide a feature to visualize the loaded and preprocessed data. This could include overlaying segmented regions on top of the original images.
   - **Saving Processed Data**: Allow users to save the processed datasets in desired formats, ensuring compatibility with popular machine learning frameworks like TensorFlow and PyTorch.

3. **Implementation Steps**:
   - **Setup Environment**: Start by setting up a Python environment with necessary dependencies, including 'anatomy-datasets'.
   - **Load Datasets**: Use 'anatomy-datasets' to implement functions that load datasets from specified paths. Test these functions with sample datasets provided by the package.
   - **Preprocess Data**: Develop functions for data augmentation, normalization, and resizing. Integrate these functions into a pipeline that applies them sequentially to each dataset.
   - **Visualize Data**: Create a visualization module that can display the original and processed images side-by-side. This will help users understand the effects of preprocessing steps.
   - **Save Data**: Implement functionality to save the processed datasets in formats compatible with major machine learning libraries.

4. **Testing and Documentation**:
   - **Test Cases**: Write test cases to ensure each component of the application works as expected.
   - **Documentation**: Prepare comprehensive documentation that explains how to install, use, and customize 'AnatomicalSegmentationPrepTool'.

By completing this project, you'll not only enhance your skills in Python programming and data processing but also contribute to the field of medical imaging research.