Mapping human microglial morphological diversity via handcrafted and deep learning-derived image features
Kayhan Alvandipour, Amélie Weiss, Mona Mathews, Brenda Besemer, Michaela Segschneider, Zahra Hanifehlou, Christian Felski, Michael Peitz, Arnaud Ogier, Peter Sommer, Oliver Brüstle, Johannes H. Wilbertz
The paper develops an imaging and analysis framework to map human microglial activation states using handcrafted and deep learning features, showing deep learning features achieve high classification accuracy and correlate with biological states.
iScience · Jun 27, 2026Peer-reviewedNeuroscience