Classifying changes in LN-18 glial cell morphology: a supervised machine learning approach to analyzing cell microscopy data via FIJI and WEKA.
In cell-based research, the process of visually monitoring cells generates large image datasets that need to be evaluated for quantifiable information in order to track the effectiveness of treatments in vitro. With the traditional, end-point assay-based approach being error-prone, and existing comp...
| Published in: | Medical & Biological Engineering & Computing Vol. 58; no. 7; pp. 1419 - 1431 |
|---|---|
| Main Authors: | , , , |
| Format: | pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Jul2020
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143819692&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143819692 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jul2020 vid: 58 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143819692 143819692 144045132 NLM32314170 143819692 10.1007/s11517-020-02177-x NLM32314170 143819692 ppf: 1419 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Classifying changes in LN-18 glial cell morphology: a supervised machine learning approach to analyzing cell microscopy data via FIJI and WEKA. aug: au: Mbiki, Sarah McClendon, Jerome Alexander-Bryant, Angela Gilmore, Jordon affil: Department of Bioengineering, Clemson University, 301 Rhodes Research Center, 29634, Clemson, SC, USA sug: subj: Image Processing, Computer Assisted Methods Cells, Cultured Microscopy Methods Probability Pharmacokinetics Cells Pathology Human ab: In cell-based research, the process of visually monitoring cells generates large image datasets that need to be evaluated for quantifiable information in order to track the effectiveness of treatments in vitro. With the traditional, end-point assay-based approach being error-prone, and existing computational approaches being complex, we tested existing machine learning frameworks to find methods that are relatively simple, yet powerful enough to accomplish the goal of analyzing cell microscopy data. This paper details the machine learning pipeline for pixel-based classification and object-based classification. Furthermore, it compares the performances of three classifiers. The classifiers evaluated were the fast-random forest (RF), the sequential minimal optimization (SMO), and the Bayesian network (BN). Images were first preprocessed using smoothing and contrast methods found in FIJI. For pixel-based classification, the preprocessed images were fed into the Trainable Waikato Segmentation (TWS). For object-based classification, training and classification were conducted within the Waikato Environment for Knowledge Analysis (WEKA) interface. All classifiers' performance was evaluated using the WEKA experimental explorer. In terms of performance, the BN had the lowest classification accuracy for both the pixel-based and object-based model. The object-based SMO classifier had the best performance with the lowest mean absolute error of 0.05. The TWS and WEKA interface allows users to easily create and train classifiers for image analysis. However, for analyzing large image datasets, they are not ideal. Grapical abstract. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|