Facilitating cell segmentation with the projection-enhancement network.
Facilitating cell segmentation with the projection-enhancement network.
复制标题
通过投影增强网络促进细胞分割。
DOI：
10.1088/1478-3975/acfe53
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发表时间：
2023
期刊：
影响因子：
2
通讯作者：
Sun,Bo
中科院分区：
文献类型：
--
作者：
Eddy,ChristopherZ;Naylor,Austin;Cunningham,ChristianT;Sun,Bo
细胞科学中的实例分割的当代方法根据实验和数据结构使用2D或3D卷积网络。然而，显微镜系统的局限性或防止光毒性的努力通常需要记录次优采样数据，这大大降低了此类3D数据的实用性，特别是在对象之间具有显着轴向重叠的拥挤样本空间中。在这种情况下，2D分割对于细胞形态更可靠，并且更容易注释。在这项工作中，我们提出了投影增强网络（PEN），一种新的卷积模块，它处理子采样的3D数据并产生2D RGB语义压缩，并与选择的实例分割网络一起训练以产生2D分割。我们的方法结合了增强来增加细胞密度，使用低密度细胞图像数据集来训练PEN，并使用策划的数据集来评估PEN。我们表明，使用PEN，CellPose中学习的语义表示与作为输入的最大强度投影图像相比对深度进行编码并大大提高了分割性能，但在Mask-RCNN等基于区域的网络中并没有类似的帮助分割。最后，我们剖析了PEN与CellPose在来自并排球体的播散细胞上的分割强度对细胞密度的影响。我们提出PEN作为一个数据驱动的解决方案，以形成压缩表示的3D数据，提高2D分割从实例分割网络。
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 that greatly reduces the utility of such 3D data, especially in crowded sample space 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.