In this paper, we proposed nested encoder-decoder architecture named T-Net. T-Net consists of several small encoder-decoders for each block constituting convolutional network. T-Net overcomes the limitation that U-Net can only have a single set of the concatenate layer between encoder and decoder block. To be more precise, the U-Net symmetrically forms the concatenate layers, so the low-level feature of the encoder is connected to the latter part of the decoder, and the high-level feature is connected to the beginning of the decoder. T-Net arranges the pooling and up-sampling appropriately during the encoding process, and likewise during the decoding process so that feature-maps of various sizes are obtained in a single block. As a result, all features from the low-level to the high-level extracted from the encoder are delivered from the beginning of the decoder to predict a more accurate mask. We evaluated T-Net for the problem of segmenting three main vessels in coronary angiography images. The experiment consisted of a comparison of U-Net and T-Nets under the same conditions, and an optimized T-Net for the main vessel segmentation. As a result, T-Net recorded a Dice Similarity Coefficient score (DSC) of 83.77%, 10.69% higher than that of U-Net, and the optimized T-Net recorded a DSC of 88.97% which was 15.89% higher than that of U-Net. In addition, we visualized the weight activation of the convolutional layer of T-Net and U-Net to show that T-Net actually predicts the mask from earlier decoders. Therefore, we expect that T-Net can be effectively applied to other similar medical image segmentation problems.

中文翻译：

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T-Net：嵌套式编码器-解码器体系结构，用于冠状动脉造影中的主血管分割。

在本文中，我们提出了称为T-Net的嵌套编码器-解码器体系结构。T-Net由构成卷积网络的每个块的几个小型编码器/解码器组成。T-Net克服了U-Net在编码器和解码器块之间只能具有一组连接层的限制。更确切地说，U-Net对称地形成连接层，因此编码器的低级功能连接到解码器的后半部分，而高级功能则连接到解码器的开始。T-Net在编码过程中以及在解码过程中适当地安排了池化和上采样，以便在单个块中获得各种大小的特征图。结果是，从编码器提取的所有功能（从低级到高级）都从解码器的开头传送，以预测更准确的掩码。我们评估了T-Net在冠状动脉造影图像中分割三个主要血管的问题。该实验包括在相同条件下对U-Net和T-Net进行比较，以及对主血管分割进行优化的T-Net。结果，T-Net的骰子相似系数得分（DSC）为83.77％，比U-Net的得分高10.69％，而优化的T-Net的DSC的DSC则为88.97％，比U-Net的高15.89％。 U-Net。此外，我们可视化了T-Net和U-Net的卷积层的权重激活，以表明T-Net实际上可以预测早期解码器的掩码。因此，