Comparing Deep Learning Performance for Chronic Lymphocytic Leukaemia Cell Segmentation in Brightfield Microscopy Images

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00098892%3A_____%2F24%3A10158756" target="_blank" >RIV/00098892:_____/24:10158756 - isvavai.cz</a>

Alternative codes found

RIV/61989592:15110/24:73627307

Result on the web

<a href="https://journals.sagepub.com/doi/10.1177/11779322241272387" target="_blank" >https://journals.sagepub.com/doi/10.1177/11779322241272387</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1177/11779322241272387" target="_blank" >10.1177/11779322241272387</a>

Result language

angličtina

Original language name

Comparing Deep Learning Performance for Chronic Lymphocytic Leukaemia Cell Segmentation in Brightfield Microscopy Images

Original language description

Objectives: This article focuses on the detection of cells in low-contrast brightfield microscopy images; in our case, it is chronic lymphocytic leukaemia cells. The automatic detection of cells from brightfield time-lapse microscopic images brings new opportunities in cell morphology and migration studies; to achieve the desired results, it is advisable to use state-of-the-art image segmentation methods that not only detect the cell but also detect its boundaries with the highest possible accuracy, thus defining its shape and dimensions. Methods: We compared eight state-of-the-art neural network architectures with different backbone encoders for image data segmentation, namely U-net, U-net++, the Pyramid Attention Network, the Multi-Attention Network, LinkNet, the Feature Pyramid Network, DeepLabV3, and DeepLabV3+. The training process involved training each of these networks for 1000 epochs using the PyTorch and PyTorch Lightning libraries. For instance segmentation, the watershed algorithm and three-class image semantic segmentation were used. We also used StarDist, a deep learning-based tool for object detection with star-convex shapes. Results: The optimal combination for semantic segmentation was the U-net++ architecture with a ResNeSt-269 background with a data set intersection over a union score of 0.8902. For the cell characteristics examined (area, circularity, solidity, perimeter, radius, and shape index), the difference in mean value using different chronic lymphocytic leukaemia cell segmentation approaches appeared to be statistically significant (Mann–Whitney U test, P &lt; .0001). Conclusion: We found that overall, the algorithms demonstrate equal agreement with ground truth, but with the comparison, it can be seen that the different approaches prefer different morphological features of the cells. Consequently, choosing the most suitable method for instance-based cell segmentation depends on the particular application, namely, the specific cellular traits being investigated.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10609 - Biochemical research methods

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Bioinformatics and Biology Insights

ISSN

1177-9322

e-ISSN

1177-9322

Volume of the periodical

18

Issue of the periodical within the volume

2024

Country of publishing house

GB - UNITED KINGDOM

Number of pages

14

Pages from-to

11779322241272387

UT code for WoS article

001307771800001

EID of the result in the Scopus database

2-s2.0-85203140912