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Unsupervised Data-Driven Nuclei Segmentation For Histology Images

2021-10-14 04:26:50
Vasileios Magoulianitis, Peida Han, Yijing Yang, C.-C. Jay Kuo
arXiv_CV
         
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.

Abstract (translated)

URL

https://arxiv.org/abs/2110.07147

PDF

https://arxiv.org/pdf/2110.07147.pdf

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