Keywords: instance segmentation, spatial embeddings, test-time augmentation, deep learning, microscopy
TL;DR: Embedding-base instance segmentation (EmbedSeg) achieves state-of-the-art results on several 2D and 3D microscopy datasets.
Abstract: Automatic detection and segmentation of objects in 2D and 3D microscopy data is important for countless biomedical applications. In the natural image domain, spatial embedding-based instance segmentation methods are known to yield high-quality results, but their utility for segmenting microscopy data is currently little researched. Here we introduce EmbedSeg, an embedding-based instance segmentation method which outperforms existing state-of-the-art baselines on 2D as well as 3D microscopy datasets. Additionally, we show that EmbedSeg has a GPU memory footprint small enough to train even on laptop GPUs, making it accessible to virtually everyone. Finally, we introduce four new 3D microscopy datasets, which we make publicly available alongside ground truth training labels. Our open-source implementation is available at https://github.com/juglab/EmbedSeg.
Registration: I acknowledge that publication of this at MIDL and in the proceedings requires at least one of the authors to register and present the work during the conference.
Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
Paper Type: both
Primary Subject Area: Segmentation
Secondary Subject Area: Application: Other
Source Code Url: https://github.com/juglab/EmbedSeg
Data Set Url: https://github.com/juglab/EmbedSeg/releases/tag/v0.1.0
Source Latex: zip