Takeshi Nishi1, Rikiya Yamashita2, Shinji Imura3, Kazuya Tateishi4, Hideki Kitahara4, Yoshio Kobayashi4, Paul G Yock3, Peter J Fitzgerald3, Yasuhiro Honda5. 1. Division of Cardiovascular Medicine, Stanford University School of Medicine and Stanford Cardiovascular Institute, Stanford, CA, USA; Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Chiba, Japan. Electronic address: takeshi24@hotmail.co.jp. 2. Department of Biomedical Data Science, Stanford University School of Medicine, CA, USA. 3. Division of Cardiovascular Medicine, Stanford University School of Medicine and Stanford Cardiovascular Institute, Stanford, CA, USA. 4. Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Chiba, Japan. 5. Division of Cardiovascular Medicine, Stanford University School of Medicine and Stanford Cardiovascular Institute, Stanford, CA, USA. Electronic address: yshonda@stanford.edu.
Abstract
BACKGROUND: Accurate segmentation of the coronary arteries with intravascular ultrasound (IVUS) is important to optimize coronary stent implantation. Recently, deep learning (DL) methods have been proposed to develop automatic IVUS segmentation. However, most of those have been limited to segmenting the lumen and vessel (i.e. lumen-intima and media-adventitia borders), not applied to segmenting stent dimension. Hence, this study aimed to develop a DL method for automatic IVUS segmentation of stent area in addition to lumen and vessel area. METHODS: This study included a total of 45,449 images from 1576 IVUS pullback runs. The datasets were randomly split into training, validation, and test datasets (0.7:0.15:0.15). After developing the DL-based system to segment IVUS images using the training and validation datasets, we evaluated the performance through the independent test dataset. RESULTS: The DL-based segmentation correlated well with the expert-analyzed segmentation with a mean intersection over union (± standard deviation) of 0.80 ± 0.20, correlation coefficient of 0.98 (95% confidence intervals: 0.98 to 0.98), 0.96 (0.95 to 0.96), and 0.96 (0.96 to 0.96) for lumen, vessel, and stent area, and the mean difference (± standard deviation) of 0.02 ± 0.57, -0.44 ± 1.56 and - 0.17 ± 0.74 mm2 for lumen, vessel and stent area, respectively. CONCLUSION: This automated DL-based IVUS segmentation of lumen, vessel and stent area showed an excellent agreement with manual segmentation by experts, supporting the feasibility of artificial intelligence-assisted IVUS assessment in patients undergoing coronary stent implantation.
BACKGROUND: Accurate segmentation of the coronary arteries with intravascular ultrasound (IVUS) is important to optimize coronary stent implantation. Recently, deep learning (DL) methods have been proposed to develop automatic IVUS segmentation. However, most of those have been limited to segmenting the lumen and vessel (i.e. lumen-intima and media-adventitia borders), not applied to segmenting stent dimension. Hence, this study aimed to develop a DL method for automatic IVUS segmentation of stent area in addition to lumen and vessel area. METHODS: This study included a total of 45,449 images from 1576 IVUS pullback runs. The datasets were randomly split into training, validation, and test datasets (0.7:0.15:0.15). After developing the DL-based system to segment IVUS images using the training and validation datasets, we evaluated the performance through the independent test dataset. RESULTS: The DL-based segmentation correlated well with the expert-analyzed segmentation with a mean intersection over union (± standard deviation) of 0.80 ± 0.20, correlation coefficient of 0.98 (95% confidence intervals: 0.98 to 0.98), 0.96 (0.95 to 0.96), and 0.96 (0.96 to 0.96) for lumen, vessel, and stent area, and the mean difference (± standard deviation) of 0.02 ± 0.57, -0.44 ± 1.56 and - 0.17 ± 0.74 mm2 for lumen, vessel and stent area, respectively. CONCLUSION: This automated DL-based IVUS segmentation of lumen, vessel and stent area showed an excellent agreement with manual segmentation by experts, supporting the feasibility of artificial intelligence-assisted IVUS assessment in patients undergoing coronary stent implantation.
Authors: Tobias Wissel; Katharina A Riedl; Klaus Schaefers; Hannes Nickisch; Fabian J Brunner; Nikolas D Schnellbaecher; Stefan Blankenberg; Moritz Seiffert; Michael Grass Journal: J Med Imaging (Bellingham) Date: 2022-03-28