Research on Calculation of Lung Compression Degree in Pneumothorax Using Semantic Segmentation Based on Deep Learning
Calculation of the degree of lung compression by Mimics software remains the"gold standard".In the forensic sphere,due to the complexity of the Mimics software,many people do not utilize this method in forensic practice.They may calculate degree of lung compression by visual observation,represent the result of degree of lung compression by some slicer of CT.These factors will lead to inaccuracies of calculated results.The aim of this study is to develop a model for automatic calculation of lung compression degree based on deep learning semantic segmentation technology,and explore the feasibility of deep learning for lung compression measurement by comparing the results of automatic calculations with Mimics software.In this study,42 cases of the computed tomography(CT)data including pneumothorax diagnosis in DICOM format were collected each cases has about 350 images with a thickness of 1 mm.Among them,32 cases used for training and 10 cases used for validation.The air-containing regions of 1943 images were manually annotated.An additional five chest CT cases were selected for external testing.The degree of lung compression was calculated by both the deep learning model and Mimics software,and the correlation between the results of the two methods and the calculation errors were analyzed.In the validation set,the average error between the deep learning model calculation results and the manual method was 2.4%,and the model processed an average of 356 per case with an average time of 60.04 s,while the average error in the test set was 4.4%.The aforementioned results lead to the following conclusions:The deep learning model constructed in this study has the potential to be applied in the automated measurement of the lung compression degree due to pneumothorax,which can provide a reference for the calculation of the lung compression degree due to pneumothorax in forensic practice.
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