# SynCellFactory: Generative Data Augmentation for Cell Tracking Source: [SynCellFactory: Generative Data Augmentation for Cell Tracking]() Collection index: [llms.txt]() Data updated: 2026-01-04T04:51:28.460Z Authors: Moritz Sturm, Lorenzo Cerrone, Fred A. Hamprecht Published: 2024-04-25T08:51:59.000Z ## 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 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 yet realistic 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Cell Tracking on Fluo-C2DL-MSC CTC (official)](): Cell tracking; Fluo-C2DL-MSC CTC (official); CTC Score: 70.3. - [Cell Tracking on DIC-C2DH-HeLa CTC (official)](): Cell tracking; DIC-C2DH-HeLa CTC (official); CTC Score: 94.3. - [Cell Tracking on Fluo-C2DL-Huh7 CTC (official)](): Cell tracking; Fluo-C2DL-Huh7 CTC (official); CTC Score: 92. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()