A table containing imaging analysis tools for biology and neuroscience, with a focus on calcium imaging.
Created by Biafra Ahanonu, PhD (HHMI Hanna Gray Fellow, Basbaum Lab, UCSF).
Calcium imaging analysis with CIAtah (https://github.com/bahanonu/ciatah).
The table can also be found at:
- https://bahanonu.com/brain/imaging_tools/
- https://bahanonu.com/syscarut/articles/233/
- If you would like to cite this table, see
Cite this repositoryin the rightAboutsection or https://zenodo.org/record/8349533.
Notes:
- I use cell extraction to refer to algorithms that perform cell segmentation and extract neural activity traces.
- In cases where the publication did not explicitly give the algorithm a name, made one based on the underlying method used.
- This table includes algorithms that simultaneously extract cell images/contours and reconstruct cell activity traces along with ones mainly focused on determining one or the other.
- Several calcium imaging related packages have also been included along with algorithms dealing with post-hoc handling of data or cell activity traces.
- Future versions of the repository will include table file (e.g. CSV) and basic LaTeX code so others can import or modify the table more easily going forward.
- Depending on monitor size and browser, scroll horizontally to see right-most table columns (e.g. websites/URLs).
- Any additional papers or algorithms that should be added or suggested updates to the table, leave a comment on the associated blog post or open an issue on the GitHub page, I want to make sure everyone’s brilliant work is acknowledged!
| # | Method | Year | Analysis pipeline | Notes/Code | Citation |
| 1 | PhaseCorrelation | 1996 | Motion correction. | • Phase correlation for motion correction, to include translation, rotation, and scale-invariance. | Reddy and Chatterji 1996 |
| 2 | Turboreg | 1998 | Motion correction. | • Motion correction. • http://bigwww.epfl.ch/thevenaz/turboreg/ | Thevenaz et al. 1998 |
| 3 | subPixelPhase | 2002 | Motion correction. | • Closed-form solution to subpixel translation estimation using phase correlation. | Foroosh et al. 2002 |
| 4 | ROI | 2005 | Cell extraction | • Matrix multiplication; in some methods neuropil/background subtraction implemented. | Kerr et al. 2005; Kuchibhotla et al. 2014; Peron et al. 2015 |
| 5 | CellProfiler | 2006 | Cell segmentation | • Multi-algorithm pipeline for cell segmentation. • https://cellprofiler.org | Carpenter et al. 2006; McQuin et al. 2018; Lamprecht et al. 2007 |
| 6 | PCA-ICA | 2009 | Cell extraction | • Cell extraction using principal component analysis (PCA) followed by independent component analysis (ICA). | Mukamel et al. 2009 |
| 7 | ANTs | 2009 | Image analysis | • Suite of tools for registering and analyzing imaging data. • http://stnava.github.io/ANTs/ | Avants et al. 2009 |
| 8 | elastix | 2009 | Motion correction | • A general toolbox for rigid and non-rigid image registration. • https://elastix.lumc.nl | Klein et al. 2009 |
| 9 | Lucas–Kanade framework | 2009 | Motion correction | • Lucas-Kanade framework for non-uniform motion image registration. | Greenberg and Kerr 2009 |
| 10 | CIRF (calcium-behavior) | 2011 | Cell extraction | • Regressive model to obtain Ca2+ signal based on behavior. | Miri et al. 2011 |
| 11 | openBIS | 2011 | Data handling | • FAIR data management. • https://openbis.ch | Bauch et al. 2011 |
| 12 | Automated ROI analysis | 2012 | Cell extraction | • Automatic ellipses based ROI detection. | Francis et al. 2012 |
| 13 | OMERO | 2012 | Data handling | • Microscopy data handling. • https://www.openmicroscopy.org | Allan et al. 2012 |
| 14 | ADINA | 2013 | Cell extraction | • Sparse dictionary learning. | Diego et al. 2013 |
| 15 | TPP | 2013 | Analysis pipeline | • Tool for processing two-photon calcium imaging data, e.g. finding cells with SeNeCA. • http://uemweb.biomed.cas.cz/tpp/ | Tomek et al. 2013 |
| 16 | NMF | 2014 | Cell extraction | • Cell extraction using nonnegative matrix factorization (NMF). Followed by CNMF. | Pnevmatikakis et al. 2014; Maruyama et al. 2014 |
| 17 | SIMA | 2014 | Analysis pipeline | • Normalized cut segmentation, motion correction, etc. • https://github.com/losonczylab/sima | Kaifosh et al. 2014 |
| 18 | DataJoint | 2015 | Data handling | • Schema for data handling. • https://github.com/datajoint/datajoint-matlab | Yatsenko et al. 2015 |
| 19 | NWB | 2015 | Data handling | • Neurodata Without Borders (NWB) initiative to produce a common data format for electrophysiology and imaging studies. • https://github.com/NeurodataWithoutBorders | Teeters et al. 2015 |
| 20 | Suite2p | 2016 | Cell extraction | • Generative model along with GUIs. | Pachitariu et al. 2016 |
| 21 | CNMF (CaImAn) | 2016 | Cell extraction | • Constrained NMF (CNMF). • https://github.com/flatironinstitute/CaImAn-MATLAB | Pnevmatikakis et al. 2016 |
| 22 | CNMF-E | 2016 | Cell extraction | • CNMF + background model to handle one-photon data. • https://github.com/zhoupc/CNMF_E | Zhou et al. 2016, 2018 |
| 23 | Apthorpe CNN | 2016 | Cell segmentation | • Convolutional neural network (CNN). | Apthorpe et al. 2016 |
| 24 | moco | 2016 | Motion correction | • Fourier-transform based motion correction. • https://github.com/NTCColumbia/moco | Dubbs et al. 2016 |
| 25 | Cytomine | 2016 | Analysis GUI | • Analysis of large-scale imaging data. • https://cytomine.be | Marée et al. 2016 |
| 26 | ROI clustering | 2016 | Cell extraction | • Select high-intensity pixels then perform clustering to segment. • https://www.bu.edu/hanlab/files/2016/02/pfgc.zip | Mohammed et al. 2016 |
| 27 | CELLMax (conference) | 2017 | Cell extraction | • Cell segmentation and activity trace extraction using a maximum likelihood approach. | Ahanonu et al. 2018, 2017; Ahanonu 2018 |
| 28 | sc-CNMF | 2017 | Cell extraction | • CNMF + GMM/RNN seed cleansing. | Lu et al. 2017 |
| 29 | OASIS | 2017 | Trace analysis | • Generalized pool adjacent violators algorithm. • https://github.com/zhoupc/OASIS_matlab | Friedrich et al. 2017 |
| 30 | ABLE | 2017 | Cell segmentation | • Multiple active contours and a cost function to identify cells in 2P data. • https://github.com/StephanieRey/ABLE | Reynolds et al. 2017 |
| 31 | SCALPEL | 2017 | Cell extraction | • Dictionary learning, dissimilarity, and clustering. • https://cran.r-project.org/web/packages/scalpel/index.html | Petersen et al. 2017 |
| 32 | HNCcorr | 2017 | Cell segmentation | • Combinatorial optimization (correlation space analysis). • https://github.com/hochbaumGroup/HNCcorr | Spaen et al. 2017 |
| 33 | OnACID | 2017 | Cell extraction (online) | • NMF variant for online Ca2+ imaging processing. | Giovannucci et al. 2017 |
| 34 | EXTRACT | 2017 | Cell extraction | • Robust statistical estimation. | Inan et al. 2017 |
| 35 | NETCAL | 2017 | Analysis pipeline | • Calcium imaging analysis GUI. • https://github.com/orlandi/netcal | Orlandi et al. |
| 36 | NoRMCorre | 2017 | Motion correction. | • Piecewise rigid motion correction. • https://github.com/simonsfoundation/NoRMCorre | Pnevmatikakis and Giovannucci 2017 |
| 37 | CellReg | 2017 | Cross-session alignment | • Alignment of cells across days using a probabilistic approach. • https://github.com/zivlab/CellReg | Sheintuch et al. 2017 |
| 38 | NeuroSeg | 2017 | Cell segmentation | • Filtering and seed/clustering based cell segmentation. • https://github.com/baidatong/NeuroSeg | Guan et al. 2018 |
| 39 | CNMF-E+ | 2017 | Cell extraction | • Shrinkage estimation to improve CNMF-E initialization. | Takekawa et al. 2017 |
| 40 | Toolbox-Romano | 2017 | Analysis pipeline | • Full analysis pipeline with ROI-based segmentation • https://github.com/zebrain-lab/Toolbox-Romano-et-al | Romano et al. 2017 |
| 41 | SamuROI | 2017 | Analysis GUI | • GUI for data visualization • https://github.com/samuroi/SamuROI | Rueckl et al. 2017 |
| 42 | KNIME | 2017 | Analysis pipeline | • Workflow manager for data analysis. • https://www.knime.com | Fillbrunn et al. 2017 |
| 43 | U-Net2DS | 2017 | Cell segmentation | • Evaluated several deep learning models on Neurofinder, U-Net2DS best. • https://github.com/alexklibisz/deep-calcium | Klibisz et al. 2017 |
| 44 | CLEAN (conference) | 2018 | Cell sorting | • Machine learning based cell sorting of cell extraction outputs based on image and activity trace features. | Ahanonu et al. 2018; Ahanonu 2018 |
| 45 | FISSA | 2018 | Trace analysis | • Neuropil decontamination using local region around cell. • https://github.com/rochefort-lab/fissa | Keemink et al. 2018 |
| 46 | LSSC | 2018 | Cell segmentation | • Spectral clustering; variant to find local subset of eigenvectors. | Mishne et al. 2018 |
| 47 | PMD - PCA | 2018 | Denoising | • Spatially-localized penalized matrix decomposition for denoising; compression; and improved demixing. • https://github.com/paninski-lab/funimag | Buchanan et al. 2018 |
| 48 | MIN1PIPE | 2018 | Analysis pipeline | • Pre-processing to enhance neural signals then sc-CNMF for cell extraction. | Lu et al. 2018 |
| 49 | CaImAn (preprint) | 2018 | Analysis pipeline | • CNMF + several other processing tools. | Giovannucci et al. 2018 |
| 50 | SEUDO (preprint) | 2018 | Trace analysis | • Mixture of Gaussians + maximum likelihood; post-hoc activity trace correction. | Gauthier et al. 2018 |
| 51 | ACSAT | 2018 | Cell segmentation | • Global and local adaptive thresholding to identify neurons. • https://github.com/sshen8/acsat | Shen et al. 2018 |
| 52 | onlineMotionCorrection | 2018 | Motion correction | • Tested multiple algorithms and developed an online motion correction pipeline. • https://github.com/amitani/onlineMotionCorrection | Mitani and Komiyama 2018 |
| 53 | CIAtah | 2019 | Analysis pipeline | • 1P and 2P Imaging analysis pipeline supporting PCA-ICA, CNMF, CELLMax, EXTRACT, etc. • https://github.com/bahanonu/ciatah | Corder et al. 2019; Ahanonu 2018; Ahanonu and Corder 2022 |
| 54 | NAOMi (bioRxiv) | 2019 | Simulator | • Generative model for creating simulated calcium imaging movies. | Charles et al. 2019 |
| 55 | CALIMA | 2019 | Analysis pipeline | • Calcium imaging analysis GUI. | Radstake et al. 2019 |
| 56 | STNeuroNet | 2019 | Cell segmentation | • Convolutional neural network to detect and segment cells. | Soltanian-Zadeh et al. 2019 |
| 57 | AQuA | 2019 | Cell extraction | • Astrocyte imaging focused. Non-ROI cluster and propagation based detection of events. | Wang et al. 2019 |
| 58 | CaImAn | 2019 | Analysis pipeline | • Popular calcium imaging pipeline that includes CNMF + several other processing tools. • https://github.com/flatironinstitute/CaImAn | Giovannucci et al. 2019 |
| 59 | DL+RWL1-SF | 2019 | Cell extraction | • Dictionary learning and spatial correlation based cell extraction. | Mishne and Charles 2019 |
| 60 | Segment2P | 2019 | Cell segmentation | • Pre-process images and run through DeepLabV3. • https://github.com/NoahDolev/Segment2P | Dolev et al. 2019 |
| 61 | LANMC | 2019 | Motion correction | • Long short-term memory non-rigid motion correction, reduce computational cost by predicting non-rigid motion. | Chen et al. 2019 |
| 62 | marked point processes | 2020 | Cell extraction | • Probabilistic generative model, specifically a marked point process, to extract activity traces. | Shibue and Komaki 2020 |
| 63 | LocaNMF | 2020 | Region extraction | • Localized semi-nonnegative matrix factorization for extracting active regions. • https://github.com/ikinsella/locaNMF | Saxena et al. 2020 |
| 64 | EZcalcium | 2020 | Analysis pipeline | • Calcium imaging analysis toolbox. • https://github.com/porteralab/EZcalcium | Cantu et al. 2020 |
| 65 | OnACID-E + ring CNN | 2020 | Cell extraction (online) | • OnACID for miniscope and new ring CNN background model to improve accuracy. • https://github.com/flatironinstitute/CaImAn | Friedrich et al. 2020 |
| 66 | Auto CNMF-E sorting | 2020 | Cell sorting | • Machine learning (AutoML) based curation of CNMF-E outputs. • https://github.com/jf-lab/cnmfe-reviewer | Tran et al. 2020a,b |
| 67 | DeepInterpolation | 2020 | Denoising | • Encoder-decoder architecture with 2D conv. to denoise imaging data. • https://github.com/AllenInstitute/deepinterpolation | Lecoq et al. 2020 |
| 68 | BIAFLOWS | 2020 | Benchmarking | • Framework for benchmarking imaging analysis workflows. • https://biaflows.neubias.org | Rubens et al. 2020 |
| 69 | FIBSI | 2020 | Trace analysis | • Extension of Ramer-Douglas-Peucker algorithm to identify baseline that is used for signal detection. • https://github.com/rmcassidy/FIBSI_program | Cassidy et al. 2020; Alles et al. 2021 |
| 70 | DISCo | 2020 | Cell segmentation | • Pixel correlation and deep learning (CNN) + graph based segmentation. • https://github.com/EKirschbaum/DISCo | Kirschbaum et al. 2020 |
| 71 | DeepCINAC | 2020 | Trace analysis | • Trace analysis after human labeling followed by CNNs + bidirectional long-short term memory (LSTM) network. • https://gitlab.com/cossartlab/deepcinac | Denis et al. 2020 |
| 72 | NDSEP | 2020 | Cell extraction | • Dataflow framework for real-time calcium imaging processing. • http://dspcad-www.iacs.umd.edu/bcnm/index.html | Lee et al. 2020 |
| 73 | DeepBrainSeg | 2020 | Segmentation | • Dual-pathway CNN to learn local and contextual features. | Tan et al. 2020 |
| 74 | RT-3DMC | 2020 | Motion correction | • Bead or soma tracking for real-time motion correction during 2P imaging. • https://github.com/SilverLabUCL/SilverLab-Microscope | Griffiths et al. 2020 |
| 75 | Cellpose | 2021 | Cell segmentation | • Neural network and gradient-based cell segmentation. • https://github.com/mouseland/cellpose | Stringer et al. 2021 |
| 76 | NAOMi | 2021 | Simulator | • Detailed model simulation for benchmarking calcium imaging algorithms. • https://bitbucket.org/adamshch/naomi_sim/src/master/ | Song et al. 2021 |
| 77 | OnACID-E + ring CNN | 2021 | Cell extraction (online) | • OnACID for 1P data and ring CNN background model. • https://github.com/flatironinstitute/CaImAn | Friedrich et al. 2021 |
| 78 | EXTRACT | 2021 | Cell extraction | • Robust statistics based cell extraction. • https://github.com/schnitzer-lab/EXTRACT-public | Inan et al. 2021 |
| 79 | Minian | 2021 | Analysis pipeline | • Imaging analysis pipeline with CNMF for cell extraction, in part using Jupyter notebooks with GUI elements. • https://github.com/DeniseCaiLab/minian | Dong et al. 2021 |
| 80 | Mesmerize | 2021 | Analysis pipeline | • Imaging analysis platform with CaImAn for cell extraction, import support for other cell extraction algorithms. • https://github.com/kushalkolar/MESmerize | Kolar et al. 2021 |
| 81 | DeepInterpolation | 2021 | Denoising | • Encoder-decoder architecture with 2D conv. to denoise imaging data. • https://github.com/AllenInstitute/deepinterpolation | Lecoq et al. 2021 |
| 82 | BEAR | 2021 | Cell extraction | • Neural network approximation of PCA for cell extraction. • https://github.com/NICALab/BEAR | Han et al. 2021 |
| 83 | CaPTure | 2021 | Cell extraction | • ROI segmentation and activity extraction. • https://github.com/LieberInstitute/CaPTure | Tippani et al. 2021 |
| 84 | CASCADE | 2021 | Trace analysis | • Spike inference based on dual ephys/calcium imaging recordings. • https://github.com/HelmchenLabSoftware/Cascade | Rupprecht et al. 2021 |
| 85 | VolPy | 2021 | Analysis pipeline | • Voltage imaging analysis pipeline integrated into CaImAn. • https://github.com/flatironinstitute/CaImAn | Cai et al. 2021 |
| 86 | DeepCAD | 2021 | Denoising | • Deep neural network based denoising. • https://github.com/cabooster/DeepCAD-RT | Li et al. 2021 |
| 87 | SpecSeg | 2021 | Cell extraction | • Spectral density of pixels to identify ROIs. Also incorporates motion correction and cross-session matching. • https://github.com/Leveltlab/SpectralSegmentation | de Kraker et al. 2021 |
| 88 | FIOLA | 2021 | Cell extraction (online) | • GPU- and computational graph-based speed-ups along with non-negative least squares for post-initialization signal extraction. • https://github.com/nel-lab/FIOLA | Giovannucci et al. 2021 |
| 89 | PatchWarp | 2021 | Motion correction | • Affine transformation of subfields followed by stitching subfields together. • https://github.com/ryhattori/PatchWarp | Hattori and Komiyama 2021 |
| 90 | MVG-CNN | 2021 | Region extraction | • Automated sleep states classification using multiplex visibility graphs and deep learning. Data URL. • https://github.com/comp-imaging-sci/MVG-CNN | Zhang et al. 2021 |
| 91 | Flow-Registration | 2021 | Motion correction | • Variational optical flow for non-uniform motion correction • https://github.com/phflot/flow_registration | Flotho et al. 2022 |
| 92 | SUNS | 2021 | Cell segmentation | • Cell segmentation using shallow U-Nets. • https://github.com/YijunBao/Shallow-UNet-Neuron-Segmentation_SUNS | Bao et al. 2021 |
| 93 | Carignan | 2021 | Cell extraction | • Online cell extraction and triggering based on OnACID and CaImAn. • https://github.com/tzklab/carignan | Taniguchi et al. 2021 |
| 94 | MullenClassifier | 2021 | Cell sorting | • Feature extraction from cell images and tracs followed by supervised learning classifier. | Mullen et al. 2021 |
| 95 | timeUnet | 2021 | Denoising | • Deep learning for denoising with temporal information added in. • https://github.com/BoHuangLab/Transfer-Learning-Denoising/ | Wang et al. 2021 |
| 96 | EMC2 | 2021 | Motion correction | • Wavelet decomposition to detect bright spots followed by motion correction with multiple hypothesis tracking and computing elastic deformation. • https://icy.bioimageanalysis.org/plugin/elastic-motion-correction-concatenation-emc2-of-tracks/ | Lagache et al. 2021 |
| 97 | GraFT | 2022 | Cell extraction | • Dictionary-based learning of activity traces followed by graph-based segmentation. • https://github.com/adamshch/GraFT-analysis | Charles et al. 2022 |
| 98 | CaPTure | 2022 | Analysis pipeline | • Binary/watershed segmentation followed by ROI-based mean traces. • https://github.com/LieberInstitute/CaPTure | Tippani et al. 2022 |
| 99 | SpecSeg | 2022 | Cell segmentation | • Cross spectral power-based segmentation of neurons and neurites. • https://github.com/Leveltlab/SpectralSegmentation | de Kraker et al. 2022 |
| 100 | CITE-On | 2022 | Cell extraction | • Online cell detection and trace extraction using CNNs. • https://gitlab.iit.it/fellin-public/cite-on | Sità et al. 2022 |
| 101 | DL-assisted 2P fiberscope | 2022 | Denoising | • Denoising 2P fiberscope data using deep neural network (conditional generative adversarial network). • https://figshare.com/articles/dataset/Data/19193792 | Guan et al. 2022 |
| 102 | 4SM | 2022 | Cell extraction | • Generative adversarial network for image segmentation. • https://github.com/SharifAmit/4SM | Kamran et al. 2022 |
| 103 | DeepCAD-RT | 2022 | Denoising | • Improved version of DeepCAD for real time performance. • https://github.com/cabooster/DeepCAD-RT/ | Li et al. 2023a |
| 104 | SEUDO | 2022 | Trace analysis | • Mixture of Gaussians + maximum likelihood; post-hoc activity trace correction. • https://github.com/adamshch/SEUDO | Gauthier et al. 2022 |
| 105 | AxialMotionCorrect | 2022 | Motion correction | • Axial motion correction via multi-plane scanning plus maximum likelihood optimization. • https://gitlab.com/anflores/axial_motion_correction | Flores-Valle and Seelig 2022 |
| 106 | FIFER | 2022 | Motion correction | • Feature-based motion correction, finding features using a density-based estimating and clustering algorithm and matching features with a similarity metric for registration. • https://github.com/Weiyi-Liu-Unique/FIFER | Liu et al. 2022 |
| 107 | NWB | 2022 | Data handling | • Neurodata Without Borders (NWB) to standardize ephys and imaging data across tools. • https://github.com/NeurodataWithoutBorders | Rübel et al. 2022 |
| 108 | DeCalciOn | 2023 | Online analysis pipeline | • Integrate hardware and software to online decode calcium signals. • https://github.com/zhe-ch/ACTEV | Chen et al. 2023 |
| 109 | jGCaMP8 | 2023 | Calcium indicator | • Improved calcium indicators with increased sensitivity and reduced background. | Zhang et al. 2023a |
| 110 | NeuroSeg-II | 2023 | Cell segmentation | • 2P cell segmentation using region-based convolutional neural network with modifications. • https://github.com/XZH-James/NeuroSeg2 | Xu et al. 2023 |
| 111 | CaliAli | 2023 | Cross-session alignment | • Cross-session alignment using vasculature information. • https://github.com/CaliAli-PV/CaliAli | Vergara et al. 2023 |
| 112 | DeepWonder | 2023 | Cell extraction | • Deep-learning-based cell finding for widefield datasets. • https://github.com/yuanlong-o/Deep_widefield_cal_inferece | Zhang et al. 2023b |
| 113 | ASTRA | 2023 | Cell segmentation | • Deep neural network for astrocyte segmentation. • https://gitlab.iit.it/fellin-public/astra | Bonato et al. 2023 |
| 114 | SRDTrans | 2023 | Denoise | • Spatial redundancy for training followed by spatiotemporal transformer architecture to reduce CNN bias/issues. • https://github.com/cabooster/SRDTrans | Li et al. 2023b |
| 115 | REALS | 2023 | Motion correction | • Motion correction via simultaneous transformation and low rank and sparse decomposition with gradient-based updates. • https://openaccess.thecvf.com/content/WACV2023/supplemental/Cho_Robust_and_Efficient_WACV_2023_supplemental.zip | Cho et al. 2023 |
| 116 | LD-MCM | 2023 | Motion correction | • Motion correction using deep learning feature identification and control point registration. • https://github.com/bahanonu/ciatah | Ahanonu et al. 2023 |
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