The Cell Tracking Challenge: 10 years of objective benchmarking
暂无分享,去创建一个
Noor M. Al-Shakarji | Andrew R. Cohen | Imad Eddine Toubal | M. Maška | E. Meijering | J. Allebach | R. Mikut | P. Matula | M. Kozubek | K. Palaniappan | F. Isensee | E. Meyerowitz | Assaf Arbelle | T. R. Raviv | A. Muñoz-Barrutia | Alexandre Cunha | V. Ulman | Cristina Ederra | F. Lux | Katharina Löffler | Tim Scherr | Tereza Nečasová | Yanming Zhu | Estibaliz Gómez-de-Mariscal | Tal Ben-Haim | Tianqi Guo | Ainhoa Urbiola | C. Ortíz-de-Solórzano | K. Maier-Hein | Ko Sugawara | Rina Bao | Pablo Delgado-Rodriguez | T. I. Ren | Gani Rahmon | Klas E. G. Magnusson | Fidel A Guerrero Peña | Yin Wang | Layton Aho | Paul F Jäger | Fabian Isensee | Paul F. Jäger
[1] J. Allebach,et al. Training a universal instance segmentation network for live cell images of various cell types and imaging modalities , 2022, ArXiv.
[2] R. Mikut,et al. EmbedTrack—Simultaneous Cell Segmentation and Tracking Through Learning Offsets and Clustering Bandwidths , 2022, IEEE Access.
[3] Assaf Arbelle,et al. Dual-Task ConvLSTM-UNet for Instance Segmentation of Weakly Annotated Microscopy Videos , 2022, IEEE Transactions on Medical Imaging.
[4] Tammy Riklin-Raviv,et al. Graph Neural Network for Cell Tracking in Microscopy Videos , 2022, ECCV.
[5] Tobias Pietzsch,et al. Labkit: Labeling and Segmentation Toolkit for Big Image Data , 2021, bioRxiv.
[6] Noor M. Al-Shakarji,et al. DMNet: Dual-Stream Marker Guided Deep Network for Dense Cell Segmentation and Lineage Tracking , 2021, 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).
[7] K. Rogers,et al. Spatial omics and multiplexed imaging to explore cancer biology , 2021, Nature Methods.
[8] Philipp J. Keller,et al. Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations , 2021, bioRxiv.
[9] R. Yuste,et al. Ensemble synchronization in the reassembly of Hydra’s nervous system , 2021, Current Biology.
[10] Bjoern H Menze,et al. The Medical Segmentation Decathlon , 2021, Nature Communications.
[11] J. D’hooge,et al. Extracting neuronal activity signals from microscopy recordings of contractile tissue using B-spline Explicit Active Surfaces (BEAS) cell tracking , 2021, Scientific Reports.
[12] R. Mikut,et al. A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction , 2021, bioRxiv.
[13] M. Averof,et al. Tracking cell lineages in 3D by incremental deep learning , 2021, bioRxiv.
[14] Guna Seetharaman,et al. Motion U-Net: Multi-cue Encoder-Decoder Network for Motion Segmentation , 2021, 2020 25th International Conference on Pattern Recognition (ICPR).
[15] Jens Petersen,et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation , 2020, Nature Methods.
[16] E. Meijering. A bird’s-eye view of deep learning in bioimage analysis , 2020, Computational and structural biotechnology journal.
[17] Ralf Mikut,et al. Cell segmentation and tracking using CNN-based distance predictions and a graph-based matching strategy , 2020, PloS one.
[18] Ing Ren Tsang,et al. J Regularization Improves Imbalanced Multiclass Segmentation , 2019, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).
[19] Ing Ren Tsang,et al. A Weakly Supervised Method for Instance Segmentation of Biological Cells , 2019, DART/MIL3ID@MICCAI.
[20] Yang Zhao,et al. Deep High-Resolution Representation Learning for Visual Recognition , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[21] Luc Van Gool,et al. Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Petr Matula,et al. DIC Image Segmentation of Dense Cell Populations by Combining Deep Learning and Watershed , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).
[23] M. Sauer,et al. Super-resolution microscopy demystified , 2019, Nature Cell Biology.
[24] M. Götz,et al. Cell tracking in vitro reveals that the extracellular matrix glycoprotein Tenascin-C modulates cell cycle length and differentiation in neural stem/progenitor cells of the developing mouse spinal cord , 2018, Biology Open.
[25] David Svoboda,et al. FiloGen: A Model-Based Generator of Synthetic 3-D Time-Lapse Sequences of Single Motile Cells With Growing and Branching Filopodia , 2018, IEEE Transactions on Medical Imaging.
[26] John M. Girkin,et al. The light-sheet microscopy revolution , 2018 .
[27] Ing Ren Tsang,et al. Multiclass Weighted Loss for Instance Segmentation of Cluttered Cells , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).
[28] Nathalie Harder,et al. An Objective Comparison of Cell Tracking Algorithms , 2017, Nature Methods.
[29] M. Maška,et al. Cell Tracking Accuracy Measurement Based on Comparison of Acyclic Oriented Graphs , 2015, PloS one.
[30] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[31] Joakim Jalden,et al. Global Linking of Cell Tracks Using the Viterbi Algorithm , 2015, IEEE Transactions on Medical Imaging.
[32] Nathalie Harder,et al. A benchmark for comparison of cell tracking algorithms , 2014, Bioinform..
[33] Trevor Darrell,et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[34] Jens Rittscher,et al. Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis , 2011, Medical Image Anal..
[35] Berthold K. P. Horn,et al. Determining Optical Flow , 1981, Other Conferences.
[36] Eduardo Romera,et al. ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation , 2018, IEEE Transactions on Intelligent Transportation Systems.
[37] David Svoboda,et al. MitoGen: A Framework for Generating 3D Synthetic Time-Lapse Sequences of Cell Populations in Fluorescence Microscopy , 2017, IEEE Transactions on Medical Imaging.