A deep learning segmentation strategy that minimizes the amount of manually annotated images.
A deep learning segmentation strategy that minimizes the amount of manually annotated images.
复制标题
一种深度学习分割策略，最大限度地减少了手动标注图像的数量。
DOI：
10.12688/f1000research.52026.2
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Wallace K
中科院分区：
文献类型：
--
作者：
Pécot T;Alekseyenko A;Wallace K
深度学习彻底改变了图像的自动处理。虽然深度卷积神经网络已经证明了对显微镜获取的许多生物对象的惊人分割结果，但这项技术的良好性能依赖于大型训练数据集。在本文中，我们提出了一种策略，以尽量减少在手动注释图像分割所花费的时间。它涉及使用高效的开源注释工具，通过数据增强人工增加训练数据集，使用条件生成对抗网络创建人工数据集，以及语义和实例分割的组合。我们评估了这些方法中的每一种对人类癌前息肉活检的2D宽视野图像中的细胞核分割的影响，以确定最佳策略。
Deep learning has revolutionized the automatic processing of images. While deep convolutional neural networks have demonstrated astonishing segmentation results for many biological objects acquired with microscopy, this technology's good performance relies on large training datasets. In this paper, we present a strategy to minimize the amount of time spent in manually annotating images for segmentation. It involves using an efficient and open source annotation tool, the artificial increase of the training dataset with data augmentation, the creation of an artificial dataset with a conditional generative adversarial network and the combination of semantic and instance segmentations. We evaluate the impact of each of these approaches for the segmentation of nuclei in 2D widefield images of human precancerous polyp biopsies in order to define an optimal strategy.