BCS-Net: Boundary, Context and Semantic for Automatic COVID-19 Lung Infection Segmentation from CT Images

The spread of COVID-19 has brought a huge disaster to the world, and the automatic segmentation of infection regions can help doctors to make diagnosis quickly and reduce workload. However, there are several challenges for the accurate and complete segmentation, such as the scattered infection area distribution, complex background noises, and blurred segmentation boundaries. To this end, in this paper, we propose a novel network for automatic COVID-19 lung infection segmentation from CT images, named BCS-Net, which considers the boundary, context, and semantic attributes. The BCS-Net follows an encoder-decoder architecture, and more designs focus on the decoder stage that includes three progressively Boundary- Context-Semantic Reconstruction (BCSR) blocks. In each BCSR block, the attention-guided global context (AGGC) module is designed to learn the most valuable encoder features for decoder by highlighting the important spatial and boundary locations and modeling the global context dependence. Besides, a semantic guidance (SG) unit generates the semantic guidance map to refine the decoder features by aggregating multi-scale high-level features at the intermediate resolution. Extensive experiments demonstrate that our proposed framework outperforms the existing competitors both qualitatively and quantitatively. IEEE

Review of human lung and lung lesion regions segmentation methods based on CT images

Lung disease like corona virus disease 2019(COVID-19) and lung cancer endanger the health of human beings. Early screening and treatment can significantly decrease the mortality of lung diseases. Computed tomography (CT) technology can be an effective information collection method for the diagnosis and treatment of lung diseases. CT-based lung lesion region image segmentation is a key step in lung disease screening. High quality lung lesion region segmentation can effectively improve the level of early stage diagnosis and treatment of lung diseases. However, high-quality lung lesion region segmentation in lung CT images has become a challenging issue in computer-aided diagnosis due to the diversity and complexity of lung diseases. Our research reviews the relevant literature recently. First, it is compared and summarized the pros and cons of traditional segmentation methods of lung CT image based on region and active contour. The region-based method uses the similarity and difference of features to guide image segmentation, mainly including threshold method, region growth method, clustering method and random walk method. The active-contour-based method is to set an initial contour line with decreasing energy. The contour line deforms in the internal energy derived from its own characteristics and the external energy originated from image characteristics. Its movement is in accordance with the principle of minimum energy until the energy function is in minimization and the contour line stops next to the boundary of lung region. The active contour method is divided into parametric active contour method and geometric active contour method in terms of the contour curve analysis. Low segmentation accuracy lung CT image segmentation methods are widely used in the early stage diagnosis. Next, the improved model analysis of lung CT image segmentation network structure is based on convolutional neural networks (CNNs), fully convolutional networks (FCNs), and generative adversarial network (GAN). In respect of the CNN-based deep learning segmentation methods, the segmentation methods of lung and lung lesion region can be divided into two-dimensional and three-dimensional methods in terms of the dimension of convolution kernel, the segmentation methods of lung and lung lesion region can also be divided into two-dimensional and three-dimensional methods based on the dimension of convolution kernel for the FCN-based deep learning segmentation methods. In respect of the U-Net based lung CT image segmentation methods, it can be divided into solo network lung CT image segmentation method and multi network lung CT image segmentation method according to the form of U-Net architecture. Due to the CT image containing COVID-19 infection area is very different from the ordinary lung CT imageand the differentiated segmentation characteristics of the two in the same network, the solo network lung CT image segmentation method can be analyzed that whether the data-set contains COVID-19 or not. The multi-network lung CT image segmentation method can be divided into cascade U-Net and dual path U-Net based on the option of serial mode or parallel mode. For the GAN-based lung CT image segmentation methods, it can be divided into GAN models based on network architecture, generator and other methods according to the ways to improve the different architectures of GAN. Deep-learning-based segmentation method has the advantages of high segmentation accuracy, strong transfer learning ability and high robustness. In particular, the auxiliary diagnosis of COVID-19 cases analysis is significantly qualified based on deep learning. Next, the common datasets and evaluation indexes of lung and lung lesion region segmentation are illustrated, including almost 10 lung CT open datasets, such as national lung screening test(NLST) dataset, computer vision and image analysis international early lung cancer action plan database(VIA/I-ELCAP) dataset, lung image database consortium and image database resource initiative(LIDC-IDRI) dataset and Nederlands-Leuvens Long anker Screenings Onderzoek(NELSON) dataset, and 7 COVID-19 lung CT datasets analysis. It also demonstrates that the related lung CT images datasets is provided based on five large-scale competitions, including TIANCHI dataset, lung nodule analysis 16(LUNA16) dataset, Lung Nodule Database(LNDb) dataset, Kaggle Data Science Bowl 2017(Kaggle DSB) 2017 dataset and Automatic Nodule Detection 2009(ANODE09) dataset, respectively. Our 8 evaluation index is commonly used to evaluate the quality of lung CT image segmentation model, including involved Dice similarity coefficient, Jaccard similarity coefficient, accuracy, precision, false positive rate, false negative rate, sensitivity and specificity, respectively. To increase the number and diversity of training samples, GAN is used to synthesize high-quality adversarial images to expand the dataset. At the end, the prospects, challenges and potentials of CT-based high-precision segmentation strategies are critical reviewed for lung and lung lesion regions. Because the special structure of U-Net can effectively extract target features and restore the information loss derived from down sampling, it does not need a large number of samples for training to achieve high segmentation effect. Therefore, it is necessary to segment lung and lung lesions based on U-Net. The integration of GAN and U-Net is to improve the segmentation accuracy of lung and lung lesion areas. GAN-based network architecture is to extend the dataset for good training quality. The further U-Net application has its priority for qualified segmentation consistently. © 2022, Editorial Office of Journal of Image and Graphics. All right reserved.

DR-MIL: deep represented multiple instance learning distinguishes COVID-19 from community-acquired pneumonia in CT images.

BACKGROUND AND OBJECTIVE: Given that the novel coronavirus disease 2019 (COVID-19) has become a pandemic, a method to accurately distinguish COVID-19 from community-acquired pneumonia (CAP) is urgently needed. However, the spatial uncertainty and morphological diversity of COVID-19 lesions in the lungs, and subtle differences with respect to CAP, make differential diagnosis non-trivial. METHODS: We propose a deep represented multiple instance learning (DR-MIL) method to fulfill this task. A 3D volumetric CT scan of one patient is treated as one bag and ten CT slices are selected as the initial instances. For each instance, deep features are extracted from the pre-trained ResNet-50 with fine-tuning and represented as one deep represented instance score (DRIS). Each bag with a DRIS for each initial instance is then input into a citation k-nearest neighbor search to generate the final prediction. A total of 141 COVID-19 and 100 CAP CT scans were used. The performance of DR-MIL is compared with other potential strategies and state-of-the-art models. RESULTS: DR-MIL displayed an accuracy of 95% and an area under curve of 0.943, which were superior to those observed for comparable methods. COVID-19 and CAP exhibited significant differences in both the DRIS and the spatial pattern of lesions (p<0.001). As a means of content-based image retrieval, DR-MIL can identify images used as key instances, references, and citers for visual interpretation. CONCLUSIONS: DR-MIL can effectively represent the deep characteristics of COVID-19 lesions in CT images and accurately distinguish COVID-19 from CAP in a weakly supervised manner. The resulting DRIS is a useful supplement to visual interpretation of the spatial pattern of lesions when screening for COVID-19.

COVSeg-NET: A deep convolution neural network for COVID-19 lung CT image segmentation.

COVID-19 is a new type of respiratory infectious disease that poses a serious threat to the survival of human beings all over the world. Using artificial intelligence technology to analyze lung images of COVID-19 patients can achieve rapid and effective detection. This study proposes a COVSeg-NET model that can accurately segment ground glass opaque lesions in COVID-19 lung CT images. The COVSeg-NET model is based on the fully convolutional neural network model structure, which mainly includes convolutional layer, nonlinear unit activation function, maximum pooling layer, batch normalization layer, merge layer, flattening layer, sigmoid layer, and so forth. Through experiments and evaluation results, it can be seen that the dice coefficient, sensitivity, and specificity of the COVSeg-NET model are 0.561, 0.447, and 0.996 respectively, which are more advanced than other deep learning methods. The COVSeg-NET model can use a smaller training set and shorter test time to obtain better segmentation results.