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.
|Title of host publication||Proceedings of Computer Vision for Microscopy Image Analysis (CVMI) workshop|
|Number of pages||9|
|Publication status||Published - 2020|
|Event||Workshop on Computer Vision for Microscopy Image Analysis: held in conjunction with the CVPR 2020 - Virtual|
Duration: 19 Jun 2020 → 19 Jun 2020
|Workshop||Workshop on Computer Vision for Microscopy Image Analysis|
|Period||19/06/2020 → 19/06/2020|
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.