scholarly journals Semantic Cardiac Segmentation in Chest CT Images Using K-Means Clustering and the Mathematical Morphology Method

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2675
Author(s):  
Beanbonyka Rim ◽  
Sungjin Lee ◽  
Ahyoung Lee ◽  
Hyo-Wook Gil ◽  
Min Hong
Keyword(s):  
Mathematical Morphology ◽  
Ground Truth ◽  
Ct Images ◽  
Chest Ct ◽  
Cardiac Segmentation ◽  
Threshold Criterion ◽  
Unsupervised Approach ◽  
The Mean ◽  
Artery Disease ◽  
Whole Heart

Whole cardiac segmentation in chest CT images is important to identify functional abnormalities that occur in cardiovascular diseases, such as coronary artery disease (CAD) detection. However, manual efforts are time-consuming and labor intensive. Additionally, labeling the ground truth for cardiac segmentation requires the extensive manual annotation of images by the radiologist. Due to the difficulty in obtaining the annotated data and the required expertise as an annotator, an unsupervised approach is proposed. In this paper, we introduce a semantic whole-heart segmentation combining K-Means clustering as a threshold criterion of the mean-thresholding method and mathematical morphology method as a threshold shifting enhancer. The experiment was conducted on 500 subjects in two cases: (1) 56 slices per volume containing full heart scans, and (2) 30 slices per volume containing about half of the top of heart scans before the liver appears. In both cases, the results showed an average silhouette score of the K-Means method of 0.4130. Additionally, the experiment on 56 slices per volume achieved an overall accuracy (OA) and mean intersection over union (mIoU) of 34.90% and 41.26%, respectively, while the performance for the first 30 slices per volume achieved an OA and mIoU of 55.10% and 71.46%, respectively.

scholarly journals COLI-NET: Fully Automated COVID-19 Lung and Infection Pneumonia Lesion Detection and Segmentation from Chest CT Images

2021 ◽  
Author(s):  
Isaac Shiri ◽  
Hossein Arabi ◽  
Yazdan Salimi ◽  
Amir Hossein Sanaat ◽  
Azadeh Akhavanalaf ◽  
...  
Keyword(s):  
Deep Learning ◽  
Relative Error ◽  
Human Error ◽  
Hounsfield Unit ◽  
Ct Images ◽  
Relative Volume ◽  
Chest Ct ◽  
Lesion Detection ◽  
Mean Relative Error ◽  
The Mean

AbstractBackgroundWe present a deep learning (DL)-based automated whole lung and COVID-19 pneumonia infectious lesions (COLI-Net) detection and segmentation from chest CT images.MethodsWe prepared 2358 (347’259, 2D slices) and 180 (17341, 2D slices) volumetric CT images along with their corresponding manual segmentation of lungs and lesions, respectively, in the framework of a multi-center/multi-scanner study. All images were cropped, resized and the intensity values clipped and normalized. A residual network (ResNet) with non-square Dice loss function built upon TensorFlow was employed. The accuracy of lung and COVID-19 lesions segmentation was evaluated on an external RT-PCR positive COVID-19 dataset (7’333, 2D slices) collected at five different centers. To evaluate the segmentation performance, we calculated different quantitative metrics, including radiomic features.ResultsThe mean Dice coefficients were 0.98±0.011 (95% CI, 0.98-0.99) and 0.91±0.038 (95% CI, 0.90-0.91) for lung and lesions segmentation, respectively. The mean relative Hounsfield unit differences were 0.03±0.84% (95% CI, −0.12 – 0.18) and −0.18±3.4% (95% CI, −0.8 - 0.44) for the lung and lesions, respectively. The relative volume difference for lung and lesions were 0.38±1.2% (95% CI, 0.16-0.59) and 0.81±6.6% (95% CI, −0.39-2), respectively. Most radiomic features had a mean relative error less than 5% with the highest mean relative error achieved for the lung for the Range first-order feature (- 6.95%) and least axis length shape feature (8.68%) for lesions.ConclusionWe set out to develop an automated deep learning-guided three-dimensional whole lung and infected regions segmentation in COVID-19 patients in order to develop fast, consistent, robust and human error immune framework for lung and pneumonia lesion detection and quantification.

scholarly journals Neural Network based Whole Heart Segmentation from 3D CT images

2020 ◽  
Vol 11 (1) ◽  
pp. 25-31
Author(s):  
Marija Habijan ◽  
Hrvoje Leventić ◽  
Irena Galić ◽  
Danilo Babin
Keyword(s):  
Neural Network ◽  
Data Augmentation ◽  
Principal Component ◽  
Ground Truth ◽  
Ct Images ◽  
Training Data ◽  
Optimal Learning ◽  
Learning Rates ◽  
Augmentation Techniques ◽  
Whole Heart

The most recent research is showing the importance and suitability of neural networks for medical image processing tasks. Nonetheless, their efficiency in segmentation tasks is greatly dependent on the amount of available training data. To overcome issues of using small datasets, various data augmentation techniques have been developed. In this paper, an approach for the whole heart segmentation based on the convolutional neural network, specifically on the 3D U-Net architecture, is presented. Also, we propose the incorporation of the principal component analysis as an additional data augmentation technique. The network is trained end-to-end, i.e., no pre-trained network is required. Evaluation of the proposed approach is performed on CT images from MICCAI 2017 Multi-Modality Whole Heart Segmentation Challenge dataset, delivering in a three-fold cross-validation an average dice coefficient overlap of 88.2% for the whole heart, i.e. all heart substructures. Final segmentation results show a high accuracy with the ground truth, indicating that the proposed approach is competitive to the state-of-the-art. Additionally, experiments on the influence of different learning rates are provided as well, showing the optimal learning rate of 0.005 to give the best segmentation results.

scholarly journals An Approach for Pulmonary Vascular Extraction from Chest CT Images

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Wenjun Tan ◽  
Yue Yuan ◽  
Anning Chen ◽  
Lin Mao ◽  
Yuqian Ke ◽  
...  
Keyword(s):  
Region Growing ◽  
Ground Truth ◽  
Ct Images ◽  
Chest Ct ◽  
Morphological Operations ◽  
Lung Region ◽  
Fast Marching Method ◽  
Fast Marching ◽  
Average Accuracy ◽  
Seed Points

Pulmonary vascular extraction from chest CT images plays an important role in the diagnosis of lung disease. To improve the accuracy rate of pulmonary vascular segmentation, a new pulmonary vascular extraction approach is proposed in this study. First, the lung tissue is extracted from chest CT images by region-growing and maximum between-class variance methods. Then the holes of the extracted region are filled by morphological operations to obtain complete lung region. Second, the points of the pulmonary vascular of the middle slice of the chest CT images are extracted as the original seed points. Finally, the seed points are spread throughout the lung region based on the fast marching method to extract the pulmonary vascular in the gradient image. Results of pulmonary vascular extraction from chest CT image datasets provided by the introduced approach are presented and discussed. Based on the ground truth pixels and the resulting quality measures, it can be concluded that the average accuracy of this approach is about 90%. Extensive experiments demonstrate that the proposed method has achieved the best performance in pulmonary vascular extraction compared with other two widely used methods.

An Unsupervised Approach for COVID-19 Detection using Chest CT Images

2021 ◽  
Author(s):  
Prashanth S ◽  
K Devika ◽  
V Ramana Murthy Oruganti
Keyword(s):  
Ct Images ◽  
Chest Ct ◽  
Unsupervised Approach

COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image (Preprint)

2020 ◽  
Author(s):  
Jinseok Lee
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Ct Images ◽  
Chest Ct ◽  
University Hospital ◽  
Detection Accuracy ◽  
Ct Image ◽  
Test Dataset ◽  
Learning Framework ◽  
Testing Dataset

BACKGROUND The coronavirus disease (COVID-19) has explosively spread worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) can be used as a relevant screening tool owing to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely busy fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE We aimed to quickly develop an AI technique to diagnose COVID-19 pneumonia and differentiate it from non-COVID pneumonia and non-pneumonia diseases on CT. METHODS A simple 2D deep learning framework, named fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning, using one of the four state-of-art pre-trained deep learning models (VGG16, ResNet50, InceptionV3, or Xception) as a backbone. For training and testing of FCONet, we collected 3,993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and non-pneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training and a testing set at a ratio of 8:2. For the test dataset, the diagnostic performance to diagnose COVID-19 pneumonia was compared among the four pre-trained FCONet models. In addition, we tested the FCONet models on an additional external testing dataset extracted from the embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS Of the four pre-trained models of FCONet, the ResNet50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100%, and accuracy 99.87%) and outperformed the other three pre-trained models in testing dataset. In additional external test dataset using low-quality CT images, the detection accuracy of the ResNet50 model was the highest (96.97%), followed by Xception, InceptionV3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS The FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing dataset, the ResNet50-based FCONet might be the best model, as it outperformed other FCONet models based on VGG16, Xception, and InceptionV3.

scholarly journals Geometric property-based convolutional neural network for indoor object detection

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142199332
Author(s):  
Xintao Ding ◽  
Boquan Li ◽  
Jinbao Wang
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Geometric Property ◽  
Ground Truth ◽  
Geometric Constraints ◽  
Depth Information ◽  
Training Set ◽  
Object Knowledge ◽  
The Mean

Indoor object detection is a very demanding and important task for robot applications. Object knowledge, such as two-dimensional (2D) shape and depth information, may be helpful for detection. In this article, we focus on region-based convolutional neural network (CNN) detector and propose a geometric property-based Faster R-CNN method (GP-Faster) for indoor object detection. GP-Faster incorporates geometric property in Faster R-CNN to improve the detection performance. In detail, we first use mesh grids that are the intersections of direct and inverse proportion functions to generate appropriate anchors for indoor objects. After the anchors are regressed to the regions of interest produced by a region proposal network (RPN-RoIs), we then use 2D geometric constraints to refine the RPN-RoIs, in which the 2D constraint of every classification is a convex hull region enclosing the width and height coordinates of the ground-truth boxes on the training set. Comparison experiments are implemented on two indoor datasets SUN2012 and NYUv2. Since the depth information is available in NYUv2, we involve depth constraints in GP-Faster and propose 3D geometric property-based Faster R-CNN (DGP-Faster) on NYUv2. The experimental results show that both GP-Faster and DGP-Faster increase the performance of the mean average precision.

scholarly journals Impact of comprehensive management on clinical outcomes in hypertensive patients with coronary artery disease: HIJ-CREATE sub-study

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
H Ogawa ◽  
H Sekiguchi ◽  
K Jujo ◽  
E Kawada-Watanabe ◽  
H Arashi ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Primary Outcome ◽  
Ldl Cholesterol ◽  
Lipid Lowering ◽  
Hypertensive Patients ◽  
Mean Values ◽  
Comprehensive Management ◽  
The Mean ◽  
Artery Disease

Abstract Background There are limited data on the effects of blood pressure (BP) control and lipid lowering in secondary prevention of coronary artery disease (CAD) patients. We report a secondary analysis of the effects of BP control and lipid management in participants of the HIJ-CREATE, a prospective randomized trial. Methods HIJ-CREATE was a multicenter, prospective, randomized, controlled trial that compared the effects of candesartan-based therapy with those of non-ARB-based standard therapy on major adverse cardiac events (MACE; a composite of cardiovascular death, non-fatal myocardial infarction, unstable angina, heart failure, stroke, and other cardiovascular events requiring hospitalization) in 2,049 hypertensive patients with angiographically documented CAD. In both groups, titration of antihypertensive agents was performed to reach the target BP of <130/85 mmHg. The primary endpoint was the time to first MACE. Incidence of endpoint events in addition to biochemistry tests and office BP was determined during the scheduled 6, 12, 24, 36, 48, and 60-month visits. Achieved systolic BP and LDL-Cholesterol (LDL-C) level were defined as the mean values of these measurements in patients who did not develop MACEs and as the mean values of them prior to MACEs in those who developed MACEs during follow-up. Results During a median follow-up of 4.2 years (follow-up rate of 99.6%), the primary outcome occurred in 304 patients (30.3%). Among HIJ-CREATE participants, 905 (44.2%) were prescribed statins on enrollment. Kaplan–Meier curves for the primary outcome revealed that there was no relationship between statin therapy and MACEs in hypertensive patients with CAD. The original HIJ-CREATE population was divided into 9 groups based on equal tertiles based on mean achieved BP and LDL-C during follow-up. For the analysis of subgroups, estimates of relative risk and the associated 95% CIs were generated with a Cox proportional-hazards model (Figure 1). The relation between LDL cholesterol level and hazard ratios for MACEs was nonlinear, with a significant increase of MACEs only in the patients with inadequate controlled LDL-C level even in the patients with tightly controlled BP. Conclusions The results of the post-hoc analysis of the HIJ-CREATE suggest that clinicians should pay careful attention to conduct comprehensive management of lipid lowering even in the contemporary BP lowering for the secondary prevention in hypertensive patients with CAD. Figure 1 Funding Acknowledgement Type of funding source: None

scholarly journals Auto Whole Heart Segmentation from CT images Using an Improved Unet-GAN

2021 ◽  
Vol 1769 (1) ◽  
pp. 012016
Author(s):  
Kening Le ◽  
Zeyu Lou ◽  
Weiliang Huo ◽  
Xiaolin Tian
Keyword(s):  
Ct Images ◽  
Whole Heart
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