This repository provides a complete workflow and software toolkit for automated nuclei detection on immunohistochemically stained images (specifially designed to work with complex ECMO membrane lung fiber mat images). The project implements a Mask R‑CNN–based instance segmentation model built with PyTorch (v2.2.0) and Detectron2 framework, along with training utilities and a ready‑to‑use Windows application for end‑users. The training and desktop application rely heavily on the following packages: Computer Vision Annotation Tool (CVAT) Detectron2 by Facebook AI Research Slicing Aided Hyper Inference (sahi) by Open Business Software Solutions PyOneDark Modern GUI Project Overview Accurate nuclei detection and shape analysis is a crucial step in analysing cellular deposits on membrane fibers to better understand why clotting occurs in ECMO. This repository delivers an end‑to‑end machine learning solution tailored to this domain: A custom desktop application optimized for nuclei identification, clustering and shape analysis. Tools to pre-process image data Evaluation and comparison against established open‑source tools such as Cellpose and StarDist. Repository Structure training/ – Training Utilities Includes scripts and helper modules for: Data pre-processing of COCO formated annotations exported from Computer Vision Annotation Tool (CVAT) (01_balance_coco_data.py, 02_split_coco_dataset_into_patches.py) Data used for training: training/TRAINDATA/sliced_coco Train script (03_train_model.py) Model evaluation (04_evaluate_model.py) Visualization of ground truth and predictions (05_inference.py) comparison/ – Comparison against available nuclei detection software Contains: Scripts to run inference using Cellpose (comparison/model_segm_comparison/cellpose/run_cellpose_on_images.py) and StarDist (comparison/model_segm_comparison/stardist/run_stardist_on_images.py) Trained model checkpoints (in mask_rcnn_configurations/mask_rcnn_configurations) Script to compare nuclei count and relative area of trained Mask R-CNN configurations with Cellpose and Stardist (model_segm_comparison.py) app/ – Python‑Based Windows Application A standalone Windows desktop application that allows end users to: Load tiff images Run nuclei detection using the trained Mask R‑CNN model Detect nulcei clusters Visualize and export segmentation results Generate summary statistics to PDF file Installation Python IDE To run the desktop application /app/main.py in your Python IDE, downalod Python (v3.10.19), create a virtual environment and install all required packages using #Create a virtual environment python -m venv env #Activate the environment env\Scripts\activate #Install required packages pip install -r requirements.txt Windows Desktop Application See /Windows_Portable and /Windows_Installer for installing the bundled Windows executable on your system. You can use /Windows_Portable for a portable installation and /Windows_Installer for installation in the user's application folder (AppData) or another user defined path.