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

Omar Ali, Alexandre Bone, Marc-Michel Rohe, Eric Vibert, Irene Vignon-Clementel

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    Length: 00:10:25
19 Oct 2022

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.