Bidirectional Mapping-Based Domain Adaptation for Nucleus Detection in Cross-Modality Microscopy Images.
Bidirectional Mapping-Based Domain Adaptation for Nucleus Detection in Cross-Modality Microscopy Images.
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DOI：
10.1109/tmi.2020.3042789
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发表时间：
2021-10
影响因子：
10.6
通讯作者：
Ghosh D
中科院分区：
文献类型：
--
作者：
Xing F;Cornish TC;Bennett TD;Ghosh D
细胞或细胞核检测是显微图像分析中的一项基本任务，最近通过使用深度神经网络实现了最先进的性能。然而，训练有监督的深度模型，如卷积神经网络（CNN），通常需要足够的注释图像数据，这在某些应用中非常昂贵或不可用。此外，当将CNN应用于新数据集时，通常会注释这些目标数据集中的单个细胞/细胞核以进行模型重新学习，从而导致低效和低通量的图像分析。为了解决这些问题，我们提出了一个双向的，对抗性的域自适应方法的跨模态显微图像数据的细胞核检测。具体地，该方法学习用于具有源到目标和目标到源图像平移的个体细胞核检测的深度回归模型。此外，我们显式地将这种无监督域自适应方法扩展到半监督学习情况，并进一步提高核检测性能。我们评估所提出的方法在三个跨模态显微图像数据集，其中涵盖了各种各样的显微成像协议或模式，并获得了显着改善核检测相比，参考基线方法。此外，我们的半监督方法与最近使用所有真实的目标训练标签训练的全监督学习模型相比非常具有竞争力。
Cell or nucleus detection is a fundamental task in microscopy image analysis and has recently achieved state-of-the-art performance by using deep neural networks. However, training supervised deep models such as convolutional neural networks (CNNs) usually requires sufficient annotated image data, which is prohibitively expensive or unavailable in some applications. Additionally, when applying a CNN to new datasets, it is common to annotate individual cells/nuclei in those target datasets for model re-learning, leading to inefficient and low-throughput image analysis. To tackle these problems, we present a bidirectional, adversarial domain adaptation method for nucleus detection on cross-modality microscopy image data. Specifically, the method learns a deep regression model for individual nucleus detection with both source-to-target and target-to-source image translation. In addition, we explicitly extend this unsupervised domain adaptation method to a semi-supervised learning situation and further boost the nucleus detection performance. We evaluate the proposed method on three cross-modality microscopy image datasets, which cover a wide variety of microscopy imaging protocols or modalities, and obtain a significant improvement in nucleus detection compared to reference baseline approaches. In addition, our semi-supervised method is very competitive with recent fully supervised learning models trained with all real target training labels.