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Preprints, Working Papers, ... Year : 2023

Sketchpose: Learning to Segment Cells with Partial Annotations

Abstract

A few neural networks in biological image segmentation rely on a prediction of a distance map. This principle is at the basis of popular software such as Stardist, Cellpose or Omnipose. It yields unprecedented accuracy but hinges on fully annotated datasets. This can be a serious limitation for generating training sets and performing transfer learning. In this paper, we show how to handle partial annotation, while still relying on the distance map. We design a variant of the Omnipose architecture embedded in a user-friendly Napari plugin. We evaluate the performance of the proposed approach in the contexts of frugal learning, transfer learning and regular learning on a large database. Our experiments show that the proposed approach can lead to substantial savings in time and resources without sacrificing segmentation quality.
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Dates and versions

hal-04330824 , version 1 (08-12-2023)

Identifiers

  • HAL Id : hal-04330824 , version 1

Cite

Clément Cazorla, Nathanaël Munier, Renaud Morin, Pierre Weiss. Sketchpose: Learning to Segment Cells with Partial Annotations. 2023. ⟨hal-04330824⟩
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