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首页> 外文会议>IEEE International Conference on Bioinformatics and Biomedicine >Multi-Stage Attention-Unet for Wireless Capsule Endoscopy Image Bleeding Area Segmentation
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Multi-Stage Attention-Unet for Wireless Capsule Endoscopy Image Bleeding Area Segmentation

机译：用于无线胶囊内窥镜图像出血区域分割的多阶段注意力-Unet

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摘要

Bleeding in gastrointestinal tract (GI) is caused by many different diseases and may lead to serious consequence if not being treated correctly. The most effective method to locate bleeding area is using wireless capsule endoscopy (WCE). However, the amount of WCE images of one patient is so large that even a professional physician may take a long time on analyzing them, and human errors may also occur with work time increasing. Thus the computer-aided diagnosis methods become more attractive. In this paper, we propose a novel deep learning based method for WCE images bleeding area segmentation. We design multi-stage architecture and attention blocks to deal with small areas segmentation. The latter stages' input is the combination of feature maps transferred from former stages so that even very deep layers can obtain small areas features, and attention blocks help shallow layers to better extract small areas features thus they can transfer more useful information to latter stages. Extensive experiments are conducted on public available WCE image dataset to show the effect of multi-stage architecture and attention blocks. Compared with other bleeding areas segmentation methods, our approach achieves state-of-the-art performance with 98.5% overall accuracy, 90.1% mean accuracy and 86.3% mean intersection over union (IoU). There is a 10.7% improvement on mean IoU compared with the previous state-of-the-art WCE bleeding segmentation method.
机译：胃肠道（GI）出血是由许多不同的疾病引起的，如果治疗不当，可能会导致严重的后果。定位出血区域的最有效方法是使用无线胶囊内窥镜检查（WCE）。但是，一个患者的WCE图像数量如此之大，以至于即使是专业医师也可能需要花费很长时间来分析它们，并且随着工作时间的增加，也可能发生人为错误。因此，计算机辅助的诊断方法变得更具吸引力。在本文中，我们提出了一种新的基于深度学习的WCE图像出血区域分割方法。我们设计了多阶段的体系结构和注意力块来处理小区域分割。后一阶段的输入是从前一阶段转移过来的特征图的组合，因此，即使非常深的图层也可以获得小面积的特征，而注意块则可以帮助浅层更好地提取小面积的特征，从而可以将更多有用的信息转移到后一阶段。对公开的WCE图像数据集进行了广泛的实验，以显示多阶段架构和关注块的效果。与其他出血区域分割方法相比，我们的方法以98.5％的整体准确度，90.1％的平均准确度和86.3％的平均经交叉点（IoU）达到了最先进的性能。与以前的最新WCE出血分割方法相比，平均IoU改善了10.7％。

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