Learning To Jointly Segment The Liver, Lesions and Vessels From Partially Annotated Datasets

An unsupervised nuclei segmentation method for histology images is proposed in this work. It consists of three modules applied to each of non-overlapping blocks: 1) data-driven color transform for dimension reduction, 2) fully-automated adaptive binarization, and 3) incorporation of geometric priors with morphological processing. The method is called CBM, which 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. It outperforms all other unsupervised methods and offers a competitive standing among supervised models based on the Aggregated Jaccard index (AJI) metric.