Lung Infection Segmentation for COVID-19 Pneumonia Based on a Cascade Convolutional Network from CT Images

Abstract

The COVID-19 pandemic is a global, national, and local public health concern which has caused a significant outbreak in all countries and regions for both males and females around the world. Automated detection of lung infections and their boundaries from medical images offers a great potential to augment the patient treatment healthcare strategies for tackling COVID-19 and its impacts. Detecting this disease from lung CT scan images is perhaps one of the fastest ways to diagnose patients. However, finding the presence of infected tissues and segment them from CT slices faces numerous challenges, including similar adjacent tissues, vague boundary, and erratic infections. To eliminate these obstacles, we propose a two-route convolutional neural network (CNN) by extracting global and local features for detecting and classifying COVID-19 infection from CT images. Each pixel from the image is classified into the normal and infected tissues. For improving the classification accuracy, we used two different strategies including fuzzy c -means clustering and local directional pattern (LDN) encoding methods to represent the input image differently. This allows us to find more complex pattern from the image. To overcome the overfitting problems due to small samples, an augmentation approach is utilized. The results demonstrated that the proposed framework achieved precision 96%, recall 97%, F score, average surface distance (ASD) of 2.8 ± 0.3 mm, and volume overlap error (VOE) of 5.6 ± 1.2 % .

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“…In today’s artificial intelligence (AI) applications, the convolutional neural network (ConvNet/CNN) pipelines that are a class of deep feed-forward artificial neural networks exhibit a tremendous breakthrough in medical image analysis and processing 28 – 32 . The structure of a CNN model was inspired by the biological organization of the visual cortex in the human brain which uses the local receptive field.…”

“…The major drawback of convolutional neural network models (CNN) lies in the fuzzy segmentation outcomes and the spatial information reduction caused by the strides of convolutions and pooling operations 32 . To further improve the segmentation accuracy and efficiency, several advanced strategies have been applied to obtain better segmentation results 21 , 25 , 33 , 34 with approaches like dilated convolution/pooling 35 – 37 , skip connections 38 , 39 , as well as additional analysis and new post-processing modules like Conditional Random Field (CRF) and Hidden Conditional Random Field (HCRF) 10 , 40 , 41 .…”

“…Due to the use of a powerful preprocessing step that eliminates about 80% of the insignificant information of each input image, there is no need for a complex deep network such as 10 , 22 , 32 . In other words, by selecting approximately 20% of the whole image (this percentage is the mean of the whole slices of a patient) for each input modality and corresponding Z-Score normalized image, there fewer pixels to investigate.…”

“…The core building block of the proposed CNN structure is expressed as the convolutional layer. This layer can calculate the dot-product between input data with arbitrary size and a set of learnable filters (masks), much like a traditional neural network 32 , 48 , 49 .

Figure 12 Our implemented cascade structure.

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“…In contrast to the convolution operation, the pooling layer which is regularly incorporated between two sequential convolutional layers has no parameters and summarizes the key information without losing any details in the sliding window (mask). Additionally, as the dimension of the feature maps (in both column and row) is decreased in this layer, the training time will be smaller and mitigates overfitting 32 , 49 . By using the max-pooling method in this paper, the feature map is divided into a set of regions with no overlapping, then takes the maximum number inside each area.…”