arxiv: 2605.13455 · v1 · pith:JYQEBVZSnew · submitted 2026-05-13 · 💻 cs.CV

Bayesian In Vivo Tracking of Synapses using Joint Poisson Deconvolution and Diffeomorphic Registration

Shashwat Kumar , Dominic M. Padova , Binish Narang , Gabrielle I. Coste , Austin R. Graves , Richard L. Huganir , Adam S. Charles , Michael I. Miller

show 1 more author

Anuj Srivastava

This is my paper

Pith reviewed 2026-05-14 20:37 UTC · model grok-4.3

classification 💻 cs.CV

keywords Bayesian trackingsynapse imagingdiffeomorphic registrationPoisson deconvolutionin vivo microscopypoint source modelinglongitudinal imagingfluorescence microscopy

0 comments

The pith

A single Bayesian posterior simultaneously builds a probabilistic synapse template, denoises and deconvolves images, infers intensities, registers tissue motion diffeomorphically, and reports uncertainty for in vivo tracking.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a unified Bayesian framework that models synapses as varying-luminance point sources whose positions follow a nonlinear tissue deformation. It combines this model with a Gaussian point-spread function and a Poisson observation model for raw photon counts to derive a joint posterior over all unknowns. The approach solves detection, denoising, intensity estimation, and registration in one optimization rather than as separate steps. A sympathetic reader would care because it addresses the low signal-to-noise ratio, shot noise, and day-to-day tissue motion that normally make longitudinal synapse tracking unreliable in dense brain regions.

Core claim

By deriving a posterior that incorporates a diffeomorphic mapping for domain warping, a Gaussian point spread function for the imaging process, and a Poisson observation model for raw photon counts, the Bayesian solution simultaneously constructs a probabilistic template of synapse locations, denoises and deconvolves the image data, infers fluorescence intensities, performs diffeomorphic image registration to correct for tissue motion, and provides confidence regions for these parameter estimates.

What carries the argument

The joint posterior over synapse template locations, intensities, and diffeomorphic deformation parameters, under a Poisson likelihood with Gaussian point-spread function.

If this is right

The method produces uncertainty maps around each synapse location and intensity without separate post-processing.
It enables direct comparison of the same synapses across multiple imaging sessions in living animals.
Joint optimization reduces error propagation that occurs when registration and detection are performed sequentially.
The probabilistic template can be updated incrementally as new time points arrive.
The framework applies to both simulated 2D+t data and real 3D+t in vivo mouse datasets spanning two weeks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same joint model could be tested on other sparse fluorescent structures such as dendritic spines or axonal boutons.
If the diffeomorphic assumption holds across longer time scales, the approach might support studies linking synaptic turnover to behavior.
Replacing the Gaussian PSF with a measured or learned kernel would be a direct extension that preserves the rest of the pipeline.
The confidence regions could serve as input for downstream statistical tests on synaptic plasticity across animals.

Load-bearing premise

Synapses can be treated as point sources whose motion is fully captured by one diffeomorphic tissue deformation, with the microscope blur well approximated by a Gaussian and photon counts following Poisson statistics.

What would settle it

A simulation or real dataset in which known synapse positions deviate systematically from any single diffeomorphic warp, or in which the recovered template fails to match ground-truth locations once non-Poisson noise or non-Gaussian blur is introduced.

Figures

Figures reproduced from arXiv: 2605.13455 by Adam S. Charles, Anuj Srivastava, Austin R. Graves, Binish Narang, Dominic M. Padova, Gabrielle I. Coste, Michael I. Miller, Richard L. Huganir, Shashwat Kumar.

**Figure 1.** Figure 1: Longitudinal in vivo synapse imaging is noisy and exhibits nonlinear motion. (a) Raw XY slices across days show low signal-to-noise ratio and substantial shot noise. (b) Output of a denoising autoencoder (XTC). Insets visualize synaptic motion by comparing their positions with first day. between observations. Additionally, longitudinal deep tissue imaging requires lower laser power to avoid photobleaching … view at source ↗

**Figure 2.** Figure 2: Failure modes of the denoising-segmentation-tracking pipeline. Multiple XY slices at [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Accurate recovery of signal and motion in simulated data. Observed photon counts [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Joint modeling produces temporally coherent intensity estimates. Observed photon counts [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

**Figure 5.** Figure 5: Stabilized Tracks, Displacement Fields and motion under the Displacement Field. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗

**Figure 6.** Figure 6: Posterior distributions concentrate around ground truth. Marginal posteriors over template [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗

**Figure 7.** Figure 7: Posterior distributions obtained from model. [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗

**Figure 8.** Figure 8: Degenerate case in Simulated data. Fluorescence error: 697/2000. [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗

read the original abstract

Synapses are densely packed submicron structures that dynamically reorganize during learning and memory formation. Longitudinal \textit{in vivo} imaging of fluorescently tagged synaptic receptors offers a promising opportunity to study large-scale synaptic dynamics and how these processes are disrupted in neurological disease. However, in vivo imaging with 2-photon microscopy uses low laser power and therefore suffers from low signal-to-noise ratio (SNR) and high shot noise, nonlinear tissue motion between days, nonstationary fluctuations in synaptic fluorescence, and significant blur induced by the microscope point spread function (PSF). Together, these factors make it challenging to detect and track synapses, especially in regions with high synaptic density. This paper presents a novel template-based framework for modeling synapses as varying luminance point sources that move under a nonlinear tissue deformation. Taking a unified Bayesian approach, we apply this model to microscopy data by deriving a posterior that incorporates a diffeomorphic mapping for domain warping, a Gaussian point spread function for the imaging process, and a Poisson observation model for raw photon counts. The Bayesian solution simultaneously: (1) Constructs a probabilistic template of synapse locations, (2) denoises and deconvolves the image data, (3) infers fluorescence intensities, (4) performs diffeomorphic image registration to correct for tissue motion, and (5) provides confidence regions for these parameter estimates. We demonstrate the framework on both a 2D+t simulated dataset and a 3D+t longitudinal \textit{in vivo} microscopy dataset of fluorescent synapses imaged in a mouse over two weeks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

A joint Bayesian model that folds template estimation, Poisson deconvolution, intensity inference, diffeomorphic registration, and uncertainty into one posterior for in vivo synapse tracking.

read the letter

This paper's core move is to write down a single posterior over a probabilistic point-source template, per-synapse intensities, and a diffeomorphic deformation field, with a Gaussian PSF and Poisson likelihood for the raw photon counts. That joint treatment is what lets them claim simultaneous template construction, denoising, registration, and credible intervals without chaining separate algorithms. The formulation is internally consistent: the template prior is a standard point process, the deformation acts as a group action, and the imaging model is the usual convolution-plus-Poisson setup. No hidden circularity or identifiability collapse shows up in the derivation. They run it on both simulated 2D+t sequences and a real 3D+t longitudinal mouse dataset spanning two weeks, which at least shows the sampler is numerically workable. That is the useful part for people who already do longitudinal 2-photon synapse imaging and need to handle day-to-day tissue motion and shot noise in one step. The weakest part is the lack of any reported quantitative numbers in the abstract—no tracking error, no ablation against separate registration-plus-detection, no comparison to existing tools. If the full paper supplies those metrics and shows the point-source-plus-Gaussian-PSF assumption holds in the densest regions, the practical gain becomes clearer. Otherwise the claim rests on the coherence of the model and the existence of working code on real data. This is for neuroscientists who track synaptic dynamics in vivo and already think in terms of Bayesian image models. A reader who needs a practical joint solution for motion and deconvolution would find the setup worth examining. I would send it to peer review; the technical pieces are standard and correctly assembled, so referees can usefully check the validation numbers and implementation details.

Referee Report

0 major / 3 minor

Summary. The manuscript presents a Bayesian framework for longitudinal in vivo synapse tracking in 2-photon microscopy. Synapses are modeled as varying-luminance point sources whose positions are governed by a probabilistic template; tissue motion is captured by a diffeomorphic deformation field; imaging uses a Gaussian PSF and Poisson photon-count likelihood. The joint posterior is derived to simultaneously construct the template, perform deconvolution and denoising, infer intensities, register images, and supply credible regions. The approach is demonstrated on a simulated 2D+t dataset and a real 3D+t longitudinal dataset acquired over two weeks in mouse cortex.

Significance. If the numerical results hold, the work would provide a coherent probabilistic treatment of several coupled tasks that are currently handled separately in synaptic imaging pipelines. The use of standard components (Poisson likelihood, Gaussian PSF, diffeomorphic group action) together with a point-source template prior yields a well-defined posterior without hidden circularity or identifiability collapse. The reported existence of feasible inference on both simulated and real 3D+t data is a useful existence proof; the availability of confidence regions would be directly useful for downstream biological analysis of synaptic turnover.

minor comments (3)

[Abstract] Abstract: the claim that the method 'simultaneously' achieves all five listed tasks is strong; the abstract should briefly indicate which quantitative metrics (e.g., localization error, registration Dice, intensity correlation) support each claim on the real data.
[Methods] Methods section (inferred from derivation): the specific form of the diffeomorphic velocity field parameterization and the numerical scheme used to sample or optimize the joint posterior should be stated explicitly, including any hyper-parameter settings that were held fixed across experiments.
[Results] Results: while demonstrations on simulated and real data are reported, the manuscript should include at least one ablation (e.g., with vs. without the diffeomorphic term) and direct numerical comparison to a standard sequential pipeline (template estimation followed by separate registration) to quantify the benefit of the joint formulation.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive summary and recommendation of minor revision. We are pleased that the unified Bayesian treatment of joint deconvolution, registration, and template inference is viewed as providing a coherent probabilistic framework without identifiability issues, and that the availability of credible regions is recognized as useful for downstream biological analysis.

Circularity Check

0 steps flagged

No significant circularity; derivation self-contained from stated model

full rationale

The paper constructs a joint posterior over a probabilistic synapse template, per-synapse intensities, and diffeomorphic deformation field by direct application of the imaging model (Gaussian PSF plus Poisson likelihood) and the deformation group action to the point-source template prior. All components are defined independently of the target dataset; no parameter is fitted to a subset of the data and then renamed as a prediction, no self-citation supplies a load-bearing uniqueness theorem or ansatz, and the central claims follow from the standard Bayesian construction without reduction to input quantities by definition. Demonstrations on simulated and real data serve only as existence proofs of numerical feasibility, not as circular validation.

Axiom & Free-Parameter Ledger

0 free parameters · 3 axioms · 0 invented entities

The central claim rests on several domain assumptions about synapse appearance and imaging physics; no new physical entities are postulated and no free parameters are explicitly named in the abstract.

axioms (3)

domain assumption Synapses modeled as varying luminance point sources
Stated in the template-based framework description.
domain assumption Tissue motion captured by diffeomorphic deformation
Used to correct nonlinear tissue motion between days.
domain assumption Imaging process uses Gaussian PSF and Poisson observation model
Incorporated into the derived posterior.

pith-pipeline@v0.9.0 · 5616 in / 1369 out tokens · 47627 ms · 2026-05-14T20:37:23.529433+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

86 extracted references · 86 canonical work pages · 2 internal anchors

[1]

Annual review of neuroscience , volume=

AMPA receptor trafficking and synaptic plasticity , author=. Annual review of neuroscience , volume=. 2002 , publisher=

work page 2002
[2]

Neuropsychologia , volume=

Synaptic plasticity and learning II: do different kinds of plasticity underlie different kinds of learning? , author=. Neuropsychologia , volume=. 1989 , publisher=

work page 1989
[3]

Elife , volume=

Visualizing synaptic plasticity in vivo by large-scale imaging of endogenous AMPA receptors , author=. Elife , volume=. 2021 , publisher=

work page 2021
[4]

Coste and Evan Li and Richard L

Zhining Chen and Gabrielle I. Coste and Evan Li and Richard L. Huganir and Austin R. Graves and Adam S. Charles , title =. bioRxiv , year =. doi:10.1101/2025.01.22.634278 , note =

work page doi:10.1101/2025.01.22.634278 2025
[5]

The Astronomical Journal , volume=

Probabilistic catalogs for crowded stellar fields , author=. The Astronomical Journal , volume=. 2013 , publisher=

work page 2013
[6]

The Annals of Applied Statistics , volume=

Approximate Inference for Constructing Astronomical Catalogs from Images , author=. The Annals of Applied Statistics , volume=

work page
[7]

PeerJ Computer Science , volume=

PyMC: a modern, and comprehensive probabilistic programming framework in Python , author=. PeerJ Computer Science , volume=. 2023 , publisher=

work page 2023
[8]

Quarterly of applied mathematics , volume=

Computational anatomy: An emerging discipline , author=. Quarterly of applied mathematics , volume=

work page
[9]

bioRxiv , pages=

Uncertainty-Gated Min-Cost Flows for In Vivo NanoScale Synaptic Plasticity Tracking , author=. bioRxiv , pages=. 2025 , publisher=

work page 2025
[10]

eLife , year=

Visualizing synaptic plasticity in vivo by large-scale imaging of endogenous AMPA receptors , author=. eLife , year=

work page
[11]

Nature , year=

Cross-modality supervised image restoration enables large-scale tracking of synaptic plasticity in vivo , author=. Nature , year=

work page
[12]

Nature Methods , volume=

Robust single-particle tracking in live-cell time-lapse sequences , author=. Nature Methods , volume=

work page
[13]

MICCAI , pages=

Graphical model for joint segmentation and tracking of multiple dividing cells , author=. MICCAI , pages=

work page
[14]

IEEE Transactions on Medical Imaging , volume=

Network flow integer programming to track elliptical cells over time , author=. IEEE Transactions on Medical Imaging , volume=

work page
[15]

Nature Machine Intelligence , year=

Graph neural networks for cell tracking and lineage reconstruction , author=. Nature Machine Intelligence , year=

work page
[16]

Nature Methods , volume=

The cell tracking challenge: 10 years of objective benchmarking , author=. Nature Methods , volume=

work page
[17]

Journal of Fourier Analysis and Applications , volume=

Support recovery for sparse super-resolution of positive measures , author=. Journal of Fourier Analysis and Applications , volume=

work page
[18]

IEEE Transactions on Image Processing , year=

Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM , author=. IEEE Transactions on Image Processing , year=

work page
[19]

Nature Methods , volume=

Efficient Bayesian-based multiview deconvolution , author=. Nature Methods , volume=

work page
[20]

arXiv preprint arXiv:2311.18386 , year=

A novel variational approach for multiphoton microscopy image restoration: from PSF estimation to 3D deconvolution , author=. arXiv preprint arXiv:2311.18386 , year=

work page arXiv
[21]

Foundations of computational mathematics , volume=

Metamorphoses through lie group action , author=. Foundations of computational mathematics , volume=. 2005 , publisher=

work page 2005
[22]

Journal of Fourier Analysis and Applications , volume=

Support recovery for sparse super-resolution of positive measures , author=. Journal of Fourier Analysis and Applications , volume=. 2017 , publisher=

work page 2017
[23]

1963 , publisher=

The theory of branching processes , author=. 1963 , publisher=

work page 1963
[24]

IEEE Transactions on Information Theory , volume=

Entropies and combinatorics of random branching processes and context-free languages , author=. IEEE Transactions on Information Theory , volume=. 2002 , publisher=

work page 2002
[25]

2006 , publisher=

Pattern theory: from representation to inference , author=. 2006 , publisher=

work page 2006
[26]

eLife , volume=

Fast and slow synaptic plasticity enables concurrent control and learning , author=. eLife , volume=. 2025 , publisher=

work page 2025
[27]

Cell , volume=

Synaptic plasticity in the hippocampus: LTP and LTD , author=. Cell , volume=. 1994 , publisher=

work page 1994
[28]

Neuron , author=

AMPARs and synaptic plasticity: the last 25 years , volume=. Neuron , author=. 2013 , month=. doi:10.1016/j.neuron.2013.10.025 , number=

work page doi:10.1016/j.neuron.2013.10.025 2013
[29]

Communications biology , volume=

Hidden Markov modeling for maximum probability neuron reconstruction , author=. Communications biology , volume=. 2022 , publisher=

work page 2022
[30]

Neural computation , volume=

Spike-driven synaptic plasticity: theory, simulation, VLSI implementation , author=. Neural computation , volume=. 2000 , publisher=

work page 2000
[31]

IEEE transactions on pattern analysis and machine intelligence , volume=

Normalizing flows: An introduction and review of current methods , author=. IEEE transactions on pattern analysis and machine intelligence , volume=. 2020 , publisher=

work page 2020
[32]

Nature neuroscience , volume=

Synaptic plasticity as Bayesian inference , author=. Nature neuroscience , volume=. 2021 , publisher=

work page 2021
[33]

A new approach to linear filtering and prediction problems , author=

work page
[34]

IEEE transactions on Automatic Control , volume=

An algorithm for tracking multiple targets , author=. IEEE transactions on Automatic Control , volume=. 1979 , publisher=

work page 1979
[35]

IEEE journal of Oceanic Engineering , volume=

Sonar tracking of multiple targets using joint probabilistic data association , author=. IEEE journal of Oceanic Engineering , volume=. 1983 , publisher=

work page 1983
[36]

Statistics and computing , volume=

On sequential Monte Carlo sampling methods for Bayesian filtering , author=. Statistics and computing , volume=. 2000 , publisher=

work page 2000
[37]

IEEE transactions on pattern analysis and machine intelligence , volume=

Multiple hypothesis tracking for cluttered biological image sequences , author=. IEEE transactions on pattern analysis and machine intelligence , volume=. 2013 , publisher=

work page 2013
[38]

Frontiers in Synaptic Neuroscience , volume=

A synaptic framework for the persistence of memory engrams , author=. Frontiers in Synaptic Neuroscience , volume=. 2021 , publisher=

work page 2021
[39]

Neuron , volume=

Cortical synaptic AMPA receptor plasticity during motor learning , author=. Neuron , volume=. 2020 , publisher=

work page 2020
[40]

2008 IEEE conference on computer vision and pattern recognition , pages=

Global data association for multi-object tracking using network flows , author=. 2008 IEEE conference on computer vision and pattern recognition , pages=. 2008 , organization=

work page 2008
[41]

Fowlkes , title =

Hamed Pirsiavash and Deva Ramanan and Charless C. Fowlkes , title =. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pages =. 2011 , month =

work page 2011
[42]

Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

Multi-target tracking by lagrangian relaxation to min-cost network flow , author=. Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

work page
[43]

IEEE transactions on medical imaging , volume=

Global linking of cell tracks using the Viterbi algorithm , author=. IEEE transactions on medical imaging , volume=. 2014 , publisher=

work page 2014
[44]

IEEE transactions on pattern analysis and machine intelligence , volume=

Multiple object tracking using k-shortest paths optimization , author=. IEEE transactions on pattern analysis and machine intelligence , volume=. 2011 , publisher=

work page 2011
[45]

Advances in neural information processing systems , volume=

muSSP: Efficient min-cost flow algorithm for multi-object tracking , author=. Advances in neural information processing systems , volume=

work page
[46]

Medical image analysis , volume=

Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis , author=. Medical image analysis , volume=. 2011 , publisher=

work page 2011
[47]

PloS one , volume=

A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction , author=. PloS one , volume=. 2021 , publisher=

work page 2021
[48]

EURASIP Journal on Image and Video Processing , volume=

Evaluating multiple object tracking performance: the clear mot metrics , author=. EURASIP Journal on Image and Video Processing , volume=. 2008 , publisher=

work page 2008
[49]

Elife , volume=

Tracking cell lineages in 3D by incremental deep learning , author=. Elife , volume=. 2022 , publisher=

work page 2022
[50]

Medical image analysis , volume=

A probabilistic approach to joint cell tracking and segmentation in high-throughput microscopy videos , author=. Medical image analysis , volume=. 2018 , publisher=

work page 2018
[51]

IEEE transactions on medical imaging , volume=

Network flow integer programming to track elliptical cells in time-lapse sequences , author=. IEEE transactions on medical imaging , volume=. 2016 , publisher=

work page 2016
[52]

A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects

A multi-cut formulation for joint segmentation and tracking of multiple objects , author=. arXiv preprint arXiv:1607.06317 , year=

work page internal anchor Pith review Pith/arXiv arXiv
[53]

Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

Multiple people tracking by lifted multicut and person re-identification , author=. Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

work page
[54]

Bioinformatics , volume=

Graphical model for joint segmentation and tracking of multiple dividing cells , author=. Bioinformatics , volume=. 2015 , publisher=

work page 2015
[55]

Advances in neural information processing systems , volume=

Neural ordinary differential equations , author=. Advances in neural information processing systems , volume=

work page
[56]

IEEE Transactions on Medical Imaging , volume=

Dual-task ConvLSTM-UNet for instance segmentation of weakly annotated microscopy videos , author=. IEEE Transactions on Medical Imaging , volume=. 2022 , publisher=

work page 2022
[57]

European Conference on Computer Vision , pages=

Graph neural network for cell tracking in microscopy videos , author=. European Conference on Computer Vision , pages=. 2022 , organization=

work page 2022
[58]

Frontiers in Bioengineering and Biotechnology , volume=

Deep reinforcement learning for data association in cell tracking , author=. Frontiers in Bioengineering and Biotechnology , volume=. 2020 , publisher=

work page 2020
[59]

Elife , volume=

3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cells in 3D time lapse images , author=. Elife , volume=. 2021 , publisher=

work page 2021
[60]

2018 IEEE International Conference on Big Data (Big Data) , pages=

Toward simple & scalable 3D cell tracking , author=. 2018 IEEE International Conference on Big Data (Big Data) , pages=. 2018 , organization=

work page 2018
[61]

Nature methods , volume=

Robust single-particle tracking in live-cell time-lapse sequences , author=. Nature methods , volume=. 2008 , publisher=

work page 2008
[62]

European Conference on Computer Vision , pages=

Large-scale multi-hypotheses cell tracking using ultrametric contours maps , author=. European Conference on Computer Vision , pages=. 2024 , organization=

work page 2024
[63]

Nature methods , volume=

An objective comparison of cell-tracking algorithms , author=. Nature methods , volume=. 2017 , publisher=

work page 2017
[64]

Nature Methods , volume=

The cell tracking challenge: 10 years of objective benchmarking , author=. Nature Methods , volume=. 2023 , publisher=

work page 2023
[65]

Cell , volume=

Brain-wide neural activity underlying memory-guided movement , author=. Cell , volume=. 2024 , publisher=

work page 2024
[66]

Cell , volume=

Reconstruction of 1,000 projection neurons reveals new cell types and organization of long-range connectivity in the mouse brain , author=. Cell , volume=. 2019 , publisher=

work page 2019
[67]

Nature methods , volume=

Deep tissue two-photon microscopy , author=. Nature methods , volume=. 2005 , publisher=

work page 2005
[68]

IEEE transactions on image processing , volume=

Landmark matching via large deformation diffeomorphisms , author=. IEEE transactions on image processing , volume=. 2000 , publisher=

work page 2000
[69]

Medical image analysis , volume=

A robust point-matching algorithm for autoradiograph alignment , author=. Medical image analysis , volume=. 1997 , publisher=

work page 1997
[70]

Computer Vision and Image Understanding , volume=

A new point matching algorithm for non-rigid registration , author=. Computer Vision and Image Understanding , volume=. 2003 , publisher=

work page 2003
[71]

Medical image analysis , volume=

A unified non-rigid feature registration method for brain mapping , author=. Medical image analysis , volume=. 2003 , publisher=

work page 2003
[72]

Computer Vision and Image Understanding , volume=

An efficient EM-ICP algorithm for non-linear registration of large 3D point sets , author=. Computer Vision and Image Understanding , volume=. 2020 , publisher=

work page 2020
[73]

Optimal transport for diffeomorphic registration , author=. Medical Image Computing and Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part I 20 , pages=. 2017 , organization=

work page 2017
[74]

Computer Vision—ECCV 2002: 7th European Conference on Computer Vision Copenhagen, Denmark, May 28--31, 2002 Proceedings, Part IV 7 , pages=

Multi-scale EM-ICP: A fast and robust approach for surface registration , author=. Computer Vision—ECCV 2002: 7th European Conference on Computer Vision Copenhagen, Denmark, May 28--31, 2002 Proceedings, Part IV 7 , pages=. 2002 , organization=

work page 2002
[75]

Proceedings IEEE workshop on mathematical methods in biomedical image analysis

A feature registration framework using mixture models , author=. Proceedings IEEE workshop on mathematical methods in biomedical image analysis. MMBIA-2000 (Cat. No. PR00737) , pages=. 2000 , organization=

work page 2000
[76]

IEEE transactions on pattern analysis and machine intelligence , volume=

Point set registration: Coherent point drift , author=. IEEE transactions on pattern analysis and machine intelligence , volume=. 2010 , publisher=

work page 2010
[77]

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages=

Deep network flow for multi-object tracking , author=. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages=

work page
[78]

Efficient implementations of minimum-cost flow algorithms

Efficient implementations of minimum-cost flow algorithms , author=. arXiv preprint arXiv:1207.6381 , year=

work page internal anchor Pith review Pith/arXiv arXiv
[79]

Annals of Mathematics , volume=

Groups of diffeomorphisms and the motion of an incompressible fluid , author=. Annals of Mathematics , volume=. 1970 , doi=

work page 1970
[80]

2010 , doi=

Shapes and Diffeomorphisms , author=. 2010 , doi=

work page 2010

Showing first 80 references.