T-Net: Nested encoder–decoder architecture for the main vessel segmentation in coronary angiography

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 bl...

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Bibliographic Details
Published in:Neural networks Vol. 128; pp. 216 - 233
Main Authors: Jun, Tae Joon, Kweon, Jihoon, Kim, Young-Hak, Kim, Daeyoung
Format: Journal Article
Language:English
Published: United States Elsevier Ltd 01.08.2020
Subjects:
Convolutional neural network
Coronary angiography
Encoder and decoder
Main vessel segmentation
Coronary angiography
Encoder and decoder
Convolutional neural network
Main vessel segmentation
ISSN:0893-6080, 1879-2782, 1879-2782
Online Access:Get full text
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Summary: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.
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ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2020.05.002