BFS-Net: Weakly Supervised Cell Instance Segmentation from Bright-Field Microscopy Z-Stacks

• URL: http://arxiv.org/abs/2206.04558v1
• Date: Thu, 9 Jun 2022 15:13:08 GMT
• Title: BFS-Net: Weakly Supervised Cell Instance Segmentation from Bright-Field Microscopy Z-Stacks
• Authors: Shervin Dehghani, Benjamin Busam, Nassir Navab, Ali Nasseri
• Abstract summary: We investigate the prediction of 3D cell instances from a set of BFM Z-Stack images. We propose a novel two-stage weakly supervised method for volumetric instance segmentation. We show that our approach can generalize not only to BFM Z-Stack data, but to other 3D cell imaging modalities.
• Score: 47.72468932196169
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Despite its broad availability, volumetric information acquisition from Bright-Field Microscopy (BFM) is inherently difficult due to the projective nature of the acquisition process. We investigate the prediction of 3D cell instances from a set of BFM Z-Stack images. We propose a novel two-stage weakly supervised method for volumetric instance segmentation of cells which only requires approximate cell centroids annotation. Created pseudo-labels are thereby refined with a novel refinement loss with Z-stack guidance. The evaluations show that our approach can generalize not only to BFM Z-Stack data, but to other 3D cell imaging modalities. A comparison of our pipeline against fully supervised methods indicates that the significant gain in reduced data collection and labelling results in minor performance difference.

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