Robust chest CT image segmentation of COVID-19 lung infection based on limited data

Müller, Dominik; Soto-Rey, Iñaki; Krämer, Frank

doi:10.1016/j.imu.2021.100681

Cited by 77 publications

(35 citation statements)

References 51 publications

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“…This approach applied to segmentation was in accordance with Unet architecture, with the study proposing a RTSU-Net that improved on structural relationships by introducing a nonlocal neural network module [ 23 ]. The study coped with a COVID-19 CT scans data shortage by focusing on increasing data size using the augmentation method along with the Unet model implementation for segmentation [ 24 ].…”

Section: Related Workmentioning

confidence: 99%

Automatic COVID-19 Lung Infection Segmentation through Modified Unet Model

Shamim

Awan

Zain

et al. 2022

Journal of Healthcare Engineering

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The coronavirus (COVID-19) pandemic has had a terrible impact on human lives globally, with far-reaching consequences for the health and well-being of many people around the world. Statistically, 305.9 million people worldwide tested positive for COVID-19, and 5.48 million people died due to COVID-19 up to 10 January 2022. CT scans can be used as an alternative to time-consuming RT-PCR testing for COVID-19. This research work proposes a segmentation approach to identifying ground glass opacity or ROI in CT images developed by coronavirus, with a modified structure of the Unet model having been used to classify the region of interest at the pixel level. The problem with segmentation is that the GGO often appears indistinguishable from a healthy lung in the initial stages of COVID-19, and so, to cope with this, the increased set of weights in contracting and expanding the Unet path and an improved convolutional module is added in order to establish the connection between the encoder and decoder pipeline. This has a major capacity to segment the GGO in the case of COVID-19, with the proposed model being referred to as “convUnet.” The experiment was performed on the Medseg1 dataset, and the addition of a set of weights at each layer of the model and modification in the connected module in Unet led to an improvement in overall segmentation results. The quantitative results obtained using accuracy, recall, precision, dice-coefficient, F1score, and IOU were 93.29%, 93.01%, 93.67%, 92.46%, 93.34%, 86.96%, respectively, which is better than that obtained using Unet and other state-of-the-art models. Therefore, this segmentation approach proved to be more accurate, fast, and reliable in helping doctors to diagnose COVID-19 quickly and efficiently.

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Section: Related Workmentioning

confidence: 99%

Automatic COVID-19 Lung Infection Segmentation through Modified Unet Model

Shamim

Awan

Zain

et al. 2022

Journal of Healthcare Engineering

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“…They concluded that diversity in the training dataset is more important than the DL algorithms. Müller et al 53 implemented a 3D U‐Net using data augmentation for generating image patches during training for lung and lesion segmentation on 20 annotated CT volumes. They achieved Dice coefficients of 0.950 and 0.761 for lung and lesions, respectively.…”

Section: Discussionmentioning

confidence: 99%

COLI‐Net: Deep learning‐assisted fully automated COVID‐19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images

Shiri

Arabi

Salimi

et al. 2021

Int J Imaging Syst Tech

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We present a deep learning (DL)‐based automated whole lung and COVID‐19 pneumonia infectious lesions (COLI‐Net) detection and segmentation from chest computed tomography (CT) images. This multicenter/multiscanner study involved 2368 (347′259 2D slices) and 190 (17 341 2D slices) volumetric CT exams along with their corresponding manual segmentation of lungs and lesions, respectively. All images were cropped, resized, and the intensity values clipped and normalized. A residual network 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 reverse transcription‐polymerase chain reaction 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. The 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 to 0.18) and −0.18 ± 3.4% (95% CI, −0.8 to 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 to 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. We developed an automated DL‐guided three‐dimensional whole lung and infected regions segmentation in COVID‐19 patients to provide fast, consistent, robust, and human error immune framework for lung and pneumonia lesion detection and quantification.

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“…The lack of public annotated training datasets is the main challenge for COVID-19 deep learning segmentation systems, especially with the hungry deep FCNs that use 3D patches for training [28] [30]. Works that are based on limited datasets could suffer from over-fitting, as number of training datasets is few that lead to biased systems lack the ability of generalization.…”

Section: F Performance Measuresmentioning

confidence: 99%

“…[28].Ma et al proposed U-net based deep learning system, which is trained using 20 CT scans, 10 cases from the Coronacases Initiative and 10 cases from Radiopedia. They used 20% of the dataset (4 cases) for training and 80% of the dataset (the remaining 16 cases) are used for testing[13].…”

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confidence: 99%

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Automatic deep learning system for COVID-19 infection quantification in chest CT

Alirr

2021

Multimed Tools Appl

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The paper proposes an automatic deep learning system for COVID-19 infection areas segmentation in chest CT scans. CT imaging proved its ability to detect the COVID-19 disease even for asymptotic patients, which make it a trustworthy alternative for PCR. Coronavirus disease spread globally and PCR screening is the adopted diagnostic testing method for COVID-19 detection. However, PCR is criticized due its low sensitivity ratios, also, it is time-consuming and manual complicated process. The proposed framework includes different steps; it starts to prepare the region of interest by segmenting the lung organ, which then undergoes edge enhancing diffusion filtering (EED) to improve the infection areas contrast and intensity homogeneity. The proposed FCN is implemented using U-net architecture with modified residual block to include concatenation skip connection. The block improves the learning of gradient values by forwarding the infection area features through the network. The proposed system is evaluated using different measures and achieved dice overlapping score of 0.961 and 0.780 for lung and infection areas segmentation, respectively. The proposed system is trained and tested using many 2D CT slices extracted from diverse datasets from different sources, which demonstrate the system generalization and effectiveness. The use of more datasets from different sources helps to enhance the system accuracy and generalization, which can be accomplished based on the data availability in in the future.

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Robust chest CT image segmentation of COVID-19 lung infection based on limited data

Cited by 77 publications

References 51 publications

Automatic COVID-19 Lung Infection Segmentation through Modified Unet Model

Automatic COVID-19 Lung Infection Segmentation through Modified Unet Model

COLI‐Net: Deep learning‐assisted fully automated COVID‐19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images

Automatic deep learning system for COVID-19 infection quantification in chest CT

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