通过非确定性训练时间和确定性测试时间染色标准化提升基于深度学习的细胞核实例分割的泛化能力
Improving Generalization Capability of Deep Learning-Based Nuclei Instance Segmentation by Non-deterministic Train Time and Deterministic Test Time Stain Normalization
AI总结：
本文提出一种结合非确定性训练时间和确定性测试时间染色标准化及集成策略的新方法，有效提升了基于深度学习的细胞核实例分割模型在未见数据集上的泛化能力，在七个测试集上平均性能提升达4.9%至5.9%。
摘要：
随着能够自动扫描和保存全切片组织学图像的数字病理学和显微系统的出现，使用计算机化方法分析所获取图像的趋势日益增长。在不同的组织病理学图像分析任务中，细胞核实例分割在广泛的临床和研究应用中发挥着基础作用。尽管已经提出了许多半自动和全自动的计算机化方法用于细胞核实例分割，但基于深度学习（DL）的方法已被证明能提供最佳性能。然而，此类方法在未见过的数据集上进行测试时，性能通常会下降。在这项工作中，我们提出了一种新方法来提升基于DL的自动分割方法的泛化能力。除了使用最先进的基于DL的模型之一作为基线外，我们的方法结合了非确定性训练时间和确定性测试时间染色标准化，以及集成策略来提升分割性能。我们使用单一训练集训练模型，并在七个测试数据集上评估了其分割性能。结果显示，与基线分割模型相比，所提出的方法在基于Dice分数、聚合Jaccard指数和全景质量分数的细胞核分割上，分别提供了高达4.9%、5.4%和5.9%的更优平均性能。
英文摘要：
With the advent of digital pathology and microscopic systems that can scan and save whole slide histological images automatically, there is a growing trend to use computerized methods to analyze acquired images. Among different histopathological image analysis tasks, nuclei instance segmentation plays a fundamental role in a wide range of clinical and research applications. While many semi- and fully-automatic computerized methods have been proposed for nuclei instance segmentation, deep learning (DL)-based approaches have been shown to deliver the best performances. However, the performance of such approaches usually degrades when tested on unseen datasets. In this work, we propose a novel method to improve the generalization capability of a DL-based automatic segmentation approach. Besides utilizing one of the state-of-the-art DL-based models as a baseline, our method incorporates non-deterministic train time and deterministic test time stain normalization, and ensembling to boost the segmentation performance. We trained the model with one single training set and evaluated its segmentation performance on seven test datasets. Our results show that the proposed method provides up to 4.9%, 5.4%, and 5.9% better average performance in segmenting nuclei based on Dice score, aggregated Jaccard index, and panoptic quality score, respectively, compared to the baseline segmentation model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
