This repository contains a Keras implementation of the U-Net architecture for biomedical image segmentation, specifically trained on the ISBI challenge membrane dataset. The code provides a straightforward pipeline: data loading and augmentation (via Keras ImageDataGenerator), model definition (U-Net with symmetric encoder-decoder), training script (main.py or notebook), and inference to generate predicted segmentation masks. The dataset is bundled within the repo (30 training images and masks, plus tests). The implementation relies on an outdated Keras 1.x API (deprecated `merge` function, `Model.fit_generator`) and has no dependency pinning or environment specification, making reproducibility low. While the functional components like the data generator and U-Net architecture are reusable patterns, the codebase is academically simple and exhibits significant technical debt relative to modern deep learning frameworks. It is best suited as a reference or educational starting point rather than production use.