scholarly journals Improving the generalization of deep learning methods to segment the left ventricle in short axis MR images

2020 ◽  
Author(s):  
Catharine V. Graves ◽  
Ramon A. Moreno ◽  
Marina S. Rebelo ◽  
Cesar H. Nomura ◽  
Marco A. Gutierrez
Keyword(s):  
Deep Learning ◽  
Left Ventricle ◽  
Short Axis ◽  
Mr Images ◽  
Learning Methods

scholarly journals A deep learning-based approach for automatic segmentation and quantification of the left ventricle from cardiac cine MR images

2020 ◽  
Vol 81 ◽  
pp. 101717 ◽  
Author(s):  
Hisham Abdeltawab ◽  
Fahmi Khalifa ◽  
Fatma Taher ◽  
Norah Saleh Alghamdi ◽  
Mohammed Ghazal ◽  
...  
Keyword(s):  
Deep Learning ◽  
Left Ventricle ◽  
Automatic Segmentation ◽  
Mr Images ◽  
Cine Mr ◽  
Cardiac Cine

Cardiac through-plane motion: comparison of long- and short-axis MR images of the left ventricle

1995 ◽  
Vol 5 (1) ◽  
pp. 60-65 ◽  
Author(s):  
N. A. A. Matheijssen ◽  
E. E. van der Wall ◽  
B. M. Pluim ◽  
J. Doornbos ◽  
A. de Roos
Keyword(s):  
Left Ventricle ◽  
Plane Motion ◽  
Short Axis ◽  
Mr Images

scholarly journals Edge-Sensitive Left Ventricle Segmentation Using Deep Reinforcement Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2375
Author(s):  
Jingjing Xiong ◽  
Lai-Man Po ◽  
Kwok Wai Cheung ◽  
Pengfei Xian ◽  
Yuzhi Zhao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Left Ventricle ◽  
Autonomous Driving ◽  
Learning Methods ◽  
Proposed Model ◽  
Markov Decision ◽  
Edge Points ◽  
Cardiac Diagnosis ◽  
Ventricle Segmentation

Deep reinforcement learning (DRL) has been utilized in numerous computer vision tasks, such as object detection, autonomous driving, etc. However, relatively few DRL methods have been proposed in the area of image segmentation, particularly in left ventricle segmentation. Reinforcement learning-based methods in earlier works often rely on learning proper thresholds to perform segmentation, and the segmentation results are inaccurate due to the sensitivity of the threshold. To tackle this problem, a novel DRL agent is designed to imitate the human process to perform LV segmentation. For this purpose, we formulate the segmentation problem as a Markov decision process and innovatively optimize it through DRL. The proposed DRL agent consists of two neural networks, i.e., First-P-Net and Next-P-Net. The First-P-Net locates the initial edge point, and the Next-P-Net locates the remaining edge points successively and ultimately obtains a closed segmentation result. The experimental results show that the proposed model has outperformed the previous reinforcement learning methods and achieved comparable performances compared with deep learning baselines on two widely used LV endocardium segmentation datasets, namely Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset, and Sunnybrook 2009 dataset. Moreover, the proposed model achieves higher F-measure accuracy compared with deep learning methods when training with a very limited number of samples.

scholarly journals Deep Learning–based Method for Fully Automatic Quantification of Left Ventricle Function from Cine MR Images: A Multivendor, Multicenter Study

Radiology ◽  
2019 ◽  
Vol 290 (1) ◽  
pp. 81-88 ◽  
Author(s):  
Qian Tao ◽  
Wenjun Yan ◽  
Yuanyuan Wang ◽  
Elisabeth H. M. Paiman ◽  
Denis P. Shamonin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Left Ventricle ◽  
Multicenter Study ◽  
Mr Images ◽  
Left Ventricle Function ◽  
Automatic Quantification ◽  
Cine Mr ◽  
Fully Automatic ◽  
Ventricle Function

scholarly journals DL-MRI: A Unified Framework of Deep Learning-Based MRI Super Resolution

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Huanyu Liu ◽  
Jiaqi Liu ◽  
Junbao Li ◽  
Jeng-Shyang Pan ◽  
Xiaqiong Yu
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Super Resolution ◽  
Motion Artifacts ◽  
Mr Images ◽  
Unified Framework ◽  
Resolution Method ◽  
Learning Methods ◽  
Scanning Time ◽  
Detection And Diagnosis

Magnetic resonance imaging (MRI) is widely used in the detection and diagnosis of diseases. High-resolution MR images will help doctors to locate lesions and diagnose diseases. However, the acquisition of high-resolution MR images requires high magnetic field intensity and long scanning time, which will bring discomfort to patients and easily introduce motion artifacts, resulting in image quality degradation. Therefore, the resolution of hardware imaging has reached its limit. Based on this situation, a unified framework based on deep learning super resolution is proposed to transfer state-of-the-art deep learning methods of natural images to MRI super resolution. Compared with the traditional image super-resolution method, the deep learning super-resolution method has stronger feature extraction and characterization ability, can learn prior knowledge from a large number of sample data, and has a more stable and excellent image reconstruction effect. We propose a unified framework of deep learning -based MRI super resolution, which has five current deep learning methods with the best super-resolution effect. In addition, a high-low resolution MR image dataset with the scales of ×2, ×3, and ×4 was constructed, covering 4 parts of the skull, knee, breast, and head and neck. Experimental results show that the proposed unified framework of deep learning super resolution has a better reconstruction effect on the data than traditional methods and provides a standard dataset and experimental benchmark for the application of deep learning super resolution in MR images.

scholarly journals Detection and Classification of Knee Injuries from MR Images Using the MRNet Dataset with Progressively Operating Deep Learning Methods

2021 ◽  
Vol 3 (4) ◽  
pp. 1009-1029
Author(s):  
Ali Can Kara ◽  
Fırat Hardalaç
Keyword(s):  
Deep Learning ◽  
Cruciate Ligament ◽  
Knee Injuries ◽  
Mr Images ◽  
Learning Methods ◽  
Learning Group ◽  
Magnetic Resonance Imaging Mri ◽  
Anterior Cruciate ◽  
Novel Model

This study aimed to build progressively operating deep learning models that could detect meniscus injuries, anterior cruciate ligament (ACL) tears and knee abnormalities in magnetic resonance imaging (MRI). The Stanford Machine Learning Group MRNet dataset was employed in the study, which included MRI image indexes in the coronal, sagittal, and axial axes, each having 1130 trains and 120 validation items. The study is divided into three sections. In the first section, suitable images are selected to determine the disease in the image index based on the disturbance under examination. It is also used to identify images that have been misclassified or are noisy and/or damaged to the degree that they cannot be utilised for diagnosis in the first section. The study employed the 50-layer residual networks (ResNet50) model in this section. The second part of the study involves locating the region to be focused on based on the disturbance that is targeted to be diagnosed in the image under examination. A novel model was built by integrating the convolutional neural networks (CNN) and the denoising autoencoder models in the second section. The third section is dedicated to making a diagnosis of the disease. In this section, a novel ResNet50 model is trained to identify disease diagnoses or abnormalities, independent of the ResNet50 model used in the first section. The images that each model selects as output after training are referred to as progressively operating deep learning methods since they are supplied as an input to the following model.

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