Unsupervised Data-Driven Nuclei Segmentation For Histology Images
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 20 references that are in the map. First posted on arXiv 14 Oct 2021.
Builds on
6 of 20 references.
References · 6
- 2018Path Aggregation Network for Instance Segmentationcited by 796
- 2020Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Imagescited by 55
- 2019CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregationcited by 54
- 2019The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GANcited by 30
- 2020Self-Supervised Nuclei Segmentation in Histopathological Images Using Attentioncited by 30
- 2020Instance-aware Self-supervised Learning for Nuclei Segmentationcited by 27
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Citing papers per year: 2022: 1.
1 paper.