TY - GEN
T1 - Fully automatic segmentation of coronary arteries based on deep neural network in intravascular ultrasound images
AU - Kim, Sekeun
AU - Jang, Yeonggul
AU - Jeon, Byunghwan
AU - Hong, Youngtaek
AU - Shim, Hackjoon
AU - Chang, Hyukjae
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018
Y1 - 2018
N2 - Accurate segmentation of coronary arteries is important for the diagnosis of cardiovascular diseases. In this paper, we propose a fully convolutional neural network to efficiently delineate the boundaries of the wall and lumen of the coronary arteries using intravascular ultrasound (IVUS) images. Our network addresses multi-label segmentation of the wall and lumen areas at the same time. The primary body of the proposed network is U-shaped which contains the encoding and decoding paths to learn rich hierarchical representations. The multi-scale input layer is adapted to take a multi-scale input. We deploy a multi-label loss function with weighted pixel-wise cross-entropy to alleviate imbalance of the rate of background, wall, and lumen. The proposed method is compared with three existing methods and the segmentation results are measured on four metrics, dice similarity coefficient, Jaccard index, percentage of area difference, and Hausdorff distance on totally 38,478 IVUS images from 35 subjects.
AB - Accurate segmentation of coronary arteries is important for the diagnosis of cardiovascular diseases. In this paper, we propose a fully convolutional neural network to efficiently delineate the boundaries of the wall and lumen of the coronary arteries using intravascular ultrasound (IVUS) images. Our network addresses multi-label segmentation of the wall and lumen areas at the same time. The primary body of the proposed network is U-shaped which contains the encoding and decoding paths to learn rich hierarchical representations. The multi-scale input layer is adapted to take a multi-scale input. We deploy a multi-label loss function with weighted pixel-wise cross-entropy to alleviate imbalance of the rate of background, wall, and lumen. The proposed method is compared with three existing methods and the segmentation results are measured on four metrics, dice similarity coefficient, Jaccard index, percentage of area difference, and Hausdorff distance on totally 38,478 IVUS images from 35 subjects.
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U2 - 10.1007/978-3-030-01364-6_18
DO - 10.1007/978-3-030-01364-6_18
M3 - Conference contribution
AN - SCOPUS:85055775048
SN - 9783030013639
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 161
EP - 168
BT - Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis - 7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018 Held in Conjunction with MICCAI 2018
A2 - Lee, Su-Lin
A2 - Trucco, Emanuele
A2 - Maier-Hein, Lena
A2 - Moriconi, Stefano
A2 - Albarqouni, Shadi
A2 - Jannin, Pierre
A2 - Balocco, Simone
A2 - Zahnd, Guillaume
A2 - Mateus, Diana
A2 - Taylor, Zeike
A2 - Demirci, Stefanie
A2 - Stoyanov, Danail
A2 - Sznitman, Raphael
A2 - Martel, Anne
A2 - Cheplygina, Veronika
A2 - Granger, Eric
A2 - Duong, Luc
PB - Springer Verlag
T2 - 7th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2018, and the 3rd International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2018, held in conjunction with the 21th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 16 September 2018
ER -