pyMarAI

Authors: Jens Maus, Janina Nitschke, Pavel Nikulin, Frank Hofheinz, Mareike Barth, Sandy Lemm, Lena Richter, Jens Pietzsch, Anja Braune, Martin Ullrich

Keywords: Tumor Spheroid Imaging, Radiopharmacological Treatment Response Assays, Delineation, Cancer, Deep-Learning, Artifical Intelligence, Convolutional Neural Networks, Network model, Graphical User Interface

Collection of neural network models for automatic image segmentation of microscopic tumor spheroids. Intended to be used with nnU-Net deep-learning framework. Trained and tested on a total of microscopic images of mouse pheochromocytoma (MPC) tumor cells.

In addition to the trained network model, a PyQt5-based graphical user interface tool is provided. This tool provides a complete pipeline for handling microscopic spheroid image data, running deep-learning–based delineation, and curating results for continuous model improvement.


Publications

Automatic Delineation of Tumor Spheroids in Microscopic Images Using Deep-Learning

Maus J, Nitschke J, Nikulin P, Hofheinz F, Barth M, Lemm S, Richter L, Pietzsch J, Braune A, Ullrich M - ACS Measurement Science Au - 2026


pyMarAI: nnU-Net-based Tumor Spheroids Auto Delineation

Maus J, Nitschke J, Nikulin P, Hofheinz F, Barth M, Lemm S, Richter L, Pietzsch J, Braune A, Ullrich M - Rodare - 2026


pyMarAI Image
Weblinks
License
Apache License 2.0, CC-BY-SA-4.0


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