PLoS ONE (Jan 2022)

Different cell imaging methods did not significantly improve immune cell image classification performance.

  • Taisaku Ogawa,
  • Koji Ochiai,
  • Tomoharu Iwata,
  • Tomokatsu Ikawa,
  • Taku Tsuzuki,
  • Katsuyuki Shiroguchi,
  • Koichi Takahashi

DOI
https://doi.org/10.1371/journal.pone.0262397
Journal volume & issue
Vol. 17, no. 1
p. e0262397

Abstract

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Developments in high-throughput microscopy have made it possible to collect huge amounts of cell image data that are difficult to analyse manually. Machine learning (e.g., deep learning) is often employed to automate the extraction of information from these data, such as cell counting, cell type classification and image segmentation. However, the effects of different imaging methods on the accuracy of image processing have not been examined systematically. We studied the effects of different imaging methods on the performance of machine learning-based cell type classifiers. We observed lymphoid-primed multipotential progenitor (LMPP) and pro-B cells using three imaging methods: differential interference contrast (DIC), phase contrast (Ph) and bright-field (BF). We examined the classification performance of convolutional neural networks (CNNs) with each of them and their combinations. CNNs achieved an area under the receiver operating characteristic (ROC) curve (AUC) of ~0.9, which was significantly better than when the classifier used only cell size or cell contour shape as input. However, no significant differences were found between imaging methods and focal positions.