Multi-object Graph-based Segmentation with Non-overlapping Surfaces

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Abstract

For 3D images, segmentation via fitting surface meshes to object boundaries provides an efficient way to handle large images and enforce geometric prior knowledge. Furthermore, fitting such meshes with graph cuts has proven to be a versatile and robust framework. However, when segmenting multiple distinct objects in one image, current methods do not allow the natural constraint that objects should not overlap. In this paper, we present an extension to graph cut based methods which can provide a globally optimal segmentation of thousands of objects while guaranteeing no overlap. Our method works by separating objects with planes whose positions are determined as part of the graph cut. To demonstrate the general applicability of our method, we apply it to several 3D microscopy data sets from both biology and materials science. Our results show both quantitative and qualitative improvements.
Original languageEnglish
Title of host publicationProceedings of Computer Vision for Microscopy Image Analysis (CVMI) workshop
Number of pages9
PublisherIEEE
Publication date2020
Publication statusPublished - 2020
EventWorkshop on Computer Vision for Microscopy Image Analysis: held in conjunction with the CVPR 2020 - Virtual
Duration: 19 Jun 202019 Jun 2020
https://cvmi2020.github.io/index.html

Workshop

WorkshopWorkshop on Computer Vision for Microscopy Image Analysis
LocationVirtual
Period19/06/202019/06/2020
Internet address

Cite this

Jensen, P. M., Dahl, A. B., & Dahl, V. A. (2020). Multi-object Graph-based Segmentation with Non-overlapping Surfaces. In Proceedings of Computer Vision for Microscopy Image Analysis (CVMI) workshop IEEE.