bibtext: @InProceedings{ Stu_SynCellFactory_MICCAI2024,
                   author = { Sturm, Moritz and Cerrone, Lorenzo and Hamprecht, Fred A. },
                   title = { { SynCellFactory: Generative Data Augmentation for Cell Tracking } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15012 },
                   month = {October},
                   pages = { pending },
                }
abstract: Cell tracking remains a pivotal yet challenging task in biomedical research. The full potential of deep learning for this purpose is often untapped due to the limited availability of comprehensive and varied training data sets. In this paper, we present SynCellFactory, a generative method for cell video augmentation.
  At the heart of SynCellFactory lies the ControlNet architecture, which has been fine-tuned to synthesize cell imagery with photorealistic accuracy in style and motion patterns. This technique enables the creation of synthetic, annotated cell videos that mirror the complexity of authentic microscopy time-lapses.
  Our experiments demonstrate that SynCellFactory boosts the performance of well-established deep learning models for cell tracking, particularly when original training data is sparse.
title: SynCellFactory: Generative Data Augmentation for Cell Tracking
authors: Sturm, Moritz Cerrone, Lorenzo Hamprecht, Fred A.