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Open AccessArticle

Image Segmentation of the Ventricular Septum in Fetal Cardiac Ultrasound Videos Based on Deep Learning Using Time-Series Information

1
Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
2
Department of Obstetrics and Gynecology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan
3
Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
4
Artificial Intelligence Laboratory, Fujitsu Laboratories Ltd., 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki, Kanagawa 211-8588, Japan
5
RIKEN AIP-Fujitsu Collaboration Center, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
6
Biomedical Science and Engineering Track, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
7
Department of Obstetrics and Gynecology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo 142-8666, Japan
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Biomolecules 2020, 10(11), 1526; https://doi.org/10.3390/biom10111526
Received: 8 October 2020 / Revised: 4 November 2020 / Accepted: 5 November 2020 / Published: 8 November 2020
(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)
Image segmentation is the pixel-by-pixel detection of objects, which is the most challenging but informative in the fundamental tasks of machine learning including image classification and object detection. Pixel-by-pixel segmentation is required to apply machine learning to support fetal cardiac ultrasound screening; we have to detect cardiac substructures precisely which are small and change shapes dynamically with fetal heartbeats, such as the ventricular septum. This task is difficult for general segmentation methods such as DeepLab v3+, and U-net. Hence, here we proposed a novel segmentation method named Cropping-Segmentation-Calibration (CSC) that is specific to the ventricular septum in ultrasound videos in this study. CSC employs the time-series information of videos and specific section information to calibrate the output of U-net. The actual sections of the ventricular septum were annotated in 615 frames from 421 normal fetal cardiac ultrasound videos of 211 pregnant women who were screened. The dataset was assigned a ratio of 2:1, which corresponded to a ratio of the training to test data, and three-fold cross-validation was conducted. The segmentation results of DeepLab v3+, U-net, and CSC were evaluated using the values of the mean intersection over union (mIoU), which were 0.0224, 0.1519, and 0.5543, respectively. The results reveal the superior performance of CSC. View Full-Text
Keywords: congenital heart disease; fetal cardiac ultrasound video; deep learning; segmentation; ventricular septum congenital heart disease; fetal cardiac ultrasound video; deep learning; segmentation; ventricular septum
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MDPI and ACS Style

Dozen, A.; Komatsu, M.; Sakai, A.; Komatsu, R.; Shozu, K.; Machino, H.; Yasutomi, S.; Arakaki, T.; Asada, K.; Kaneko, S.; Matsuoka, R.; Aoki, D.; Sekizawa, A.; Hamamoto, R. Image Segmentation of the Ventricular Septum in Fetal Cardiac Ultrasound Videos Based on Deep Learning Using Time-Series Information. Biomolecules 2020, 10, 1526. https://doi.org/10.3390/biom10111526

AMA Style

Dozen A, Komatsu M, Sakai A, Komatsu R, Shozu K, Machino H, Yasutomi S, Arakaki T, Asada K, Kaneko S, Matsuoka R, Aoki D, Sekizawa A, Hamamoto R. Image Segmentation of the Ventricular Septum in Fetal Cardiac Ultrasound Videos Based on Deep Learning Using Time-Series Information. Biomolecules. 2020; 10(11):1526. https://doi.org/10.3390/biom10111526

Chicago/Turabian Style

Dozen, Ai; Komatsu, Masaaki; Sakai, Akira; Komatsu, Reina; Shozu, Kanto; Machino, Hidenori; Yasutomi, Suguru; Arakaki, Tatsuya; Asada, Ken; Kaneko, Syuzo; Matsuoka, Ryu; Aoki, Daisuke; Sekizawa, Akihiko; Hamamoto, Ryuji. 2020. "Image Segmentation of the Ventricular Septum in Fetal Cardiac Ultrasound Videos Based on Deep Learning Using Time-Series Information" Biomolecules 10, no. 11: 1526. https://doi.org/10.3390/biom10111526

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