1 code implementation • 10 Aug 2023 • Haoju Leng, Ruining Deng, Shunxing Bao, Dazheng Fang, Bryan A. Millis, Yucheng Tang, Haichun Yang, Xiao Wang, Yifan Peng, Lipeng Wan, Yuankai Huo
The performance evaluation encompasses two key scenarios: (1) a pure CPU-based image analysis scenario ("CPU scenario"), and (2) a GPU-based deep learning framework scenario ("GPU scenario").
no code implementations • 22 Jun 2021 • Mengyang Zhao, Quan Liu, Aadarsh Jha, Ruining Deng, Tianyuan Yao, Anita Mahadevan-Jansen, Matthew J. Tyska, Bryan A. Millis, Yuankai Huo
Recently, pixel embedding-based cell instance segmentation and tracking provided a neat and generalizable computing paradigm for understanding cellular dynamics.
no code implementations • 3 Jan 2021 • Quan Liu, Isabella M. Gaeta, Mengyang Zhao, Ruining Deng, Aadarsh Jha, Bryan A. Millis, Anita Mahadevan-Jansen, Matthew J. Tyska, Yuankai Huo
Contribution: The contribution of this paper is three-fold: (1) the proposed method aggregates adversarial simulations and single-stage pixel-embedding based deep learning; (2) the method is assessed with both the cellular (i. e., HeLa cells) and subcellular (i. e., microvilli) objects; and (3) to the best of our knowledge, this is the first study to explore annotation-free instance segmentation and tracking study for microscope videos.
no code implementations • 2 Nov 2020 • Quan Liu, Isabella M. Gaeta, Mengyang Zhao, Ruining Deng, Aadarsh Jha, Bryan A. Millis, Anita Mahadevan-Jansen, Matthew J. Tyska, Yuankai Huo
Instance object segmentation and tracking provide comprehensive quantification of objects across microscope videos.
1 code implementation • 2 Nov 2020 • Ruining Deng, Quan Liu, Shunxing Bao, Aadarsh Jha, Catie Chang, Bryan A. Millis, Matthew J. Tyska, Yuankai Huo
Our contribution is three-fold: (1) we approach the weakly supervised segmentation from a novel codebook learning perspective; (2) the CaCL algorithm segments diffuse image patterns rather than focal objects; and (3) the proposed algorithm is implemented in a multi-task framework based on Vector Quantised-Variational AutoEncoder (VQ-VAE) via joint image reconstruction, classification, feature embedding, and segmentation.
no code implementations • 22 Oct 2020 • Quan Liu, Isabella M. Gaeta, Bryan A. Millis, Matthew J. Tyska, Yuankai Huo
To match the number of objects at the micro-level, the novel fluorescence-based micro-level matching approach was presented.
1 code implementation • 28 Jul 2020 • Mengyang Zhao, Aadarsh Jha, Quan Liu, Bryan A. Millis, Anita Mahadevan-Jansen, Le Lu, Bennett A. Landman, Matthew J. Tyskac, Yuankai Huo
With both embedding simulation and empirical validation via the four cohorts from the ISBI cell tracking challenge, the proposed Faster Mean-shift algorithm achieved 7-10 times speedup compared to the state-of-the-art embedding based cell instance segmentation and tracking algorithm.