The newly added README provides a comprehensive overview of the automated pipeline for processing microscopy images from CZI format to TIFF, including segmentation using Ilastik. It details features, setup instructions, dependencies, usage guidelines, and troubleshooting tips, significantly improving documentation and aiding user understanding and implementation of the process.

Added new configuration options (`area_threshold`, `closing_kernel_size`, `aspect_ratio_min`, `aspect_ratio_max`) for improved segmentation correction in processing pipeline. These changes preserve true nodes of Ranvier by filtering based on cluster size and aspect ratio, and fill interruptions in nerve segments using morphological operations. Updated the function and documentation to reflect these enhancements. This improves accuracy and reliability of image segmentation results.

This change extracts the base file name without its extension when creating TIFF output files. Previously, the full file path with an extension was used, which could lead to redundant extensions in output filenames. This update ensures clearer and more concise naming conventions for generated files.

Introduced `process_pipeline.py` to automate conversion of CZI images to TIFF, perform analysis with Ilastik, and resize outputs based on YAML configuration. Updated dependencies in `requirements.txt` to support new functionality.

Switched from using OpenCV (cv2) to Tifffile for reading images, specifically TIFF files, in the resize_image function to better handle multi-dimensional image data. The change was made due to Tifffile's superior capabilities in dealing with TIFF images compared to OpenCV. Additionally, removed unnecessary resizing before Ilastik processing to streamline the workflow and avoid redundancy. This update aims to improve workflow efficiency and maintain image quality handling in microscopy applications.

This new script automates the process of running Ilastik in headless mode for image segmentation while resizing input and output images. Input images are resized using quality-preserving interpolation, whereas segmented outputs use nearest-neighbor interpolation to maintain segmentation integrity. It is designed to handle large batches of images efficiently, supporting user-specified scale factors and ensuring ease of use with command-line arguments.