How Deep Learning Enables Cellular Image Analysis

How Deep Learning Enables Cellular Image Analysis

Deep learning empowers cell image analysis Overview of deep learning-based cellular image analysis. A typical analysis pipeline consists of a recycling module and an inference module: the inference module directly produces estimated metrics. However, as the experimental setup changes, the parameters of the deep learning model must be recycled. Credit: Intelligent Computing (2022). DOI: 10.34133/2022/9861263

The cell is the basic structural and functional unit of life, with varying sizes, shapes and densities. There are many different physiological and pathological factors that influence these parameters. It is therefore extremely important for biomedical and pharmaceutical research to study the characteristics of cells.

Traditionally, researchers observed cell samples directly under a microscope to study morphological changes in cells. In recent years, with the development of computer science and artificial intelligence, deep learning can now be combined with cell analysis methods. It can replace researchers’ direct observation under the microscope and manual image interpretation, improving research efficiency and accuracy.

An increasing number of deep learning-based algorithms have been developed to enable cellular image analysis, primarily to address three key tasks:

  1. Segmentation. To identify significant objects or features, the image is split into several parts using deep learning. Cell segmentation is the basic principle for the identification, counting, tracking and morphological analysis of cellular images;
  2. Followed. That is, after the segmentation of the cellular images, the cellular behavior of the entire spectrum is monitored. Living cells contain a lot of information about the living organism, and the dynamic characteristics of cells, especially morphological changes, can reflect the health status of the organism in pathological and physiological processes, such as immune response , wound healing, cancer cell spread and metastasis, etc.
  3. Classification. Classification of cellular morphological characteristics based on extracted parameters often serves as a downstream analytical task for phenotypic screening and cell profiling.

For the three crucial tasks above, a review article published in the journal Intelligent Computing discusses in depth advances in deep learning techniques.

“Unlike traditional computer vision techniques, a deep neural network (DNN) can automatically produce more efficient representations than homemade representations by learning from a large-scale dataset. In cellular images, methods based on deep learning also show promising results in cell segmentation and tracking,” the authors said. “Such successful applications demonstrate the ability of DNNs to extract high-level features and shed light on the ability potential to use deep learning to reveal more sophisticated laws of life behind cellular phenotypes.”

Furthermore, the authors also discuss the challenges and opportunities of deep learning methods in cellular image processing. The authors said, “Deep learning has demonstrated an incredible ability to perform cellular image analysis. However, there remains a significant performance gap between deep learning algorithms in academic research and practical applications. There are currently challenges and opportunities in three aspects, namely data quantity, data quality and data trust:

  1. Deep learning with a small but expensive dataset. Building a large-scale cellular image dataset is a daunting task. Indeed, cellular images require skilled biology experts to assign labels frame by frame. The scale of cellular image datasets is often limited by the difficulty of annotation.
  2. Deep learning with noisy and unbalanced labels. The quality of annotations of cellular image datasets is highly dependent on the professional skills of humans, resulting in label noise and label imbalance. Label noise is introduced by assigning incorrect or incomplete labels to training images. The label imbalance is caused by the preference for annotation, where the number of labeled images for different classes is quite unbalanced.
  3. Cell image analysis sensitive to uncertainty. Uncertainty-sensitive learning is crucial for deep learning applications in biological scenarios. It is impossible for a simple neural network to detect new phenotypes without a mechanism reflecting the confidence of the classification results.

Using deep learning, scientists are exploring new technologies to improve the analysis of cellular images. More effective solutions will be offered in the future, and deep learning and biomedical research will be more closely integrated.

More information:
Junde Xu et al, Deep Learning in Cellular Image Analysis, Intelligent Computing (2022). DOI: 10.34133/2022/9861263

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