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<p>The segmentation of overlapping chromosomes in metaphase images is a longstanding challenge in cytogenetics, where limited spectral contrast in conventional RGB imaging. In this study, we explore hyperspectral imaging (HSI) as a promising alternative modality to enhance segmentation accuracy by exploiting information from a narrow band of the spectrum. To evaluate the benefit of spectral data, we acquired and annotated a dedicated dataset of overlapping chromosomes, using a hyperspectral microscopy system. Multiple segmentation baseline models were trained and benchmarked on both RGB and HSI data. Experimental results show that HSI-based models consistently outperform RGB-based counterparts, achieving up to 92% Dice score. These findings underline the potential of HSI for improving cytogenetic image analysis and provide a new benchmark dataset for future research in spectral segmentation.</p>

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Exploring HSI imagery for overlapped chromosomes segmentation

  • 期刊论文

  • 发表日期：

    2025年12月12日

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  • Najoua Zeffate,
  • Mohammed El Amine Bechar,
  • Jean-Marie Guyader,
  • Nathalie Douet-Guilbert,
  • Marwa El Bouz,
  • Marie-Bérengère Troadec

摘要

The segmentation of overlapping chromosomes in metaphase images is a longstanding challenge in cytogenetics, where limited spectral contrast in conventional RGB imaging. In this study, we explore hyperspectral imaging (HSI) as a promising alternative modality to enhance segmentation accuracy by exploiting information from a narrow band of the spectrum. To evaluate the benefit of spectral data, we acquired and annotated a dedicated dataset of overlapping chromosomes, using a hyperspectral microscopy system. Multiple segmentation baseline models were trained and benchmarked on both RGB and HSI data. Experimental results show that HSI-based models consistently outperform RGB-based counterparts, achieving up to 92% Dice score. These findings underline the potential of HSI for improving cytogenetic image analysis and provide a new benchmark dataset for future research in spectral segmentation.