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Lin Q, Gao R, Luo M, Wang H, Cao Y, Man Z, Wang R. Semi-supervised segmentation of metastasis lesions in bone scan images. Front Mol Biosci 2022; 9:956720. [DOI: 10.3389/fmolb.2022.956720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 10/13/2022] [Indexed: 11/13/2022] Open
Abstract
To develop a deep image segmentation model that automatically identifies and delineates lesions of skeletal metastasis in bone scan images, facilitating clinical diagnosis of lung cancer–caused bone metastasis by nuclear medicine physicians. A semi-supervised segmentation model is proposed, comprising the feature extraction subtask and pixel classification subtask. During the feature extraction stage, cascaded layers which include the dilated residual convolution, inception connection, and feature aggregation learn the hierarchal representations of low-resolution bone scan images. During the pixel classification stage, each pixel is first classified into categories in a semi-supervised manner, and the boundary of pixels belonging to an individual lesion is then delineated using a closed curve. Experimental evaluation conducted on 2,280 augmented samples (112 original images) demonstrates that the proposed model performs well for automated segmentation of metastasis lesions, with a score of 0.692 for DSC if the model is trained using 37% of the labeled samples. The self-defined semi-supervised segmentation model can be utilized as an automated clinical tool to detect and delineate metastasis lesions in bone scan images, using only a few manually labeled image samples. Nuclear medicine physicians need only attend to those segmented lesions while ignoring the background when they diagnose bone metastasis using low-resolution images. More images of patients from multiple centers are typically needed to further improve the scalability and performance of the model via mitigating the impacts of variability in size, shape, and intensity of bone metastasis lesions.
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Guo Y, Lin Q, Zhao S, Li T, Cao Y, Man Z, Zeng X. Automated detection of lung cancer-caused metastasis by classifying scintigraphic images using convolutional neural network with residual connection and hybrid attention mechanism. Insights Imaging 2022; 13:24. [PMID: 35138479 PMCID: PMC8828823 DOI: 10.1186/s13244-022-01162-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 01/20/2022] [Indexed: 12/03/2022] Open
Abstract
Background Whole-body bone scan is the widely used tool for surveying bone metastases caused by various primary solid tumors including lung cancer. Scintigraphic images are characterized by low specificity, bringing a significant challenge to manual analysis of images by nuclear medicine physicians. Convolutional neural network can be used to develop automated classification of images by automatically extracting hierarchal features and classifying high-level features into classes. Results Using convolutional neural network, a multi-class classification model has been developed to detect skeletal metastasis caused by lung cancer using clinical whole-body scintigraphic images. The proposed method consisted of image aggregation, hierarchal feature extraction, and high-level feature classification. Experimental evaluations on a set of clinical scintigraphic images have shown that the proposed multi-class classification network is workable for automated detection of lung cancer-caused metastasis, with achieving average scores of 0.7782, 0.7799, 0.7823, 0.7764, and 0.8364 for accuracy, precision, recall, F-1 score, and AUC value, respectively. Conclusions The proposed multi-class classification model can not only predict whether an image contains lung cancer-caused metastasis, but also differentiate between subclasses of lung cancer (i.e., adenocarcinoma and non-adenocarcinoma). On the context of two-class (i.e., the metastatic and non-metastatic) classification, the proposed model obtained a higher score of 0.8310 for accuracy metric.
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Affiliation(s)
- Yanru Guo
- School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, Gansu, China.,Key Laboratory of Streaming Data Computing Technologies and Application, Northwest Minzu University, Lanzhou, Gansu, China
| | - Qiang Lin
- School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, Gansu, China. .,Key Laboratory of Streaming Data Computing Technologies and Application, Northwest Minzu University, Lanzhou, Gansu, China. .,Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, Gansu, China.
| | - Shaofang Zhao
- School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, Gansu, China.,Key Laboratory of Streaming Data Computing Technologies and Application, Northwest Minzu University, Lanzhou, Gansu, China
| | - Tongtong Li
- School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, Gansu, China.,Key Laboratory of Streaming Data Computing Technologies and Application, Northwest Minzu University, Lanzhou, Gansu, China
| | - Yongchun Cao
- School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, Gansu, China.,Key Laboratory of Streaming Data Computing Technologies and Application, Northwest Minzu University, Lanzhou, Gansu, China.,Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, Gansu, China
| | - Zhengxing Man
- School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, Gansu, China.,Key Laboratory of Streaming Data Computing Technologies and Application, Northwest Minzu University, Lanzhou, Gansu, China.,Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, Gansu, China
| | - Xianwu Zeng
- Department of Nuclear Medicine, Gansu Provincial Tumor Hospital, Lanzhou, Gansu, China.
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Li T, Lin Q, Guo Y, Zhao S, Zeng X, Man Z, Cao Y, Hu Y. Automated detection of skeletal metastasis of lung cancer with bone scans using convolutional nuclear network. Phys Med Biol 2021; 67. [PMID: 34933282 DOI: 10.1088/1361-6560/ac4565] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 12/21/2021] [Indexed: 11/12/2022]
Abstract
Bone scan is widely used for surveying bone metastases caused by various solid tumors. Scintigraphic images are characterized by inferior spatial resolution, bringing a significant challenge to manual analysis of images by nuclear medicine physicians. We present in this work a new framework for automatically classifying scintigraphic images collected from patients clinically diagnosed with lung cancer. The framework consists of data preparation and image classification. In the data preparation stage, data augmentation is used to enlarge the dataset, followed by image fusion and thoracic region extraction. In the image classification stage, we use a self-defined convolutional neural network consisting of feature extraction, feature aggregation, and feature classification sub-networks. The developed multi-class classification network can not only predict whether a bone scan image contains bone metastasis but also tell which subcategory of lung cancer that a bone metastasis metastasized from is present in the image. Experimental evaluations on a set of clinical bone scan images have shown that the proposed multi-class classification network is workable for automated classification of metastatic images, with achieving average scores of 0.7392, 0.7592, 0.7242, and 0.7292 for accuracy, precision, recall, and F-1 score, respectively.
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Affiliation(s)
- Tongtong Li
- Northwest Minzu University, No. 1, Xibei Xincun Rd., Lanzhou, Gansu, 730030, CHINA
| | - Qiang Lin
- School of Mathematics and Computer Science, Northwest Minzu University, No. 1, Xibei Xincun Rd., Lanzhou, 730030, CHINA
| | - Yanru Guo
- Northwest Minzu University, No. 1, Xibei Xincun Rd., Lanzhou, Gansu, 730030, CHINA
| | - Shaofang Zhao
- Northwest Minzu University, No. 1, Xibei Xincun Rd., Lanzhou, Gansu, 730030, CHINA
| | - Xianwu Zeng
- Gansu Provincial Cancer Hospital, No. 2, Dongjie Rd., Lanzhou, Gansu, 730050, CHINA
| | - Zhengxing Man
- Northwest Minzu University, No. 1, Xibei Xincun Rd., Lanzhou, Gansu, 730030, CHINA
| | - Yongchun Cao
- Northwest Minzu University, No. 1, Xibei Xincun Rd., Lanzhou, Gansu, 730030, CHINA
| | - Yonghua Hu
- Gansu University of Chinese Medicine, No. 35, Dingxi Donglu Rd., Lanzhou, 730000, CHINA
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