Cell Image Classification: A Comparative Overview

• Published in: Cytometry. Part A Vol. 97; no. 4; pp. 347 - 362
• Main Authors: Shifat‐E‐Rabbi, Mohammad, Yin, Xuwang, Fitzgerald, Cailey E., Rohde, Gustavo K.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.04.2020; Wiley Subscription Services, Inc
• Subjects: cell biology; Classification; computational biology; Cytometry; Datasets; digital pathology; Drug screening; Feature extraction; Image acquisition; Image classification; image informatics; Localization; Medical imaging; Morphometry; Neural networks; Production methods; Reviews; Training; computational biology; cell biology; image informatics; digital pathology
• ISSN: 1552-4922; 1552-4930
• DOI: 10.1002/cyto.a.23984

Cell image classification methods are currently being used in numerous applications in cell biology and medicine. Applications include understanding the effects of genes and drugs in screening experiments, understanding the role and subcellular localization of different proteins, as well as diagnosis and prognosis of cancer from images acquired using cytological and histological techniques. The article also reviews three main approaches for cell image classification most often used: numerical feature extraction, end‐to‐end classification with neural networks (NNs), and transport‐based morphometry (TBM). In addition, we provide comparisons on four different cell imaging datasets to highlight the relative strength of each method. The results computed using four publicly available datasets show that numerical features tend to carry the best discriminative information for most of the classification tasks. Results also show that NN‐based methods produce state‐of‐the‐art results in the dataset that contains a relatively large number of training samples. Data augmentation or the choice of a more recently reported architecture does not necessarily improve the classification performance of NNs in the datasets with limited number of training samples. If understanding and visualization are desired aspects, TBM methods can offer the ability to invert classification functions, and thus can aid in the interpretation of results. These and other comparison outcomes are discussed with the aim of clarifying the advantages and disadvantages of each method. © 2020 International Society for Advancement of Cytometry