1. A dense connection encoding–decoding convolutional neural network structure for semantic segmentation of thymoma
- Author
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Yi Zhang, Jingyuan Li, Guangbin Cui, Wenfang Sun, Gang Xing, Karen M. von Deneen, Xiu-Long Feng, and Wen Wang
- Subjects
0209 industrial biotechnology ,Thymoma ,Intersection (set theory) ,business.industry ,Computer science ,Cognitive Neuroscience ,Pattern recognition ,02 engineering and technology ,medicine.disease ,Convolutional neural network ,Computer Science Applications ,020901 industrial engineering & automation ,Artificial Intelligence ,Feature (computer vision) ,Robustness (computer science) ,hemic and lymphatic diseases ,Encoding (memory) ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Preprocessor ,020201 artificial intelligence & image processing ,Segmentation ,Artificial intelligence ,business - Abstract
Accurately positioning and segmenting thymoma from computed tomography (CT) images is of great importance for an image-driven thymoma analysis. In clinical practice, the diagnosis and segmentation of thymomas for radiologists are time-consuming and inefficient tasks. Thus, it is necessary to develop a method to accurately and efficiently realize automatic segmentation of thymoma. Here, a dense skip connection encoding–decoding model (DSC-Net), which is a deep convolutional neural network, was proposed to perform automatic segmentation of thymoma with the ability to fuse feature maps under receptive fields of different scales. An image preprocessing method was also proposed to provide much more texture information and enhance the contrast between thymoma and its surrounding tissues. A total of 310 subjects who underwent contrast-enhanced CT scanning were included in this ethically-approved retrospective study. All of the CT slices were manually labeled by four experienced radiologists, and 80% of images were included in the training set and the rest were included in the testing set. The performance of segmentation was evaluated by calculating the accuracy, intersection over union (IoU), and Boundary F1 contour matching score (BFScore) between the predicted segmentation and the manual labels. For segmentation of thymoma in the testing set, the accuracy, IoU and BFScore were 92.96%, 87.86% and 0.9087 respectively. Compared to the U-Net method, the DSC-Net model improved IoU by 3.94%. In addition, the efficacy and robustness of DSC-Net in segmentation of different patients and different types of thymoma classified by the WHO histological classification criteria were verified. The proposed preprocessing method and DSC-Net demonstrated improved performance in segmentation of thymomas, suggesting the ability to provide consistent delineation and assist radiologists in their clinical applications.
- Published
- 2021
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