Detection of extremely rare dead cells in fluorescence microscopy images

Key points

• However, automated identification remains challenging due to signal variability, optical artifacts, and colocalization ambiguities across multiple channels.1
• Markers that define nuclear boundaries do not neatly coincide spatially or temporally with apoptotic markers.1
• Using a dataset of 258 images containing 65,527 cells/nuclei across multiple scenes with 243 manually annotated ground truth dead cells, we evaluate each method on metrics such as PR-AUC, Precision, Recall, F1-score, and macro-F1.1
• Our results indicate that classification using image features (deep learning) and numerical features (traditional) achieved the best performance, with ResNet-18 and XGBoost significantly outperforming manually designed heuristics and anomaly detection methods.1

Sources (1)

• [1]Detection of extremely rare dead cells in fluorescence microscopy images
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 04:01 PM

  Dead cell detection in fluorescence microscopy is essential for phenotypic screening and drug efficacy assessment.

  However, automated identification remains challenging due to signal variability, optical artifacts, and colocalization ambiguities across multiple channels.

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