Unsupervised Data-Driven Nuclei Segmentation For Histology Images
This addresses the problem of automating nuclei segmentation for medical image analysis, offering an unsupervised approach that reduces the need for labeled data, though it appears incremental as it builds on existing unsupervised techniques.
The paper tackles unsupervised nuclei segmentation in histology images by proposing the CBM method, which achieves competitive performance against supervised models on the MoNuSeg dataset using the Aggregated Jaccard Index metric.
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.