scholarly journals Deep learning for calcium segmentation in intravascular ultrasound images

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.

scholarly journals Deep learning for guidewire detection in intravascular ultrasound images

2021 ◽  
Vol 7 (1) ◽  
pp. 106-110
Author(s):  
Lennart Bargsten ◽  
Daniel Klisch ◽  
Katharina A. Riedl ◽  
Tobias Wissel ◽  
Fabian J. Brunner ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intravascular Ultrasound ◽  
Hausdorff Distance ◽  
Area Under Curve ◽  
Ground Truth ◽  
Automated Analysis ◽  
Ultrasound Images ◽  
Percutaneous Coronary Interventions ◽  
Task Learning ◽  
Percutaneous Coronary

Abstract Algorithms for automated analysis of intravascular ultrasound (IVUS) images can be disturbed by guidewires, which are often encountered when treating bifurcations in percutaneous coronary interventions. Detecting guidewires in advance can therefore help avoiding potential errors. This task is not trivial, since guidewires appear rather small compared to other relevant objects in IVUS images. We employed CNNs with additional multi-task learning as well as different guidewire-specific regularizations to enable and improve guidewire detection. In this context, we developed a network block which generates heatmaps that highlight guidewires without the need of localization annotations. The guidewire detection results reach values of 0.931 in terms of the F1-score and 0.996 in terms of area under curve (AUC). Comparing thresholded guidewire heatmaps with ground truth segmentation masks leads to a Dice score of 23.1 % and an average Hausdorff distance of 1.45 mm. Guidewire detection has proven to be a task that CNNs can handle quite well. Employing multi-task learning and guidewire-specific regularizations further improve detection results and enable generation of heatmaps that indicate the position of guidewires without actual labels.

scholarly journals Paraspinal Muscle Segmentation Based on Deep Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2650 ◽  
Author(s):  
Haixing Li ◽  
Haibo Luo ◽  
Yunpeng Liu
Keyword(s):  
Hausdorff Distance ◽  
Automatic Segmentation ◽  
Automatic Measurement ◽  
Automated Analysis ◽  
Erector Spinae ◽  
Paraspinal Muscle ◽  
Spinal Diseases ◽  
Dice Coefficient ◽  
Feature Maps ◽  
Paravertebral Muscles

The accurate segmentation of the paraspinal muscle in Magnetic Resonance (MR) images is a critical step in the automated analysis of lumbar diseases such as chronic low back pain, disc herniation and lumbar spinal stenosis. However, the automatic segmentation of multifidus and erector spinae has not yet been achieved due to three unusual challenges: (1) the muscle boundary is unclear; (2) the gray histogram distribution of the target overlaps with the background; (3) the intra- and inter-patient shape is variable. We propose to tackle the problem of the automatic segmentation of paravertebral muscles using a deformed U-net consisting of two main modules: the residual module and the feature pyramid attention (FPA) module. The residual module can directly return the gradient while preserving the details of the image to make the model easier to train. The FPA module fuses different scales of context information and provides useful salient features for high-level feature maps. In this paper, 120 cases were used for experiments, which were provided and labeled by the spine surgery department of Shengjing Hospital of China Medical University. The experimental results show that the model can achieve higher predictive capability. The dice coefficient of the multifidus is as high as 0.949, and the Hausdorff distance is 4.62 mm. The dice coefficient of the erector spinae is 0.913 and the Hausdorff distance is 7.89 mm. The work of this paper will contribute to the development of an automatic measurement system for paraspinal muscles, which is of great significance for the treatment of spinal diseases.

Automatic Segmentation of Calcifications in Intravascular Ultrasound Images Using Snakes and the Contourlet Transform

2010 ◽  
Vol 36 (1) ◽  
pp. 111-129 ◽  
Author(s):  
Qi Zhang ◽  
Yuanyuan Wang ◽  
Weiqi Wang ◽  
Jianying Ma ◽  
Juying Qian ◽  
...  
Keyword(s):  
Intravascular Ultrasound ◽  
Automatic Segmentation ◽  
Contourlet Transform ◽  
Ultrasound Images

Automatic segmentation of intravascular ultrasound images: A texture-based approach

1997 ◽  
Vol 25 (6) ◽  
pp. 1059-1071 ◽  
Author(s):  
Aleksandra Mojsilović ◽  
Miodrag Popović ◽  
Nenad Amodaj ◽  
Rade Babić ◽  
Miodrag Ostojić
Keyword(s):  
Intravascular Ultrasound ◽  
Automatic Segmentation ◽  
Ultrasound Images

scholarly journals Joint optic disc and cup segmentation based on multi-scale feature analysis and attention pyramid architecture for glaucoma screening

2021 ◽  
Author(s):  
Guangmin Sun ◽  
Zhongxiang Zhang ◽  
Junjie Zhang ◽  
Meilong Zhu ◽  
Xiao-rong Zhu ◽  
...  
Keyword(s):  
Optic Disc ◽  
Hausdorff Distance ◽  
Automatic Segmentation ◽  
Fundus Images ◽  
Dice Coefficient ◽  
Scale Feature ◽  
Multi Scale ◽  
Glaucoma Screening ◽  
Feature Extractor ◽  
Public Datasets

AbstractAutomatic segmentation of optic disc (OD) and optic cup (OC) is an essential task for analysing colour fundus images. In clinical practice, accurate OD and OC segmentation assist ophthalmologists in diagnosing glaucoma. In this paper, we propose a unified convolutional neural network, named ResFPN-Net, which learns the boundary feature and the inner relation between OD and OC for automatic segmentation. The proposed ResFPN-Net is mainly composed of multi-scale feature extractor, multi-scale segmentation transition and attention pyramid architecture. The multi-scale feature extractor achieved the feature encoding of fundus images and captured the boundary representations. The multi-scale segmentation transition is employed to retain the features of different scales. Moreover, an attention pyramid architecture is proposed to learn rich representations and the mutual connection in the OD and OC. To verify the effectiveness of the proposed method, we conducted extensive experiments on two public datasets. On the Drishti-GS database, we achieved a Dice coefficient of 97.59%, 89.87%, the accuracy of 99.21%, 98.77%, and the Averaged Hausdorff distance of 0.099, 0.882 on the OD and OC segmentation, respectively. We achieved a Dice coefficient of 96.41%, 83.91%, the accuracy of 99.30%, 99.24%, and the Averaged Hausdorff distance of 0.166, 1.210 on the RIM-ONE database for OD and OC segmentation, respectively. Comprehensive results show that the proposed method outperforms other competitive OD and OC segmentation methods and appears more adaptable in cross-dataset scenarios. The introduced multi-scale loss function achieved significantly lower training loss and higher accuracy compared with other loss functions. Furthermore, the proposed method is further validated in OC to OD ratio calculation task and achieved the best MAE of 0.0499 and 0.0630 on the Drishti-GS and RIM-ONE datasets, respectively. Finally, we evaluated the effectiveness of the glaucoma screening on Drishti-GS and RIM-ONE datasets, achieving the AUC of 0.8947 and 0.7964. These results proved that the proposed ResFPN-Net is effective in analysing fundus images for glaucoma screening and can be applied in other relative biomedical image segmentation applications.

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

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 (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.

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

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.

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

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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