PatchPerPix for instance segmentation

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Item Type:	Article
Title:	PatchPerPix for instance segmentation
Creators Name:	Mais, L., Hirsch, P. and Kainmueller, D.
Abstract:	We present a novel method for proposal free instance segmentation that can handle sophisticated object shapes which span large parts of an image and form dense object clusters with crossovers. Our method is based on predicting dense local shape descriptors, which we assemble to form instances. All instances are assembled simultaneously in one go. To our knowledge, our method is the first non-iterative method that yields instances that are composed of learnt shape patches. We evaluate our method on a diverse range of data domains, where it defines the new state of the art on four benchmarks, namely the ISBI 2012 EM segmentation benchmark, the BBBC010 C. elegans dataset, and 2d as well as 3d fluorescence microscopy data of cell nuclei. We show furthermore that our method also applies to 3d light microscopy data of Drosophila neurons, which exhibit extreme cases of complex shape clusters.
Source:	Lecture Notes in Computer Science
Series Name:	Lecture Notes in Computer Science
Title of Book:	Computer Vision - ECCV 2020
ISSN:	0302-9743
ISBN:	978-3-030-58594-5
Publisher:	Springer
Volume:	12370
Page Range:	288-304
Date:	20 November 2020
Official Publication:	https://doi.org/10.1007/978-3-030-58595-2_18

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