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Li Z, Jian Y, Hu J, Zhang C, Meng X, Liu J. SpindlesTracker: An Automatic and Low-Cost Labeled Workflow for Spindle Analysis. IEEE J Biomed Health Inform 2023; 27:4098-4109. [PMID: 37252866 DOI: 10.1109/jbhi.2023.3281454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Quantitative analysis of spindle dynamics in mitosis through fluorescence microscopy requires tracking spindle elongation in noisy image sequences. Deterministic methods, which use typical microtubule detection and tracking methods, perform poorly in the sophisticated background of spindles. In addition, the expensive data labeling cost also limits the application of machine learning in this field. Here we present a fully automatic and low-cost labeled workflow that efficiently analyzes the dynamic spindle mechanism of time-lapse images, called SpindlesTracker. In this workflow, we design a network named YOLOX-SP which can accurately detect the location and endpoint of each spindle under box-level data supervision. We then optimize the algorithm SORT and MCP for spindle's tracking and skeletonization. As there was no publicly available dataset, we annotated a S.pombe dataset that was entirely acquired from the real world for both training and evaluation. Extensive experiments demonstrate that SpindlesTracker achieves excellent performance in all aspects, while reducing label costs by 60%. Specifically, it achieves 84.1% mAP in spindle detection and over 90% accuracy in endpoint detection. Furthermore, the improved algorithm enhances tracking accuracy by 1.3% and tracking precision by 6.5%. Statistical results also indicate that the mean error of spindle length is within 1 μm. In summary, SpindlesTracker holds significant implications for the study of mitotic dynamic mechanisms and can be readily extended to the analysis of other filamentous objects. The code and the dataset are both released on GitHub.
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Park SA, Sipka T, Krivá Z, Lutfalla G, Nguyen-Chi M, Mikula K. Segmentation-based tracking of macrophages in 2D+time microscopy movies inside a living animal. Comput Biol Med 2023; 153:106499. [PMID: 36599208 DOI: 10.1016/j.compbiomed.2022.106499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/19/2022] [Accepted: 12/27/2022] [Indexed: 12/31/2022]
Abstract
The automated segmentation and tracking of macrophages during their migration are challenging tasks due to their dynamically changing shapes and motions. This paper proposes a new algorithm to achieve automatic cell tracking in time-lapse microscopy macrophage data. First, we design a segmentation method employing space-time filtering, local Otsu's thresholding, and the SUBSURF (subjective surface segmentation) method. Next, the partial trajectories for cells overlapping in the temporal direction are extracted in the segmented images. Finally, the extracted trajectories are linked by considering their direction of movement. The segmented images and the obtained trajectories from the proposed method are compared with those of the semi-automatic segmentation and manual tracking. The proposed tracking achieved 97.4% of accuracy for macrophage data under challenging situations, feeble fluorescent intensity, irregular shapes, and motion of macrophages. We expect that the automatically extracted trajectories of macrophages can provide pieces of evidence of how macrophages migrate depending on their polarization modes in the situation, such as during wound healing.
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Affiliation(s)
- Seol Ah Park
- Department of Mathematics and Descriptive Geometry, Slovak University of Technology in Bratislava, Radlinskeho 11, Bratislava, 810 05, Slovakia.
| | - Tamara Sipka
- LPHI Laboratory of Pathogen Host Interaction, CNRS, Univ. Montpellier, Place E.Bataillon-Building 24, 34095, Montpellier Cedex 05, France.
| | - Zuzana Krivá
- Department of Mathematics and Descriptive Geometry, Slovak University of Technology in Bratislava, Radlinskeho 11, Bratislava, 810 05, Slovakia.
| | - Georges Lutfalla
- LPHI Laboratory of Pathogen Host Interaction, CNRS, Univ. Montpellier, Place E.Bataillon-Building 24, 34095, Montpellier Cedex 05, France.
| | - Mai Nguyen-Chi
- LPHI Laboratory of Pathogen Host Interaction, CNRS, Univ. Montpellier, Place E.Bataillon-Building 24, 34095, Montpellier Cedex 05, France.
| | - Karol Mikula
- Department of Mathematics and Descriptive Geometry, Slovak University of Technology in Bratislava, Radlinskeho 11, Bratislava, 810 05, Slovakia.
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Østerlund I, Persson S, Nikoloski Z. Tracing and tracking filamentous structures across scales: A systematic review. Comput Struct Biotechnol J 2022; 21:452-462. [PMID: 36618983 PMCID: PMC9804014 DOI: 10.1016/j.csbj.2022.12.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/14/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Filamentous structures are ubiquitous in nature, are studied in diverse scientific fields, and span vastly different spatial scales. Filamentous structures in biological systems fulfill different functions and often form dynamic networks that respond to perturbations. Therefore, characterizing the properties of filamentous structures and the networks they form is important to gain better understanding of systems level functions and dynamics. Filamentous structures are captured by various imaging technologies, and analysis of the resulting imaging data addresses two problems: (i) identification (tracing) of filamentous structures in a single snapshot and (ii) characterizing the dynamics (i.e., tracking) of filamentous structures over time. Therefore, considerable research efforts have been made in developing automated methods for tracing and tracking of filamentous structures. Here, we provide a systematic review in which we present, categorize, and discuss the state-of-the-art methods for tracing and tracking of filamentous structures in sparse and dense networks. We highlight the mathematical approaches, assumptions, and constraints particular for each method, allowing us to pinpoint outstanding challenges and offer perspectives for future research aimed at gaining better understanding of filamentous structures in biological systems.
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Affiliation(s)
- Isabella Østerlund
- Department of Plant and Environmental Sciences, University of Copenhagen, 1871 Frederiksberg, Denmark,,Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany
| | - Staffan Persson
- Department of Plant and Environmental Sciences, University of Copenhagen, 1871 Frederiksberg, Denmark
| | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany,Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany,Corresponding author at: Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany.
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Liu Z, Jin L, Chen J, Fang Q, Ablameyko S, Yin Z, Xu Y. A survey on applications of deep learning in microscopy image analysis. Comput Biol Med 2021; 134:104523. [PMID: 34091383 DOI: 10.1016/j.compbiomed.2021.104523] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 05/13/2021] [Accepted: 05/17/2021] [Indexed: 01/12/2023]
Abstract
Advanced microscopy enables us to acquire quantities of time-lapse images to visualize the dynamic characteristics of tissues, cells or molecules. Microscopy images typically vary in signal-to-noise ratios and include a wealth of information which require multiple parameters and time-consuming iterative algorithms for processing. Precise analysis and statistical quantification are often needed for the understanding of the biological mechanisms underlying these dynamic image sequences, which has become a big challenge in the field. As deep learning technologies develop quickly, they have been applied in bioimage processing more and more frequently. Novel deep learning models based on convolution neural networks have been developed and illustrated to achieve inspiring outcomes. This review article introduces the applications of deep learning algorithms in microscopy image analysis, which include image classification, region segmentation, object tracking and super-resolution reconstruction. We also discuss the drawbacks of existing deep learning-based methods, especially on the challenges of training datasets acquisition and evaluation, and propose the potential solutions. Furthermore, the latest development of augmented intelligent microscopy that based on deep learning technology may lead to revolution in biomedical research.
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Affiliation(s)
- Zhichao Liu
- Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, 310027, China; Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 310058, China
| | - Luhong Jin
- Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, 310027, China; Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 310058, China
| | - Jincheng Chen
- Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, 310027, China; Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 310058, China
| | - Qiuyu Fang
- Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, 310027, China
| | - Sergey Ablameyko
- National Academy of Sciences, United Institute of Informatics Problems, Belarusian State University, Minsk, 220012, Belarus
| | - Zhaozheng Yin
- AI Institute, Department of Biomedical Informatics and Department of Computer Science, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Yingke Xu
- Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, 310027, China; Department of Endocrinology, The Affiliated Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China; Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 310058, China.
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Özdemir B, Reski R. Automated and semi-automated enhancement, segmentation and tracing of cytoskeletal networks in microscopic images: A review. Comput Struct Biotechnol J 2021; 19:2106-2120. [PMID: 33995906 PMCID: PMC8085673 DOI: 10.1016/j.csbj.2021.04.019] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 04/05/2021] [Accepted: 04/07/2021] [Indexed: 11/28/2022] Open
Abstract
Cytoskeletal filaments are structures of utmost importance to biological cells and organisms due to their versatility and the significant functions they perform. These biopolymers are most often organised into network-like scaffolds with a complex morphology. Understanding the geometrical and topological organisation of these networks provides key insights into their functional roles. However, this non-trivial task requires a combination of high-resolution microscopy and sophisticated image processing/analysis software. The correct analysis of the network structure and connectivity needs precise segmentation of microscopic images. While segmentation of filament-like objects is a well-studied concept in biomedical imaging, where tracing of neurons and blood vessels is routine, there are comparatively fewer studies focusing on the segmentation of cytoskeletal filaments and networks from microscopic images. The developments in the fields of microscopy, computer vision and deep learning, however, began to facilitate the task, as reflected by an increase in the recent literature on the topic. Here, we aim to provide a short summary of the research on the (semi-)automated enhancement, segmentation and tracing methods that are particularly designed and developed for microscopic images of cytoskeletal networks. In addition to providing an overview of the conventional methods, we cover the recently introduced, deep-learning-assisted methods alongside the advantages they offer over classical methods.
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Affiliation(s)
- Bugra Özdemir
- Plant Biotechnology, Faculty of Biology, University of Freiburg, Freiburg, Germany.,Signalling Research Centres BIOSS and CIBSS, Freiburg, Germany
| | - Ralf Reski
- Plant Biotechnology, Faculty of Biology, University of Freiburg, Freiburg, Germany.,Signalling Research Centres BIOSS and CIBSS, Freiburg, Germany.,Cluster of Excellence livMatS @ FIT - Freiburg Centre for Interactive Materials and Bioinspired Technologies, Freiburg, Germany
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