Segmentation of multiple myeloma plasma cells in microscopy images with noisy labels

Abstract

A key component towards an improved cancer diagnosis is the development of computer-assisted tools. In this article, we present the solution that won the SegPC-2021 competition for the segmentation of multiple myeloma plasma cells in microscopy images. The labels in the competition dataset were generated semi-automatically and presented noise. To deal with it, new labels were generated from existing ones, heavy image augmentation was carried out and predictions were combined by a custom ensemble strategy. These techniques, along with state-of-the-art feature extractors and instance segmentation architectures, resulted in a mean Intersection-over-Union of 0.9389 on the SegPC-2021 final test set.