An approach to automatic segmentation of 3D intravascular ultrasound images

Abstract Background: Intravascular ultrasound (IVUS) is the golden standard in accessing the coronary lesions, stenosis, and atherosclerosis plaques. In this paper, a fully-automatic approach by an 8-layer U-Net is developed to segment the coronary artery lumen and the area bounded by external elastic membrane (EEM), i.e. cross section area (EEM-CSA). The database comprises of single-vendor and single-frequency IVUS data. Particularly, the proposed data augmentation of MeshGrid combined with flip and rotation operations is implemented, improving the model performance without pre- or post-processing of the raw IVUS images.Results: The mean intersection of union (mIoU) of 0.937 and 0.804 for the lumen and EEM-CSA respectively were achieved, which exceeded the manual labeling accuracy of the clinician.Conclusion: The accuracy shown by the proposed method is sufficient for subsequent reconstruction of 3D IVUS images, which is essential for doctors’ diagnosis in the tissue characterization of coronary artery walls and plaque compositions, qualitatively and quantitatively.

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Automatic Segmentation of Coronary Lumen and External Elastic Membrane in Intravascular Ultrasound Images Using 8-layer U-Net

10.21203/rs.3.rs-42239/v5 ◽

2021 ◽

Author(s):

Liang Dong ◽

Wenbing Jiang ◽

Wei Lu ◽

Jun Jiang ◽

Ya Zhao ◽

...

Keyword(s):

Coronary Artery ◽

Intravascular Ultrasound ◽

Tissue Characterization ◽

Data Augmentation ◽

Automatic Segmentation ◽

Model Performance ◽

Single Frequency ◽

Elastic Membrane ◽

Ultrasound Images ◽

External Elastic Membrane

Abstract Background: Intravascular ultrasound (IVUS) is the golden standard in accessing the coronary lesions, stenosis, and atherosclerosis plaques. In this paper, a fully-automatic approach by an 8-layer U-Net is developed to segment the coronary artery lumen and the area bounded by external elastic membrane (EEM), i.e. cross section area (EEM-CSA). The database comprises of single-vendor and single-frequency IVUS data. Particularly, the proposed data augmentation of MeshGrid combined with flip and rotation operations is implemented, improving the model performance without pre- or post-processing of the raw IVUS images.Results: The mean intersection of union (MIoU) of 0.937 and 0.804 for the lumen and EEM-CSA respectively were achieved, which exceeded the manual labeling accuracy of the clinician. Conclusion: The accuracy shown by the proposed method is sufficient for subsequent reconstruction of 3D IVUS images, which is essential for doctors’ diagnosis in the tissue characterization of coronary artery walls and plaque compositions, qualitatively and quantitatively.

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Automatic Segmentation of Coronary Lumen and External Elastic Membrane in Intravascular Ultrasound Images Using 8-layer U-Net

10.21203/rs.3.rs-42239/v2 ◽

2020 ◽

Author(s):

Liang Dong ◽

Wei Lu ◽

Jun Jiang ◽

Ya Zhao ◽

Xiangfen Song ◽

...

Keyword(s):

Coronary Artery ◽

Intravascular Ultrasound ◽

Tissue Characterization ◽

Data Augmentation ◽

Automatic Segmentation ◽

Model Performance ◽

Single Frequency ◽

Elastic Membrane ◽

Ultrasound Images ◽

External Elastic Membrane

Abstract Background: Intravascular ultrasound (IVUS) is the golden standard in accessing the coronary lesions, stenosis, and atherosclerosis plaques. In this paper, a fully-automatic approach by an 8-layer U-Net is developed to segment the coronary artery lumen and the area bounded by external elastic membrane, i.e. EEM cross section area (EEM-CSA). The database comprises of single-vendor and single-frequency IVUS data. Particularly, the proposed data augmentation of MeshGrid combined with flip and rotation operations is implemented, improving the model performance without pre- or post-processing of the raw IVUS images.Results: The mean intersection of union (mIoU) of 0.941 and 0.750 for the lumen and EEM-CSA respectively were achieved, which exceeded the manual labeling accuracy of the clinician. Conclusion: The accuracy shown by the proposed method is sufficient for subsequent reconstruction of 3D IVUS images, which is essential for doctors’ diagnosis in the tissue characterization of coronary artery walls and plaque compositions, qualitatively and quantitatively.

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Deep learning for calcium segmentation in intravascular ultrasound images

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2021-1021 ◽

2021 ◽

Vol 7 (1) ◽

pp. 96-100

Author(s):

Lennart Bargsten ◽

Katharina A. Riedl ◽

Tobias Wissel ◽

Fabian J. Brunner ◽

Klaus Schaefers ◽

...

Keyword(s):

Intravascular Ultrasound ◽

Hausdorff Distance ◽

Automatic Segmentation ◽

Image Data ◽

Ultrasound Images ◽

Dice Coefficient ◽

Clinical Workflow ◽

Auxiliary Data ◽

Training Approach ◽

Percutaneous Coronary

Abstract Knowing the shape of vascular calcifications is crucial for appropriate planning and conductance of percutaneous coronary interventions. The clinical workflow can therefore benefit from automatic segmentation of calcified plaques in intravascular ultrasound (IVUS) images. To solve segmentation problems with convolutional neural networks (CNNs), large datasets are usually required. However, datasets are often rather small in the medical domain. Hence, developing and investigating methods for increasing CNN performance on small datasets can help on the way towards clinically relevant results. We compared two state-of-the-art CNN architectures for segmentation, U-Net and DeepLabV3, and investigated how incorporating auxiliary image data with vessel wall and lumen annotations improves the calcium segmentation performance by using these either for pretraining or multi-task training. DeepLabV3 outperforms U-Net with up to 6.3 % by means of the Dice coefficient and 36.5 % by means of the average Hausdorff distance. Using auxiliary data improves the segmentation performance in both cases, whereas the multi-task approach outperforms the pre-training approach. The improvements of the multi-task approach in contrast to not using auxiliary data at all is 5.7 % for the Dice coefficient and 42.9 % for the average Hausdorff distance. Automatic segmentation of calcified plaques in IVUS images is a demanding task due to their relatively small size compared to the image dimensions and due to visual ambiguities with other image structures. We showed that this problem can generally be tackled by CNNs. Furthermore, we were able to improve the performance by a multi-task learning approach with auxiliary segmentation data.

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