Learning to Segment Fine Structures Under Image-Level Supervision With an Application to Nematode Segmentation

Chen L, Strauch M, Daub M, Luigs HG, Jansen M, Merhof D (2022)

Publication Type: Conference contribution

Publication year: 2022

Journal

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society

Publisher: Institute of Electrical and Electronics Engineers Inc.

Book Volume: 2022-July

Pages Range: 2128-2131

Conference Proceedings Title: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Event location: Glasgow, GBR

ISBN: 9781728127828

DOI: 10.1109/EMBC48229.2022.9871517

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 image-level labels, our approach achieves Dice coefficients of 79.72% and 58.51 % for nematode and nematode cyst segmentation, respectively.

Involved external institutions

Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen DE Germany (DE) Julius Kühn-Institut – Bundesforschungsinstitut für Kulturpflanzen (JKI) DE Germany (DE) LemnaTec GmbH DE Germany (DE)

How to cite

APA:

Chen, L., Strauch, M., Daub, M., Luigs, H.G., Jansen, M., & Merhof, D. (2022). Learning to Segment Fine Structures Under Image-Level Supervision With an Application to Nematode Segmentation. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp. 2128-2131). Glasgow, GBR: Institute of Electrical and Electronics Engineers Inc..

MLA:

Chen, Long, et al. "Learning to Segment Fine Structures Under Image-Level Supervision With an Application to Nematode Segmentation." Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022, Glasgow, GBR Institute of Electrical and Electronics Engineers Inc., 2022. 2128-2131.

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