HUNIS: High-Performance Unsupervised Nuclei Instance Segmentation
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Abstract (arXiv)
A high-performance unsupervised nuclei instance segmentation (HUNIS) method is proposed in this work. HUNIS consists of two-stage block-wise operations. The first stage includes: 1) adaptive thresholding of pixel intensities, 2) incorporation of nuclei size/shape priors and 3) removal of false positive nuclei instances. Then, HUNIS conducts the second stage segmentation by receiving guidance from the first one. The second stage exploits the segmentation masks obtained in the first stage and leverages color and shape distributions for a more accurate segmentation. The main purpose of the two-stage design is to provide pixel-wise pseudo-labels from the first to the second stage. This self-supervision mechanism is novel and effective. Experimental results on the MoNuSeg dataset show that HUNIS outperforms all other unsupervised methods by a substantial margin. It also has a competitive standing among state-of-the-art supervised methods.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 26 references that are in the map. First posted on arXiv 28 Mar 2022.
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8 of 26 references.
References · 8
- 2018HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Imagescited by 388
- 2018Attention Gated Networks: Learning to Leverage Salient Regions in Medical Imagescited by 272
- 2019CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregationcited by 54
- 2020Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weightingcited by 38
- 2020Self-Supervised Nuclei Segmentation in Histopathological Images Using Attentioncited by 30
- 2020Instance-aware Self-supervised Learning for Nuclei Segmentationcited by 27
- 2021DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Imagescited by 14
- 2021Unsupervised Data-Driven Nuclei Segmentation For Histology Imagescited by 1
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