scholarly journals Automatic Segmentation of Novel Coronavirus Pneumonia Lesions in CT Images Utilizing Deep-Supervised Ensemble Learning Network

2022 ◽  
Vol 8 ◽  
Author(s):  
Yuanyuan Peng ◽  
Zixu Zhang ◽  
Hongbin Tu ◽  
Xiong Li
Keyword(s):  
Ensemble Learning ◽  
Learning Strategy ◽  
Treatment Plan ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Living Environment ◽  
Lesion Segmentation ◽  
Global Features ◽  
Learning Network ◽  
Novel Coronavirus

Background: The novel coronavirus disease 2019 (COVID-19) has been spread widely in the world, causing a huge threat to the living environment of people.Objective: Under CT imaging, the structure features of COVID-19 lesions are complicated and varied greatly in different cases. To accurately locate COVID-19 lesions and assist doctors to make the best diagnosis and treatment plan, a deep-supervised ensemble learning network is presented for COVID-19 lesion segmentation in CT images.Methods: Since a large number of COVID-19 CT images and the corresponding lesion annotations are difficult to obtain, a transfer learning strategy is employed to make up for the shortcoming and alleviate the overfitting problem. Based on the reality that traditional single deep learning framework is difficult to extract complicated and varied COVID-19 lesion features effectively that may cause some lesions to be undetected. To overcome the problem, a deep-supervised ensemble learning network is presented to combine with local and global features for COVID-19 lesion segmentation.Results: The performance of the proposed method was validated in experiments with a publicly available dataset. Compared with manual annotations, the proposed method acquired a high intersection over union (IoU) of 0.7279 and a low Hausdorff distance (H) of 92.4604.Conclusion: A deep-supervised ensemble learning network was presented for coronavirus pneumonia lesion segmentation in CT images. The effectiveness of the proposed method was verified by visual inspection and quantitative evaluation. Experimental results indicated that the proposed method has a good performance in COVID-19 lesion segmentation.

scholarly journals Auto-diagnosis of COVID-19 using Lung CT Images with Semi-supervised Shallow Learning Network

2021 ◽  
Author(s):  
Debanjan Konar ◽  
Bijaya Ketan Panigrahi ◽  
Siddhartha Bhattacharyya ◽  
Nilanjan Dey ◽  
Richard Jiang
Keyword(s):  
Automatic Segmentation ◽  
Feature Learning ◽  
Image Data ◽  
Network Models ◽  
Ct Images ◽  
Neural Network Models ◽  
Learning Network ◽  
Novel Coronavirus ◽  
Neighborhood Topology ◽  
Lung Ct

Abstract Infection of Novel Coronavirus 2019 (COVID-19) on lung cells and human respiratory systems have raised real concern to the human lives during the current pandemic spread across the world. Recent observations on CT images of human lungs infected by COVID-19 is a challenging task for the researchers in finding suitable image patterns for automatic diagnosis. In this paper, a novel semi-supervised shallow learning network model comprising Parallel Quantum-Inspired Self-supervised Network (PQIS-Net) with Fully Connected (FC) layers is proposed for automatic segmentation followed by patch-based classifications on segmented lung CT images for the diagnosis of COVID-19 disease. The PQIS-Net model is incorporated for fully automated segmentation of lung CT scan images obviating pre-trained convolutional neural network models for feature learning. The PQIS-Net model comprises a trinity of layered structures of quantum bits inter-connected through rotation gates using an 8-connected second-order neighborhood topology for the segmentation of wide variation of local intensities of the CT images. Intensive experiments have been carried out on two publicly available lung CT image data sets thereby achieving promising segmentation outcome and diagnosis efficiency (F1-score and AUC) while compared with the state of the art pre-trained convolutional based models.

scholarly journals Auto-diagnosis of COVID-19 using Lung CT Images with Semi-supervised Shallow Learning Network

2020 ◽  
Author(s):  
Debanjan Konar ◽  
Bijaya Ketan Panigrahi ◽  
Siddhartha Bhattacharyya ◽  
Nilanjan Dey
Keyword(s):  
Automatic Segmentation ◽  
Feature Learning ◽  
Image Data ◽  
Network Models ◽  
Ct Images ◽  
Neural Network Models ◽  
Learning Network ◽  
Novel Coronavirus ◽  
Neighborhood Topology ◽  
Lung Ct

Abstract Infection of Novel Coronavirus 2019 (COVID-19) on lung cells and human respiratory systems have raised real concern to the human lives during the current pandemic spread across the world. Recent observations on CT images of human lungs infected by COVID-19 is a challenging task for the researchers in finding suitable image patterns for automatic diagnosis. In this paper, a novel semi-supervised shallow learning network model comprising Parallel Quantum-Inspired Self-supervised Network (PQIS-Net) with Fully Connected (FC) layers is proposed for automatic segmentation followed by patch-based classifications on segmented lung CT images for the diagnosis of COVID-19 disease. The PQIS-Net model is incorporated for fully automated segmentation of lung CT scan images obviating pre-trained convolutional neural network models for feature learning. The PQIS-Net model comprises a trinity of layered structures of quantum bits inter-connected through rotation gates using an 8-connected second-order neighborhood topology for the segmentation of wide variation of local intensities of the CT images. Intensive experiments have been carried out on two publicly available lung CT image data sets thereby achieving promising segmentation outcome and diagnosis efficiency (F1-score and AUC) while compared with the state of the art pre-trained convolutional based models.

Evaluation on Auto-segmentation of the Clinical Target Volume (CTV) for Graves' Ophthalmopathy (GO) with a Fully Convolutional Network (FCN) on CT Images

2020 ◽  
Vol 16 ◽  
Author(s):  
Jialiang Jiang ◽  
Yong Luo ◽  
Feng Wang ◽  
Yuchuan Fu ◽  
Hang Yu ◽  
...  
Keyword(s):  
Network Architecture ◽  
Treatment Plan ◽  
Automatic Segmentation ◽  
Evaluation Criteria ◽  
Target Volume ◽  
Ct Images ◽  
Dice Similarity Coefficient ◽  
Target Area ◽  
Convolutional Network ◽  
Significant Difference

: Purpose: To evaluate the accuracy and dosimetric effects for auto-segmentation of the CTV for GO in CT images based on FCN. Methods: An FCN-8s network architecture for auto-segmentation was built based on Caffe. CT images of 121 patients with GO who have received radiotherapy at the West China Hospital of Sichuan University were randomly selected for training and testing. Two methods were used to segment the CTV of GO: treating the two-part CTV as a whole anatomical region or considering the two parts of CTV as two independent regions. Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD) were used as evaluation criteria. The auto-segmented contours were imported into the original treatment plan to analysis the dosimetric characteristics. Results: The similarity comparison between manual contours and auto-segmental contours showed an average DSC value up to 0.83. The max HD values for segmenting two parts of CTV separately was a little bit smaller than treating CTV with one label (8.23±2.80 vs. 9.03±2.78). The dosimetric comparison between manual contours and auto-segmental contours showed there was a significant difference (p<0.05) with the lack of dose for auto-segmental CTV. Conclusion: Based on deep learning architecture, the automatic segmentation model for small target area can carry out auto contouring task well. Treating separate parts of one target as different anatomic regions can help to improve the auto-contouring quality. The dosimetric evaluation can provide us with different perspectives for further exploration of automatic sketching tools.

An Ensemble Learning Framework For Multi-Class Covid-19 Lesion Segmentation From Chest Ct Images

2021 ◽  
Author(s):  
Nastaran Enshaei ◽  
Parnian Afshar ◽  
Shahin Heidarian ◽  
Arash Mohammadi ◽  
Moezedin Javad Rafiee ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Ct Images ◽  
Chest Ct ◽  
Lesion Segmentation ◽  
Learning Framework

scholarly journals Anterior Mediastinal Lesion Segmentation Based on Two-Stage 3D ResUNet With Attention Gates and Lung Segmentation

2021 ◽  
Vol 10 ◽  
Author(s):  
Su Huang ◽  
Xiaowei Han ◽  
Jingfan Fan ◽  
Jing Chen ◽  
Lei Du ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Ct Images ◽  
Common Disease ◽  
Original Image ◽  
Lesion Segmentation ◽  
Two Stage ◽  
Image Artifact ◽  
Lung Segmentation ◽  
Mediastinal Disease ◽  
Segmentation Accuracy

ObjectivesAnterior mediastinal disease is a common disease in the chest. Computed tomography (CT), as an important imaging technology, is widely used in the diagnosis of mediastinal diseases. Doctors find it difficult to distinguish lesions in CT images because of image artifact, intensity inhomogeneity, and their similarity with other tissues. Direct segmentation of lesions can provide doctors a method to better subtract the features of the lesions, thereby improving the accuracy of diagnosis.MethodAs the trend of image processing technology, deep learning is more accurate in image segmentation than traditional methods. We employ a two-stage 3D ResUNet network combined with lung segmentation to segment CT images. Given that the mediastinum is between the two lungs, the original image is clipped through the lung mask to remove some noises that may affect the segmentation of the lesion. To capture the feature of the lesions, we design a two-stage network structure. In the first stage, the features of the lesion are learned from the low-resolution downsampled image, and the segmentation results under a rough scale are obtained. The results are concatenated with the original image and encoded into the second stage to capture more accurate segmentation information from the image. In addition, attention gates are introduced in the upsampling of the network, and these gates can focus on the lesion and play a role in filtering the features. The proposed method has achieved good results in the segmentation of the anterior mediastinal.ResultsThe proposed method was verified on 230 patients, and the anterior mediastinal lesions were well segmented. The average Dice coefficient reached 87.73%. Compared with the model without lung segmentation, the model with lung segmentation greatly improved the accuracy of lesion segmentation by approximately 9%. The addition of attention gates slightly improved the segmentation accuracy.ConclusionThe proposed automatic segmentation method has achieved good results in clinical data. In clinical application, automatic segmentation of lesions can assist doctors in the diagnosis of diseases and may facilitate the automated diagnosis of illnesses in the future.

scholarly journals A deep learning method for automatic segmentation of the bony orbit in MRI and CT images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jared Hamwood ◽  
Beat Schmutz ◽  
Michael J. Collins ◽  
Mark C. Allenby ◽  
David Alonso-Caneiro
Keyword(s):  
Deep Learning ◽  
Human Performance ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Human Observer ◽  
In Series ◽  
Magnetic Resonance Imaging Mri ◽  
High Degree ◽  
Orbital Region ◽  
Ct And Mri

AbstractThis paper proposes a fully automatic method to segment the inner boundary of the bony orbit in two different image modalities: magnetic resonance imaging (MRI) and computed tomography (CT). The method, based on a deep learning architecture, uses two fully convolutional neural networks in series followed by a graph-search method to generate a boundary for the orbit. When compared to human performance for segmentation of both CT and MRI data, the proposed method achieves high Dice coefficients on both orbit and background, with scores of 0.813 and 0.975 in CT images and 0.930 and 0.995 in MRI images, showing a high degree of agreement with a manual segmentation by a human expert. Given the volumetric characteristics of these imaging modalities and the complexity and time-consuming nature of the segmentation of the orbital region in the human skull, it is often impractical to manually segment these images. Thus, the proposed method provides a valid clinical and research tool that performs similarly to the human observer.

Automatic Segmentation of COVID-19 CT Images using improved MultiResUNet

2020 ◽  
Author(s):  
Qi Yang ◽  
Yunke Li ◽  
Mengyi Zhang ◽  
Tian Wang ◽  
Fei Yan ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Ct Images
Sign in / Sign up

Export Citation Format

Share Document