Sketchpose: Learning to Segment Cells with Partial Annotations
This addresses a limitation for researchers and practitioners in biomedical imaging by enabling frugal and transfer learning with partial data, though it is incremental as it builds on existing distance map methods.
The paper tackles the problem of cell segmentation requiring fully annotated datasets by proposing a method that works with partial annotations, resulting in substantial savings in time and resources without sacrificing segmentation quality.
The most popular networks used for cell segmentation (e.g. Cellpose, Stardist, HoverNet,...) rely on a prediction of a distance map. It yields unprecedented accuracy but hinges on fully annotated datasets. This is a serious limitation to generate training sets and perform transfer learning. In this paper, we propose a method that still relies on the distance map and handles partially annotated objects. We evaluate the performance of the proposed approach in the contexts of frugal learning, transfer learning and regular learning on regular databases. Our experiments show that it can lead to substantial savings in time and resources without sacrificing segmentation quality. The proposed algorithm is embedded in a user-friendly Napari plugin.