Learning to Segment Fine Structures Under Image-Level Supervision With an Application to Nematode Segmentation
10 Jul 2022
Reading time ~1 minute
Authors: Long Chen, Martin Strauch, Matthias Daub, Hans-Georg Luigs, Marcus Jansen and Dorit Merhof
Published in: International Engineering in Medicine and Biology Conference (EMBC)
Abstract: Image segmentation models trained only with image-level labels have become increasingly popular as they require significantly less annotation effort than models trained with scribble, bounding box or pixel-wise annotations. While methods utilizing image-level labels achieve promising performance for the segmentation of larger-scale objects, they perform less well for the fine structures frequently encountered in biological images. In order to address this performance gap, we propose a deep network architecture based on two key principles, Global Weighted Pooling (GWP) and segmentation refinement by low-level image cues, that, together, make segmentation of fine structures possible. We apply our segmentation method to image datasets containing such fine structures, nematodes (worms + eggs) and nematode cysts immersed in organic debris objects, which is an application scenario encountered in automated soil sample screening. Supervised only with imagelevel labels, our approach achieves Dice coefficients of 79.72% and 58.51% for nematode and nematode cyst segmentation, respectively.