Iwata Hiroyasu
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:1485-1488. doi: 10.1109/EMBC.2017.8037116.
This paper explores the effectiveness of applying a deep learning based method to segment the amniotic fluid and fetal tissues in fetal ultrasound (US) images. The deeply learned model firstly encodes the input image into down scaled feature maps by convolution and pooling structures, then up-scale the feature maps to confidence maps by corresponded un-pooling and convolution layers. Additional convolution layers with 1×1 sized kernels are adopted to enhance the feature representations, which could be used to further improve the discriminative learning of our model. We effectively update the weights of the network by fine-tuning on part of the layers from a pre-trained model. By conducting experiments using clinical data, the feasibility of our proposed approach is compared and discussed. The result proves that this work achieves satisfied results for segmentation of specific anatomical structures from US images.
本文探讨了应用基于深度学习的方法对胎儿超声（US）图像中的羊水和胎儿组织进行分割的有效性。深度模型首先通过卷积和池化结构将输入图像编码为下采样特征图，然后通过相应的反池化和卷积层将特征图上采样为置信度图。采用具有1×1大小内核的附加卷积层来增强特征表示，这可用于进一步改善模型的判别学习。我们通过对预训练模型的部分层进行微调来有效地更新网络权重。通过使用临床数据进行实验，对我们提出的方法的可行性进行了比较和讨论。结果证明，这项工作在从超声图像中分割特定解剖结构方面取得了令人满意的结果。
