Scalable automated segmentation quantifies mitochondrial proteins and morphology at the nanoscale
Yuan Tian, Alexander Sauer, Mariia Dmitrieva, Hongqing Han, Hannahmariam T. Mekbib, Yujin Bao, Xiaojia Guo, Chen Tian-min, Robert L. Safirstein, Gary V. Désir, Jens Rittscher, Joerg Bewersdorf
This paper introduces MAPS, a deep-learning approach for automated segmentation of mitochondrial ultrastructure in 3D pan-ExM images, enabling high-throughput quantification of morphology and protein localization.
bioRxiv · Jul 28, 2025 · PreprintCell Biology