Principal component-based image segmentation: a new approach to outline in vitro cell colonies

被引:2
作者
Arous, Delmon [1 ]
Schrunner, Stefan [1 ,2 ]
Hanson, Ingunn [3 ]
Edin, Nina Frederike Jeppesen [3 ]
Malinen, Eirik [1 ,3 ]
机构
[1] Oslo Univ Hosp, Dept Med Phys, Oslo, Norway
[2] Norwegian Univ Life Sci, Dept Data Sci, As, Norway
[3] Univ Oslo, Dept Phys, Oslo, Norway
关键词
Cell colony counting; image processing; topological watershed segmentation;
D O I
10.1080/21681163.2022.2035822
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Identification, segmentation and counting of stained in vitro cell colonies play a vital part in biological assays. Automating these tasks by optical scanning of cell dishes and subsequent image processing is not trivial due to challenges with, e.g. background noise and contaminations. Here, we present a machine learning procedure to amend these issues by characterising, extracting and segmenting inquired cell colonies using principal component analysis, kk-means clustering and a modified watershed segmentation algorithm to automatically identify visible colonies. The proposed segmentation algorithm was tested on two data sets: a T-47D (proprietary) cell colony and a bacteria (open source) data set. High F-1 scores (similar to 0.90 for T-47D and >0.95 for bacterial images), along with low absolute percentage errors (similar to 11% for T-47D and <5% for bacterial images), underlined good agreement with ground truth data. Our approach outperformed a recent state-of-the-art method on both data sets, demonstrating the usefulness of the presented algorithm.
引用
收藏
页码:18 / 30
页数:13
相关论文
共 39 条
[1]   Principal component analysis [J].
Abdi, Herve ;
Williams, Lynne J. .
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS, 2010, 2 (04) :433-459
[2]   Cell Segmentation Proposal Network for Microscopy Image Analysis [J].
Akram, Saad Ullah ;
Kannala, Juho ;
Eklund, Lauri ;
Heikkila, Janne .
DEEP LEARNING AND DATA LABELING FOR MEDICAL APPLICATIONS, 2016, 10008 :21-29
[3]   Automated Counting of Colony Forming Units Using Deep Transfer Learning From a Model for Congested Scenes Analysis [J].
Albaradei, Somayah A. ;
Napolitano, Francesco ;
Uludag, Mahmut ;
Thafar, Maha ;
Napolitano, Sara ;
Essack, Magbubah ;
Bajic, Vladimir B. ;
Gao, Xin .
IEEE ACCESS, 2020, 8 :164340-164346
[4]  
[Anonymous], 2006, Linear Algebra and Its Applications
[5]  
Arous D., 2021, CELL COLONY IMAGE SE, DOI [10.5281/zenodo.4593510, DOI 10.5281/ZENODO.4593510]
[6]   Automated cell colony counting and analysis using the circular Hough image transform algorithm (CHiTA) [J].
Bewes, J. M. ;
Suchowerska, N. ;
McKenzie, D. R. .
PHYSICS IN MEDICINE AND BIOLOGY, 2008, 53 (21) :5991-6008
[7]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[8]   Optimized digital counting colonies of clonogenic assays using ImageJ software and customized macros: Comparison with manual counting [J].
Cai, Zhongli ;
Chattopadhyay, Niladri ;
Liu, Wenchao Jessica ;
Chan, Conrad ;
Pignol, Jean-Philippe ;
Reilly, Raymond M. .
INTERNATIONAL JOURNAL OF RADIATION BIOLOGY, 2011, 87 (11) :1135-1146
[9]   CellProfiler: image analysis software for identifying and quantifying cell phenotypes [J].
Carpenter, Anne E. ;
Jones, Thouis Ray ;
Lamprecht, Michael R. ;
Clarke, Colin ;
Kang, In Han ;
Friman, Ola ;
Guertin, David A. ;
Chang, Joo Han ;
Lindquist, Robert A. ;
Moffat, Jason ;
Golland, Polina ;
Sabatini, David M. .
GENOME BIOLOGY, 2006, 7 (10)
[10]   Automated counting of bacterial colonies by image analysis [J].
Chiang, Pei-Ju ;
Tseng, Min-Jen ;
He, Zong-Sian ;
Li, Chia-Hsun .
JOURNAL OF MICROBIOLOGICAL METHODS, 2015, 108 :74-82