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

Markéta Vašinková¹, Vít Doleží¹, Michal Vašinek¹

¹Department of Computer Science, FEECS, VSB - Technical University of Ostrava, Ostrava, Czech Republic.

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|September 9, 2024

Summary

Accurate cell detection in low-contrast microscopy images is crucial for cell studies. U-net++ with ResNeSt-269 excelled in segmenting chronic lymphocytic leukemia cells, highlighting the need for method selection based on specific cell morphology features.

Keywords:

Cell detection U-net++cell segmentation chronic lymphocytic leukaemia cells image analysis

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Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

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Area of Science:

Biomedical Imaging
Computational Biology
Cell Biology

Background:

Accurate cell detection in low-contrast brightfield microscopy images is challenging.
Automatic cell detection is vital for quantitative cell morphology and migration studies.
State-of-the-art image segmentation methods are required for precise cell boundary detection.

Purpose of the Study:

To evaluate and compare eight advanced neural network architectures for segmenting cells in low-contrast brightfield microscopy images.
To identify the optimal deep learning model for detecting and segmenting chronic lymphocytic leukemia (CLL) cells.
To assess the impact of different segmentation methods on the analysis of cellular morphological features.

Main Methods:

Comparison of eight neural network architectures (U-net, U-net++, PAN, MAN, LinkNet, FPN, DeepLabV3, DeepLabV3+) for image segmentation.
Training networks for 1000 epochs using PyTorch and PyTorch Lightning.
Utilizing watershed algorithm and three-class semantic segmentation for instance segmentation.
Employing StarDist, a deep learning tool for object detection.

Main Results:

U-net++ architecture with a ResNeSt-269 backbone achieved the highest Intersection over Union (IoU) score of 0.8902 for semantic segmentation.
Statistically significant differences (p < 0.0001) were observed in mean cell characteristics (area, circularity, solidity, perimeter, radius, shape index) across different segmentation methods.
While algorithms showed overall agreement with ground truth, distinct methods prioritized different morphological features.

Conclusions:

The optimal choice of cell segmentation method depends on the specific application and the cellular traits under investigation.
U-net++ demonstrates superior performance for semantic segmentation of CLL cells in this context.
Different segmentation approaches can influence the quantitative analysis of cell morphology, necessitating careful method selection.