\documentclass{midl} % Include author names
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\usepackage{amsmath}
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\usepackage{mwe} % to get dummy images
%\jmlrvolume{-- Under Review}
\jmlryear{2021}
\jmlrworkshop{Full Paper -- MIDL 2021 submission}
%\editors{Under Review for MIDL 2021}

\title[Embedding-based Instance Segmentation in Microscopy ]{Embedding-based Instance Segmentation in Microscopy}

\midlauthor{
\hspace{-0.3em}\Name{Manan Lalit \nametag{$^{1,2}$}} \Email{lalit@mpi-cbg.de}
\AND
\Name{Pavel Tomancak \nametag{$^{1,2,3}$}} \Email{tomancak@mpi-cbg.de}
 \AND
\Name{Florian Jug \nametag{$^{1,2,4}$}} \Email{florian.jug@fht.org} \\
\addr $^{1}$~Center for Systems Biology Dresden (CSBD)\\
\addr $^{2}$~Max Planck Institute of Molecular Cell Biology and Genetics\\
\addr $^{3}$~IT4Innovations, V\v{S}B - Technical University of Ostrava, Ostrava-Poruba, Czech Republic \\
\addr $^{4}$~Fondazione Human Technopole, Milano, Italy}

% Commands...
% --------------------
\newcommand{\EmbedSeg}{\mbox{\textsc{EmbedSeg}}\xspace}
\newcommand{\miniheadline}[1]{\vspace{3mm}\noindent\textbf{#1.}}
\newcommand{\PlatynereisLive}{\mbox{\textit{Platynereis}-Nuclei-CBG}\xspace}
\newcommand{\PlatynereisFixed}{\mbox{\textit{Platynereis}-ISH-Nuclei-CBG}\xspace}
\newcommand{\Parhyale}{\mbox{\textit{Paryhale}-Nuclei-IGFL }\xspace}
\newcommand{\Organoid}{\mbox{Mouse-Organoid-Cells-CBG}\xspace}
\newcommand{\MouseSkull}{\mbox{Mouse-Skull-Nuclei-CBG}\xspace}
\makeatletter
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\input{figures}
\input{tables}
\begin{document}

\maketitle

\begin{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 \url{https://github.com/juglab/EmbedSeg}.


\end{abstract}

\begin{keywords}
instance segmentation, microscopy, spatial embeddings, deep learning
\end{keywords}

\input{01-introduction}
\input{02-approach}
\input{03-experiments_and_results}
\input{04-discussion}

\midlacknowledgments{\small
The authors would like to thank the Scientific Computing Facility at MPI-CBG, thank Matthias Arzt, Joran Deschamps and Nuno Pimp$\tilde{a}$o Martins for feedback and testing.
Alf Honigmann and Anna Goncharova provided the \Organoid data and annotations. 
Jacqueline Tabler and Diana Afonso provided the \MouseSkull dataset and annotations.
This work was supported by the German Federal Ministry of Research and Education (BMBF) under the codes 031L0102 (de.NBI) and 01IS18026C (ScaDS2), and the German Research Foundation (DFG) under the code JU3110/1-1(FiSS) and TO563/8-1 (FiSS).
P.T. was supported by the European Regional Development Fund in the IT4Innovations national supercomputing center,
%path to exascale project, 
project number CZ.02.1.01/0.0/0.0/16\_013/0001791 within the Program Research, Development and Education.
}

\bibliography{lalit21}
\input{05-appendix}


\end{document}
