Machine learning
Nuclei image segmentation
A custom U-Net training pipeline for microscopy-image segmentation, connected to a Django inference application.
Overview
Identifying individual structures in microscopy images
This project segments cell nuclei in microscopy images. Instead of assigning one label to an entire image, it predicts which pixels belong to the structures of interest.
A Django interface connects image input to segmentation output, bringing the model into an application that can present its result visually. The work joins model development with the engineering needed to make inference accessible.
Technical detail
A custom PyTorch U-Net uses an encoder–decoder structure and double-convolution blocks. The training pipeline uses the 2018 Data Science Bowl nuclei dataset and includes preparation, checkpointing, pixel-accuracy checks, and intersection-over-union utilities.
The inference application is separate from the training workflow and includes Docker/Compose configuration. This separation allows model development and application presentation to evolve independently.