NuSeT: A deep learning tool for reliably separating and analyzing crowded cells.
NuSeT: A deep learning tool for reliably separating and analyzing crowded cells.
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DOI：
10.1371/journal.pcbi.1008193
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发表时间：
2020-09
影响因子：
4.3
通讯作者：
Liphardt JT
中科院分区：
文献类型：
--
作者：
Yang L;Ghosh RP;Franklin JM;Chen S;You C;Narayan RR;Melcher ML;Liphardt JT
在显微镜图像中分割细胞核是生物学研究和临床应用中普遍存在的任务。不幸的是，在标准的基于深度学习的模型中，分割低对比度的重叠对象是一个主要的瓶颈。我们报告了一种基于深度学习的核分割工具（NuSeT），该工具可以跨多种类型的荧光成像数据准确地分割核。使用由U-Net和区域建议网络（RPN）组成的混合网络，然后是分水岭步骤，我们在检测和描绘不同复杂性的2D和3D图像中的核边界方面取得了卓越的性能。通过对包含非细胞伪影的合成图像进行前景归一化和额外训练，NuSeT改进了核检测并减少了误报。NuSeT解决了核分割中的常见挑战，如核信号和形状的可变性，有限的训练样本量和样品制备伪影。与其他分割模型相比，NuSeT在生成准确的分割掩模和为接触核分配边界方面一直表现更好。细胞核的大小和形状是细胞周期阶段和细胞病理的重要指标。在复杂的环境中，特别是对于高价值但低质量的样品，细胞核的有效分割对于检测病理状态至关重要。在大多数情况下，生物特征仍然使用传统的分割方法进行分割，需要人工管理分割，这非常耗时，并且无法达到最佳性能。虽然最近深度学习工具的激增极大地帮助了分割任务的自动化，但现有的平台无法有效地分割具有重叠核边界的拥挤细胞中的细胞核。NuSeT吸收了语义分割（U-Net）和实例分割（Mask R-CNN）的优点，在分析复杂的三维细胞簇和在拥挤、动态环境中跟踪细胞核方面，始终优于其他最先进的深度学习分割模型。NuSeT可以处理荧光和组织病理学图像样本。我们还开发了一个用于定制训练和分割的图形用户界面，这将大大有助于在广泛的图像类型中轻松和准确地进行图像分割。
Segmenting cell nuclei within microscopy images is a ubiquitous task in biological research and clinical applications. Unfortunately, segmenting low-contrast overlapping objects that may be tightly packed is a major bottleneck in standard deep learning-based models. We report a Nuclear Segmentation Tool (NuSeT) based on deep learning that accurately segments nuclei across multiple types of fluorescence imaging data. Using a hybrid network consisting of U-Net and Region Proposal Networks (RPN), followed by a watershed step, we have achieved superior performance in detecting and delineating nuclear boundaries in 2D and 3D images of varying complexities. By using foreground normalization and additional training on synthetic images containing non-cellular artifacts, NuSeT improves nuclear detection and reduces false positives. NuSeT addresses common challenges in nuclear segmentation such as variability in nuclear signal and shape, limited training sample size, and sample preparation artifacts. Compared to other segmentation models, NuSeT consistently fares better in generating accurate segmentation masks and assigning boundaries for touching nuclei. Nuclear size and shape are essential indicators of cell cycle stage and cellular pathology. Efficient segmentation of nuclei in complex environments, especially for high-value yet low-quality samples is critical for detecting pathological states. In the majority of cases, biological features are still segmented using traditional segmentation methods requiring manual curation of segmentations, which is hugely time-consuming and does not achieve optimal performance. While a recent surge in deep learning tools has helped tremendously with the automation of segmentation tasks, existing platforms inefficiently segment nuclei in crowded cells with overlapping nuclear boundaries. NuSeT, assimilates the advantages of semantic segmentation (U-Net) and instance segmentation (Mask R-CNN), and consistently outperforms other start-of-the-art deep learning segmentation models in analyzing complex three-dimensional cell clusters and in tracking nuclei in crowded, dynamic environments. NuSeT can work with both fluorescent and histopathology image samples. We have also developed a graphic user interface for customized training and segmentation, that will aid considerably in the ease and accuracy of image segmentation in a wide range of image types.
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影响因子：
4.6
作者：
Abdolhoseini, Mahmoud;Kluge, Murielle G.;Johnson, Sarah J.
通讯作者：
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DOI：
10.1109/tpami.2018.2844175
发表时间：
2020-02-01
影响因子：
23.6
作者：
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通讯作者：
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影响因子：
4.6
作者：
He Y;Gong H;Xiong B;Xu X;Li A;Jiang T;Sun Q;Wang S;Luo Q;Chen S
通讯作者：
Chen S
影响因子：
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作者：
Al-Kofahi Y;Zaltsman A;Graves R;Marshall W;Rusu M
通讯作者：
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