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arXiv · 2110.07147

Unsupervised Data-Driven Nuclei Segmentation For Histology Images

Vasileios Magoulianitis·Peida Han·Yijing Yang·C. -C. Jay Kuo
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Abstract

An unsupervised data-driven nuclei segmentation method for histology images, called CBM, is proposed in this work. CBM consists of three modules applied in a block-wise manner: 1) data-driven color transform for energy compaction and dimension reduction, 2) data-driven binarization, and 3) incorporation of geometric priors with morphological processing. CBM comes from the first letter of the three modules - "Color transform", "Binarization" and "Morphological processing". Experiments on the MoNuSeg dataset validate the effectiveness of the proposed CBM method. CBM outperforms all other unsupervised methods and offers a competitive standing among supervised models based on the Aggregated Jaccard Index (AJI) metric.

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Vasileios Magoulianitis, Peida Han, Yijing Yang, C. -C. Jay Kuo. 2021-10-14. Unsupervised Data-Driven Nuclei Segmentation For Histology Images. https://arxiv.org/abs/2110.07147

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