[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120783-en":3,"doc-seo-120783-105":29,"detail-sidebar-cat-0-en-105":82},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},120783,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Machine learning enhanced cell tracking - end-to-end ML-enhanced pipeline review","Machine learning enhanced cell tracking addresses the need for reliable, high-fidelity multi-generational lineage reconstruction from multidimensional bioimaging. It reviews how ML has transformed microscopy image analysis—supporting robust cell detection and related tasks—while noting that accurate cell tracking remains difficult to generalize across unseen cell types and datasets. The work proposes an end-to-end ML-enhanced pipeline where representation learning, tracking datasets, metrics, and evaluation methods jointly improve tracking solutions.","TYPE Perspective  \nPUBLISHED 14 July 2023  \nDOI 10.3389/fbinf.2023.1228989  \nOPEN ACCESS  \nEDITED BY  \nFlorian Levet,  \nUMR5297 Institut Interdisciplinaire de Neurosciences (IINS), France  \nREVIEWED BY  \nJean-Yves Tinevez, Institut Pasteur, France Ko Sugawara,  \nRIKEN Center for Biosystems Dynamics Research (BDR), Japan  \n*CORRESPONDENCE  \nAlan R. Lowe,  \n [a.lowe@ucl.ac.uk](a.lowe@ucl.ac.uk)  \nRECEIVED 25 May 2023  \nACCEPTED 03 July 2023  \nPUBLISHED 14 July 2023  \nCITATION  \nSoelistyo CJ, Ulicna K and Lowe AR (2023), Machine learning enhanced cell tracking.  \nFront. Bioinform. 3:1228989 .  \ndoi: 10.3389/fbinf.2023.1228989  \nCOPYRIGHT  \n© 2023 Soelistyo, Ulicna and Lowe. This isan open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning enhanced cell tracking  \nChristopher J. Soelistyo 1,2, Kristina Ulicna 1,2 and Alan R. Lowe 1,2,3*  \n1Department of Structural and Molecular Biology, University College London, London, United Kingdom, 2Institute for the Physics of Living Systems, London, United Kingdom, 3Alan Turing Institute, London, United Kingdom  \nQuantifying cell biology in space and time requires computational methods to detect cells, measure their properties, and assemble these into meaningful trajectories. In this aspect, machine learning (ML) is having a transformational effect on bioimage analysis, now enabling robust cell detection in multidimensional image data. However, the task of cell tracking, or constructing accurate multi-generational lineages from imaging data, remains an open challenge. Most cell tracking algorithms are largely based on our prior knowledge of cell behaviors, and as such, are difficult to generalize to new and unseen cell types or datasets. Here, we propose that ML provides the framework to learn aspects of cell behavior using cell tracking as the task to be learned. We suggest that advances in representation learning, cell tracking datasets, metrics, and methods for constructing and evaluating tracking solutions can all form part of an end-to-end ML-enhanced pipeline. These developments will lead the way to new computational methods that can be used to understand complex, timeevolving biological systems.  \nKEYWORDS  \nmachine learning (ML), computer vision, tracking, cell tracking, bioimage analysis, optimisation  \n1 Introduction  \nUnderstanding how cells self-organize to become tissues and whole organisms is one of the most fundamental questions of biology. Indeed, single cell biology has the potential to illuminate processes from development and regeneration to diseases such as cancer. A predicate of quantifying cell biology in space and time is a suite of computational tools that can extract measurements from the myriad sources of experimental data. These include algorithms to detect cells, measure properties such as shape, morphology, or biochemical activity and to link these observations over time into biologically meaningful trajectories. Recent advances in optical imaging methods such as light-sheet microscopy now allow researchers to capture volumetric (3D + t) timelapse image data at high-frame rates, with multiple biochemical reporters (Dunsby, 2008; Chen et al., 2014; Kumar et al., 2014; Sapoznik et al., 2020; Yang et al., 2022) . As such, we are now in an era where we can generate vast volumes of information-rich experimental imagery more easily than we can extract meaning from the data.  \nIn recent years, machine learning (ML) has had a transformational effect on microscopy data analysis; common image processing tasks such as cell segmentation, image denoising, feature extraction and cel","cbCaifUNmQjLo8kh","https://ap.wps.com/l/cbCaifUNmQjLo8kh","pdf",1517949,1,"English","en",105,"# Introduction\n## Tracking-by-detection paradigm\n## ML-enabled cell detection\n# Training and validation data\n## Annotated datasets for ML development","[{\"question\":\"What is the central thesis of using machine learning for cell tracking?\",\"answer\":\"Advances in ML can learn models of cell behavior by treating cell tracking itself as the task to be learned, enabling an end-to-end ML-enhanced pipeline.\"}]","Machine learning enhanced cell tracking - end-to-end ML-enhanced pipeline review | PDF",1785732017,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":77,"head_meta":79,"extra_data":81,"updated_unix":27},"machine-learning-enhanced-cell-tracking-end-to-end-ml-enhanced-pipeline-review","",{"@graph":35,"@context":76},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/machine-learning-enhanced-cell-tracking-end-to-end-ml-enhanced-pipeline-review/120783/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70],{"name":71,"@type":72,"acceptedAnswer":73},"What is the central thesis of using machine learning for cell tracking?","Question",{"text":74,"@type":75},"Advances in ML can learn models of cell behavior by treating cell tracking itself as the task to be learned, enabling an end-to-end ML-enhanced pipeline.","Answer","https://schema.org",{"og:url":51,"og:type":78,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":80,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":83},[84,88,92,96,101,106,111,114,118,121,125],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":45,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":45,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":112,"slug":113},30,"research-report",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":28,"slug":117},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":119,"show_sort_weight":28,"slug":120},"World Cup","world-cup",{"id":122,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":122,"slug":124},10,"Lifestyle","lifestyle",{"id":126,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":97,"slug":128},19,"General","general"]