Tracking one-in-a-million: Large-scale benchmark for microbial single-cell tracking with experiment-aware robustness metrics

• URL: http://arxiv.org/abs/2411.00552v1
• Date: Fri, 01 Nov 2024 13:03:51 GMT
• Title: Tracking one-in-a-million: Large-scale benchmark for microbial single-cell tracking with experiment-aware robustness metrics
• Authors: J. Seiffarth, L. Blöbaum, R. D. Paul, N. Friederich, A. J. Yamachui Sitcheu, R. Mikut, H. Scharr, A. Grünberger, K. Nöh,
• Abstract summary: We present the largest publicly available and annotated dataset for microbial live-cell imaging (MLCI) This dataset contains more than 1.4 million cell instances, 29k cell tracks, and 14k cell divisions. Our new benchmark quantifies the influence of experiment parameters on the tracking quality, and gives the opportunity to develop new data-driven methods.
• Score: 0.0
• License:
• Abstract: Tracking the development of living cells in live-cell time-lapses reveals crucial insights into single-cell behavior and presents tremendous potential for biomedical and biotechnological applications. In microbial live-cell imaging (MLCI), a few to thousands of cells have to be detected and tracked within dozens of growing cell colonies. The challenge of tracking cells is heavily influenced by the experiment parameters, namely the imaging interval and maximal cell number. For now, tracking benchmarks are not widely available in MLCI and the effect of these parameters on the tracking performance are not yet known. Therefore, we present the largest publicly available and annotated dataset for MLCI, containing more than 1.4 million cell instances, 29k cell tracks, and 14k cell divisions. With this dataset at hand, we generalize existing tracking metrics to incorporate relevant imaging and experiment parameters into experiment-aware metrics. These metrics reveal that current cell tracking methods crucially depend on the choice of the experiment parameters, where their performance deteriorates at high imaging intervals and large cell colonies. Thus, our new benchmark quantifies the influence of experiment parameters on the tracking quality, and gives the opportunity to develop new data-driven methods that generalize across imaging and experiment parameters. The benchmark dataset is publicly available at https://zenodo.org/doi/10.5281/zenodo.7260136.

Related papers

• Cell as Point: One-Stage Framework for Efficient Cell Tracking [54.19259129722988]
  This paper proposes the novel end-to-end CAP framework to achieve efficient and stable cell tracking in one stage. CAP abandons detection or segmentation stages and simplifies the process by exploiting the correlation among the trajectories of cell points to track cells jointly. Cap demonstrates strong cell tracking performance while also being 10 to 55 times more efficient than existing methods.
  arXiv  Detail & Related papers  (2024-11-22T10:16:35Z)
• EAP4EMSIG -- Experiment Automation Pipeline for Event-Driven Microscopy to Smart Microfluidic Single-Cells Analysis [1.8258105145031496]
  We introduce the Experiment Automation Pipeline for Event-Driven Microscopy to Smart Microfluidic Single-Cells Analysis (EAP4IG) In particular, we present initial zero-shot results from the real-time segmentation module of our approach. Our findings indicate that among four State-Of-The- Art (SOTA) segmentation methods evaluated, Omnipose delivers the highest Panoptic Quality (PQ) score of 0.9336, while Contour Proposal Network (CPN) achieves the fastest inference time of 185 ms.
  arXiv  Detail & Related papers  (2024-11-06T09:37:31Z)
• Scaling Wearable Foundation Models [54.93979158708164]
  We investigate the scaling properties of sensor foundation models across compute, data, and model size. Using a dataset of up to 40 million hours of in-situ heart rate, heart rate variability, electrodermal activity, accelerometer, skin temperature, and altimeter per-minute data from over 165,000 people, we create LSM. Our results establish the scaling laws of LSM for tasks such as imputation, extrapolation, both across time and sensor modalities.
  arXiv  Detail & Related papers  (2024-10-17T15:08:21Z)
• The TYC Dataset for Understanding Instance-Level Semantics and Motions of Cells in Microstructures [29.29348484938194]
  trapped yeast cell (TYC) dataset is a novel dataset for understanding instance-level semantics and motions of cells in microstructures. TYC offers ten times more instance annotations than the previously largest dataset, including cells and microstructures.
  arXiv  Detail & Related papers  (2023-08-23T13:10:33Z)
• AMIGO: Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-pixel Images [53.29794593104923]
  We present a novel concept of shared-context processing for whole slide histopathology images. AMIGO uses the celluar graph within the tissue to provide a single representation for a patient. We show that our model is strongly robust to missing information to an extent that it can achieve the same performance with as low as 20% of the data.
  arXiv  Detail & Related papers  (2023-03-01T23:37:45Z)
• Cell tracking for live-cell microscopy using an activity-prioritized assignment strategy [0.9134244356393666]
  Cell tracking is an essential tool in live-cell imaging to determine single-cell features, such as division patterns or elongation rates. In microbial live-cell experiments cells are growing, moving, and dividing over time, to form cell colonies that are densely packed in mono-layer structures. We propose a fast-prioritized cell tracking approach, which consists of activity-prioritized nearest neighbor assignment of growing cells and a solver that assigns splitting mother cells to their daughters.
  arXiv  Detail & Related papers  (2022-10-20T17:40:31Z)
• A biology-driven deep generative model for cell-type annotation in cytometry [0.0]
  We introduce Scyan, a Single-cell Cytometry Network that automatically annotates cell types using only prior expert knowledge. Scyan significantly outperforms the related state-of-the-art models on multiple public datasets while being faster and interpretable. In addition, Scyan overcomes several complementary tasks such as batch-effect removal, debarcoding, and population discovery.
  arXiv  Detail & Related papers  (2022-08-11T10:50:44Z)
• Deep neural networks approach to microbial colony detection -- a comparative analysis [52.77024349608834]
  This study investigates the performance of three deep learning approaches for object detection on the AGAR dataset. The achieved results may serve as a benchmark for future experiments.
  arXiv  Detail & Related papers  (2021-08-23T12:06:00Z)
• Stochastic Neural Networks for Automatic Cell Tracking in Microscopy Image Sequences of Bacterial Colonies [0.0]
  We describe an automated analysis method to quantify the detailed growth dynamics of a population of bacilliform bacteria. We propose an innovative approach to frame-sequence tracking of deformable-cell motion by the automated minimization of a new, specific cost functional. We validate this automatic cell tracking algorithm using recordings of simulated cell colonies that closely mimic the growth dynamics of emphE. coli in microfluidic traps.
  arXiv  Detail & Related papers  (2021-04-27T21:24:32Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• Split and Expand: An inference-time improvement for Weakly Supervised Cell Instance Segmentation [71.50526869670716]
  We propose a two-step post-processing procedure, Split and Expand, to improve the conversion of segmentation maps to instances. In the Split step, we split clumps of cells from the segmentation map into individual cell instances with the guidance of cell-center predictions. In the Expand step, we find missing small cells using the cell-center predictions.
  arXiv  Detail & Related papers  (2020-07-21T14:05:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.