MPM: Joint Representation of Motion and Position Map for Cell Tracking

Hayashida, Junya; Nishimura, Kohei; Bise, Ryoma

doi:10.1109/cvpr42600.2020.00388

Cited by 32 publications

(43 citation statements)

References 29 publications

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“…These estimated coordinates are subsequently used to find the parent of the nucleus at the previous timepoint by the nearest neighbor algorithm (a similar concept was introduced for 2D phase contrast microscopy data; (Hayashida & Bise, 2019;Hayashida, Nishimura, & Bise, 2020)). The pairs with a distance smaller than are considered as link candidates, where the closer the Euclidean distance between the two points, the higher their priority of being the correct link.…”

Section: Algorithm For Detectionmentioning

confidence: 99%

Tracking cell lineages in 3D by incremental deep learning

Sugawara

Çevrim

Averof

2021

Preprint

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Deep learning is emerging as a powerful approach for bioimage analysis, but its wider use is limited by the scarcity of annotated data for training. We present ELEPHANT, an interactive platform for cell tracking in 4D that seamlessly integrates annotation, deep learning, and proofreading. ELEPHANT's user interface supports cycles of incremental learning starting from sparse annotations, yielding accurate, user-validated cell lineages with a modest investment in time and effort.

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Section: Algorithm For Detectionmentioning

confidence: 99%

Tracking cell lineages in 3D by incremental deep learning

Sugawara

Çevrim

Averof

2021

Preprint

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“…Convolutional neural networks (CNNs) have proven to efficiently process microscope images of cell cultures [1,2]. CNN can perform image de-noising [3,4], cell detection [5], cell segmentation [6], cell virtual staining [7], cell classification [8], cell motility estimation [9], and so on. To date, CNNs outperform standard image processing algorithms in terms of performance and computation speed.…”

Section: Introductionmentioning

confidence: 99%

CNN-Based Cell Analysis: From Image to Quantitative Representation

et al. 2022

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We present a novel deep learning-based quantification pipeline for the analysis of cell culture images acquired by lens-free microscopy. The image reconstruction part of the pipeline features a convolutional neural network performing phase unwrapping and accelerating the inverse problem optimization. It allows phase retrieval at the 4K level (3,840 × 2,748 pixels) in 3 s. The analysis part of the pipeline features a suite of convolutional neural networks estimating different cell metrics from the reconstructed image, that is, cell surface area, cell dry mass, cell length, and cell thickness. The networks have been trained to predict quantitative representation of the cell measurements that can be next translated into measurement lists with a local maxima algorithm. In this article, we discuss the performance and limitations of this novel deep learning-based quantification pipeline in comparison with a standard image processing solution. The main advantage brought by this method is the fast processing time, that is, the analysis rate of ∼25.000 cells measurements per second. Although our proof of principle has been established with lens-free microscopy, the approach of using quantitative cell representation in a deep learning framework can be similarly applied to other microscopy techniques.

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“…A simple linking measure is the Euclidean distance between the positions of the cell centroids. Other linking measures are based on handcrafted features, such as position and appearance [25, 30, 37, 38], features of the cell’s neighborhood [39], features derived from a graph structure [40], or learned features [18, 19, 26, 27, 41]. The contribution of the extracted features in the measure is often learned for instance by using logistic regression [30], a structured support vector machine [42], a random forest [33] or training convolutional neural networks [18, 19].…”

Section: Introductionmentioning

confidence: 99%

A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction

Löffler

Scherr

Mikut

2021

Preprint

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Automatic cell segmentation and tracking enables to gain quantitative insights into the processes driving cell migration. To investigate new data with minimal manual effort, cell tracking algorithms should be easy to apply and reduce manual curation time by providing automatic correction of segmentation errors. Current cell tracking algorithms, however, are either easy to apply to new data sets but lack automatic segmentation error correction, or have a vast set of parameters that needs either manual tuning or annotated data for parameter tuning. In this work, we propose a tracking algorithm with only few manually tunable parameters and automatic segmentation error correction. Moreover, no training data is needed. We investigate the performance of our approach by simulating erroneous segmentation data, including false negatives, over- and under-segmentation errors, on 2D and 3D cell data sets. We compare our approach against three well-performing tracking algorithms from the Cell Tracking Challenge. Our tracking algorithm can correct false negatives, over- and under-segmentation errors as well as a mixture of the aforementioned segmentation errors. Furthermore, in case of under-segmentation or a combination of segmentation errors our approach outperforms the other tracking approaches.

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MPM: Joint Representation of Motion and Position Map for Cell Tracking

Cited by 32 publications

References 29 publications

Tracking cell lineages in 3D by incremental deep learning

Tracking cell lineages in 3D by incremental deep learning

CNN-Based Cell Analysis: From Image to Quantitative Representation

A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction

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