论文标题
BFS-NET：明亮场显微镜Z-stacks的弱监督细胞实例分割
BFS-Net: Weakly Supervised Cell Instance Segmentation from Bright-Field Microscopy Z-Stacks
论文作者
论文摘要
尽管有广泛的可用性，但由于采集过程的投射性质，从明亮场显微镜（BFM）中获取的体积信息固有地很困难。我们从一组BFM Z-stack图像中研究了3D单元格实例的预测。我们提出了一种新型的两阶段弱监督方法，用于细胞的体积实例分割，这仅需要近似细胞质心注释。因此，创建的伪标签是通过Z-stack Guidance进行了新颖的改进损失来完善的。评估表明，我们的方法不仅可以推广到BFM Z-stack数据，还可以推广到其他3D细胞成像模式。我们的管道与完全监督的方法的比较表明，减少数据收集和标记的显着增益会导致较小的性能差异。
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.
