  In this paper, we proposed T-Net containing a small encoder-decoder inside
the encoder-decoder structure (EDiED). 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 encoder 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 0.815, 0.095 higher than that of U-Net,
and the optimized T-Net recorded a DSC of 0.890 which was 0.170 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.
