Enabling a Single Deep Learning Model for Accurate Gland Instance Segmentation: A Shape-Aware Adversarial Learning Framework
Enabling a Single Deep Learning Model for Accurate Gland Instance Segmentation: A Shape-Aware Adversarial Learning Framework
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启用单一深度学习模型以实现精确的腺体实例分割：形状感知的对抗性学习框架
DOI：
10.1109/tmi.2020.2966594
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发表时间：
2020-01
影响因子：
10.6
通讯作者：
Cheng Kwang-Ting
中科院分区：
文献类型：
--
作者：
Yan Zengqiang;Yang Xin;Cheng Kwang-Ting
分割组织学图像中的腺体实例具有很高的挑战性，因为它不仅需要从复杂的背景中检测腺体，而且需要通过准确的边界检测来分离每个腺体实例。然而，由于人工标注中的边界不确定性问题，基于像素到像素匹配的损失函数对于同时检测腺体和边界的限制太大。最先进的方法采用多模式方案，导致不必要的高模式复杂性和培训过程中的困难。在本文中，我们提出使用单一深度学习模型来准确地分割腺体实例。为了解决边界不确定性问题，我们提出了一种线段级形状相似性度量来计算每个标注边界段与固定搜索范围内检测到的边界段之间的曲线相似度，而不是像素级的匹配。由于分段级度量允许在固定范围内进行形状相似度计算，因此对边界不确定性具有更好的容忍度，更有效地进行边界检测。此外，通过调整搜索范围的半径，分段级形状相似性度量能够处理不同级别的边界不确定性。因此，在我们的框架中，对不同尺度的图像进行下采样和整合，为训练提供全局和局部上下文信息，这有助于分割不同大小的腺体实例。为了减少多尺度训练图像的变化，借鉴对抗性领域自适应的思想，提出了一种伪域自适应特征对齐框架。通过构建基于分段级形状相似性度量的损失函数，结合对抗性损失函数，所提出的形状感知对抗性学习框架实现了单个深度学习模型的腺体实例分割。在2015年MICCAI Gland Challenges数据集上的实验结果表明，该框架在单一深度学习模型下获得了最先进的性能。由于边界不确定性问题在医学图像分割中广泛存在，因此它在其他应用中也具有广泛的适用性。
Segmenting gland instances in histology images is highly challenging as it requires not only detecting glands from a complex background but also separating each individual gland instance with accurate boundary detection. However, due to the boundary uncertainty problem in manual annotations, pixel-to-pixel matching based loss functions are too restrictive for simultaneous gland detection and boundary detection. State-of-the-art approaches adopted multi-model schemes, resulting in unnecessarily high model complexity and difficulties in the training process. In this paper, we propose to use one single deep learning model for accurate gland instance segmentation. To address the boundary uncertainty problem, instead of pixel-to-pixel matching, we propose a segment-level shape similarity measure to calculate the curve similarity between each annotated boundary segment and the corresponding detected boundary segment within a fixed searching range. As the segment-level measure allows location variations within a fixed range for shape similarity calculation, it has better tolerance to boundary uncertainty and is more effective for boundary detection. Furthermore, by adjusting the radius of the searching range, the segment-level shape similarity measure is able to deal with different levels of boundary uncertainty. Therefore, in our framework, images of different scales are down-sampled and integrated to provide both global and local contextual information for training, which is helpful in segmenting gland instances of different sizes. To reduce the variations of multi-scale training images, by referring to adversarial domain adaptation, we propose a pseudo domain adaptation framework for feature alignment. By constructing loss functions based on the segment-level shape similarity measure, combining with the adversarial loss function, the proposed shape-aware adversarial learning framework enables one single deep learning model for gland instance segmentation. Experimental results on the 2015 MICCAI Gland Challenge dataset demonstrate that the proposed framework achieves state-of-the-art performance with one single deep learning model. As the boundary uncertainty problem widely exists in medical image segmentation, it is broadly applicable to other applications.
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DOI：
10.1117/12.2082010
发表时间：
2015-03
期刊：
--
影响因子：
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作者：
K. Sirinukunwattana;D. Snead;N. Rajpoot
通讯作者：
K. Sirinukunwattana;D. Snead;N. Rajpoot
DOI：
--
发表时间：
2016-11
期刊：
--
影响因子：
--
作者：
J. Goldberger;Ehud Ben-Reuven
通讯作者：
J. Goldberger;Ehud Ben-Reuven
影响因子：
10.6
作者：
Manivannan, Siyamalan;Li, Wenqi;McKenna, Stephen J.
通讯作者：
McKenna, Stephen J.
DOI：
--
发表时间：
2017-12
期刊：
--
影响因子：
--
作者：
Lu Jiang;Zhengyuan Zhou;Thomas Leung;Li-Jia Li-Li-Jia-Li-2040091191;Li Fei-Fei-Li-Fei-Fei-48004138
通讯作者：
Lu Jiang;Zhengyuan Zhou;Thomas Leung;Li-Jia Li-Li-Jia-Li-2040091191;Li Fei-Fei-Li-Fei-Fei-48004138
DOI：
10.1109/tpami.2016.2572683
发表时间：
2017-04-01
影响因子：
23.6
作者：
Shelhamer, Evan;Long, Jonathan;Darrell, Trevor
通讯作者：
Darrell, Trevor