Contemporary approaches to instance segmentation in cell science use 2D or 3D convolutional networks depending on the experiment and data structures. However, limitations in microscopy systems or efforts to prevent phototoxicity commonly require recording sub-optimally sampled data regimes that greatly reduces the utility of such 3D data, especially in crowded environments with significant axial overlap between objects. In such regimes, 2D segmentations are both more reliable for cell morphology and easier to annotate. In this work, we propose the Projection Enhancement Network (PEN), a novel convolutional module which processes the sub-sampled 3D data and produces a 2D RGB semantic compression, and is trained in conjunction with an instance segmentation network of choice to produce 2D segmentations. Our approach combines augmentation to increase cell density using a low-density cell image dataset to train PEN, and curated datasets to evaluate PEN. We show that with PEN, the learned semantic representation in CellPose encodes depth and greatly improves segmentation performance in comparison to maximum intensity projection images as input, but does not similarly aid segmentation in region-based networks like Mask-RCNN. Finally, we dissect the segmentation strength against cell density of PEN with CellPose on disseminated cells from side-by-side spheroids. We present PEN as a data-driven solution to form compressed representations of 3D data that improve 2D segmentations from instance segmentation networks.

翻译：当代细胞科学中的实例分割方法根据实验和数据结构采用二维或三维卷积网络。然而，显微成像系统的局限性或为预防光毒性所采取的措施通常需要记录次优采样数据，这极大地降低了此类三维数据的实用性，尤其在物体间存在显著轴向重叠的密集环境中。在此类采样条件下，二维分割既能更可靠地反映细胞形态，也更易于标注。本文提出投影增强网络（PEN）——一种新型卷积模块，用于处理次采样三维数据并生成二维RGB语义压缩表示，该模块可与任意选定的实例分割网络联合训练，以产出二维分割结果。我们采用低密度细胞图像数据集训练PEN以增强细胞密度，并利用精心整理的评估数据集进行验证。实验表明，与最大强度投影图像作为输入相比，PEN在CellPose中编码的语义表征能有效捕捉深度信息，显著提升分割性能，但对Mask-RCNN等基于区域的分割网络并无类似助益。最后，我们基于旁侧球体解离细胞，系统剖析了CellPose结合PEN在不同细胞密度下的分割强度。本文提出PEN作为一种数据驱动解决方案，通过构建三维数据的压缩表征来改进实例分割网络的二维分割效果。