Computational Methods for Neuron Segmentation in Two-Photon Calcium Imaging Data: A Survey

Abbas, Waseem; Masip, David

doi:10.3390/app12146876

Cited by 3 publications

(3 citation statements)

References 86 publications

(100 reference statements)

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“…Current methods of neuron segmentation have a trade-off between accuracy and manual effort: supervised methods have superior accuracy but require substantial manual effort to generate ground truth labels for each imaging condition ( Abbas and Masip, 2022 ). This work developed SAND, the first semi-supervised pipeline to segment active neurons from two-photon calcium recordings with limited ground truth labels.…”

Section: Discussionmentioning

confidence: 99%

“…Both supervised and unsupervised machine learning methods exist for neuron segmentation ( Abbas and Masip, 2022 ; Bao and Gong, 2023 ). Supervised methods consist of convolutional neural networks (CNNs), while unsupervised methods include dictionary learning, PCA/ICA, and matrix factorization [e.g., CaImAn ( Giovannucci et al, 2019 ) and Suite2p ( Pachitariu et al, 2017 )].…”

Section: Introductionmentioning

confidence: 99%

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A Semi-supervised Pipeline for Accurate Neuron Segmentation with Fewer Ground Truth Labels

Baker,

Gong

2024

eNeuro

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Recent advancements in two-photon calcium imaging have enabled scientists to record the activity of thousands of neurons with cellular resolution. This scope of data collection is crucial to understanding the next generation of neuroscience questions, but analyzing these large recordings requires automated methods for neuron segmentation. Supervised methods for neuron segmentation achieve state of-the-art-accuracy and speed, but currently require large amounts of manually generated ground truth training labels. We reduced the required number of training labels by designing a semi-supervised pipeline. Our pipeline used neural network ensembling to generate pseudolabels to train a single shallow U-Net. We tested our method on three publicly available datasets and compared our performance to three widely-used segmentation methods. Our method outperformed other methods when trained on a small number of ground truth labels and could achieve state-of-the-art accuracy after training on approximately a quarter of the number of ground truth labels as supervised methods. When trained on many ground truth labels, our pipeline attained higher accuracy than that of state-of-the-art methods. Overall, our work will help researchers accurately process large neural recordings while minimizing the time and effort needed to generate manual labels.Significance statementModern neuroscience analyzes the activity of hundreds to thousands of neurons from large optical imaging datasets. One important step in this analysis is neuron segmentation. Supervised algorithms have performed neuron segmentation with class-leading accuracy and speed but lag unsupervised algorithms in training time. A large component of training time is the manual labeling of neurons as training samples; current supervised methods train over many manual labels to achieve accurate prediction. We developed a semi-supervised neuron segmentation algorithm, SAND, that retained high accuracy in the few-label regime. SAND employed neural network ensembling to generate robust pseudolabels and used domain-specific hyperparameter optimization. SAND was more accurate than existing supervised and unsupervised algorithms in low and high label regimes of multiple imaging conditions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Semi-supervised Pipeline for Accurate Neuron Segmentation with Fewer Ground Truth Labels

Baker,

Gong

2024

eNeuro

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“…There are already several algorithms for registration and ROI segmentation of neural cell bodies in two-photon calcium imaging data. 71 Here we summarize particularly important papers on real-time use. Mitani and Komiyama 48 completed motion correction of 1000

video frames in

and showed that such processing can be applied to BMIs.…”

Section: Brain Machine Interfaces Using In Vivo Mu...mentioning

confidence: 99%

Closed-loop experiments and brain machine interfaces with multiphoton microscopy

Hira

2024

Neurophoton.

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. In the field of neuroscience, the importance of constructing closed-loop experimental systems has increased in conjunction with technological advances in measuring and controlling neural activity in live animals. We provide an overview of recent technological advances in the field, focusing on closed-loop experimental systems where multiphoton microscopy—the only method capable of recording and controlling targeted population activity of neurons at a single-cell resolution in vivo —works through real-time feedback. Specifically, we present some examples of brain machine interfaces (BMIs) using in vivo two-photon calcium imaging and discuss applications of two-photon optogenetic stimulation and adaptive optics to real-time BMIs. We also consider conditions for realizing future optical BMIs at the synaptic level, and their possible roles in understanding the computational principles of the brain.

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CalciSeg: A versatile approach for unsupervised segmentation of calcium imaging data

Günzel,

Couzin-Fuchs,

Paoli

2024

NeuroImage

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Computational Methods for Neuron Segmentation in Two-Photon Calcium Imaging Data: A Survey

Cited by 3 publications

References 86 publications

A Semi-supervised Pipeline for Accurate Neuron Segmentation with Fewer Ground Truth Labels

A Semi-supervised Pipeline for Accurate Neuron Segmentation with Fewer Ground Truth Labels

Closed-loop experiments and brain machine interfaces with multiphoton microscopy

CalciSeg: A versatile approach for unsupervised segmentation of calcium imaging data

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