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7 Janelia Publications

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    11/07/22 | Cellpose 2.0: how to train your own model.
    Pachitariu M, Stringer C
    Nature Methods. 2022 Nov 07:. doi: 10.1038/s41592-022-01663-4

    Pretrained neural network models for biological segmentation can provide good out-of-the-box results for many image types. However, such models do not allow users to adapt the segmentation style to their specific needs and can perform suboptimally for test images that are very different from the training images. Here we introduce Cellpose 2.0, a new package that includes an ensemble of diverse pretrained models as well as a human-in-the-loop pipeline for rapid prototyping of new custom models. We show that models pretrained on the Cellpose dataset can be fine-tuned with only 500-1,000 user-annotated regions of interest (ROI) to perform nearly as well as models trained on entire datasets with up to 200,000 ROI. A human-in-the-loop approach further reduced the required user annotation to 100-200 ROI, while maintaining high-quality segmentations. We provide software tools such as an annotation graphical user interface, a model zoo and a human-in-the-loop pipeline to facilitate the adoption of Cellpose 2.0.

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    11/04/22 | Facemap: a framework for modeling neural activity based on orofacial tracking
    Atika Syeda , Lin Zhong , Renee Tung , Will Long , Marius Pachitariu , Carsen Stringer
    bioRxiv. 2022 Nov 04:. doi: 10.1101/2022.11.03.515121

    Recent studies in mice have shown that orofacial behaviors drive a large fraction of neural activity across the brain. To understand the nature and function of these signals, we need better computational models to characterize the behaviors and relate them to neural activity. Here we developed Facemap, a framework consisting of a keypoint tracking algorithm and a deep neural network encoder for predicting neural activity. We used the Facemap keypoints as input for the deep neural network to predict the activity of ∼50,000 simultaneously-recorded neurons and in visual cortex we doubled the amount of explained variance compared to previous methods. Our keypoint tracking algorithm was more accurate than existing pose estimation tools, while the inference speed was several times faster, making it a powerful tool for closed-loop behavioral experiments. The Facemap tracker was easy to adapt to data from new labs, requiring as few as 10 annotated frames for near-optimal performance. We used Facemap to find that the neuronal activity clusters which were highly driven by behaviors were more spatially spread-out across cortex. We also found that the deep keypoint features inferred by the model had time-asymmetrical state dynamics that were not apparent in the raw keypoint data. In summary, Facemap provides a stepping stone towards understanding the function of the brainwide neural signals and their relation to behavior.

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    10/17/22 | Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation.
    Cutler KJ, Stringer C, Lo TW, Rappez L, Stroustrup N, Brook Peterson S, Wiggins PA, Mougous JD
    Nature Methods. 2022 Oct 17:. doi: 10.1038/s41592-022-01639-4

    Advances in microscopy hold great promise for allowing quantitative and precise measurement of morphological and molecular phenomena at the single-cell level in bacteria; however, the potential of this approach is ultimately limited by the availability of methods to faithfully segment cells independent of their morphological or optical characteristics. Here, we present Omnipose, a deep neural network image-segmentation algorithm. Unique network outputs such as the gradient of the distance field allow Omnipose to accurately segment cells on which current algorithms, including its predecessor, Cellpose, produce errors. We show that Omnipose achieves unprecedented segmentation performance on mixed bacterial cultures, antibiotic-treated cells and cells of elongated or branched morphology. Furthermore, the benefits of Omnipose extend to non-bacterial subjects, varied imaging modalities and three-dimensional objects. Finally, we demonstrate the utility of Omnipose in the characterization of extreme morphological phenotypes that arise during interbacterial antagonism. Our results distinguish Omnipose as a powerful tool for characterizing diverse and arbitrarily shaped cell types from imaging data.

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    10/05/22 | Not so spontaneous: Multi-dimensional representations of behaviors and context in sensory areas.
    Avitan L, Stringer C
    Neuron. 2022 Oct 05;110(19):3064. doi: 10.1016/j.neuron.2022.06.019

    Sensory areas are spontaneously active in the absence of sensory stimuli. This spontaneous activity has long been studied; however, its functional role remains largely unknown. Recent advances in technology, allowing large-scale neural recordings in the awake and behaving animal, have transformed our understanding of spontaneous activity. Studies using these recordings have discovered high-dimensional spontaneous activity patterns, correlation between spontaneous activity and behavior, and dissimilarity between spontaneous and sensory-driven activity patterns. These findings are supported by evidence from developing animals, where a transition toward these characteristics is observed as the circuit matures, as well as by evidence from mature animals across species. These newly revealed characteristics call for the formulation of a new role for spontaneous activity in neural sensory computation.

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    07/27/22 | Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation
    Kevin J. Cutler , Carsen Stringer , Paul A. Wiggins , Joseph D. Mougous
    bioRxiv. 2022 Jul 27:. doi: 10.1101/2021.11.03.467199

    Advances in microscopy hold great promise for allowing quantitative and precise readouts of morphological and molecular phenomena at the single cell level in bacteria. However, the potential of this approach is ultimately limited by the availability of methods to perform unbiased cell segmentation, defined as the ability to faithfully identify cells independent of their morphology or optical characteristics. In this study, we present a new algorithm, Omnipose, which accurately segments samples that present significant challenges to current algorithms, including mixed bacterial cultures, antibiotic-treated cells, and cells of extended or branched morphology. We show that Omnipose achieves generality and performance beyond leading algorithms and its predecessor, Cellpose, by virtue of unique neural network outputs such as the gradient of the distance field. Finally, we demonstrate the utility of Omnipose in the characterization of extreme morphological phenotypes that arise during interbacterial antagonism and on the segmentation of non-bacterial objects. Our results distinguish Omnipose as a uniquely powerful tool for answering diverse questions in bacterial cell biology.

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    04/05/22 | Cellpose 2.0: how to train your own model
    Stringer C, Pachitariu M
    bioRxiv. 2022 Apr 05:. doi: 10.1101/2022.04.01.486764

    Generalist models for cellular segmentation, like Cellpose, provide good out-of-the-box results for many types of images. However, such models do not allow users to adapt the segmentation style to their specific needs and may perform sub-optimally for test images that are very different from the training images. Here we introduce Cellpose 2.0, a new package which includes an ensemble of diverse pretrained models as well as a human-in-the-loop pipeline for quickly prototyping new specialist models. We show that specialist models pretrained on the Cellpose dataset can achieve state-of-the-art segmentation on new image categories with very little user-provided training data. Models trained on 500-1000 segmented regions-of-interest (ROIs) performed nearly as well as models trained on entire datasets with up to 200,000 ROIs. A human-in-the-loop approach further reduced the required user annotations to 100-200 ROIs, while maintaining state-of-the-art segmentation performance. This approach enables a new generation of specialist segmentation models that can be trained on new image types with only 1-2 hours of user effort. We provide software tools including an annotation GUI, a model zoo and a human-in-the-loop pipeline to facilitate the adoption of Cellpose 2.0.

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    02/23/22 | The importance of accounting for movement when relating neuronal activity to sensory and cognitive processes.
    Edward Zagha , Jeffrey C Erlich , Soohyun Lee , Gyorgy Lur , Daniel H O'Connor , Nicholas A Steinmetz , Carsen Stringer , Hongdian Yang
    Journal of Neuroscience: The Official Journal of the Society for Neuroscience. 2022 Feb 23;42(8):1375-1382. doi: 10.1523/JNEUROSCI.1919-21.2021

    A surprising finding of recent studies in mouse is the dominance of widespread movement-related activity throughout the brain, including in early sensory areas. In awake subjects, failing to account for movement risks misattributing movement-related activity to other (e.g., sensory or cognitive) processes. In this article, we 1) review task designs for separating task-related and movement-related activity, 2) review three 'case studies' in which not considering movement would have resulted in critically different interpretations of neuronal function, and 3) discuss functional couplings that may prevent us from ever fully isolating sensory, motor, and cognitive-related activity. Our main thesis is that neural signals related to movement are ubiquitous, and therefore ought to be considered first and foremost when attempting to correlate neuronal activity with task-related processes.

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