At the time of writing,coronavirus disease 2019(COVID-19)is seriously threatening human lives and health throughout the world.Many epidemic models have been developed to provide references for decision-making by gover...At the time of writing,coronavirus disease 2019(COVID-19)is seriously threatening human lives and health throughout the world.Many epidemic models have been developed to provide references for decision-making by governments and the World Health Organization.To capture and understand the characteristics of the epidemic trend,parameter optimization algorithms are needed to obtain model parameters.In this study,the authors propose using the Levenberg–Marquardt algorithm(LMA)to identify epidemic models.This algorithm combines the advantage of the Gauss–Newton method and gradient descent method and has improved the stability of parameters.The authors selected four countries with relatively high numbers of confirmed cases to verify the advantages of the Levenberg–Marquardt algorithm over the traditional epidemiological model method.The results show that the Statistical-SIR(Statistical-Susceptible–Infected–Recovered)model using LMA can fit the actual curve of the epidemic well,while the epidemic simulation of the traditional model evolves too fast and the peak value is too high to reflect the real situation.展开更多
Objective To assess inter-observer variations of pulmonary nodule marking in routine clinical chest digital radiograph (DR) softcopy reading by using a lung nodule computer toolkit.Methods A total of 601 chest posteri...Objective To assess inter-observer variations of pulmonary nodule marking in routine clinical chest digital radiograph (DR) softcopy reading by using a lung nodule computer toolkit.Methods A total of 601 chest posterior-anterior DR images were randomly selected from routine outpatient screening in Peking Union Medical College Hospital. Two chest radiologists with experience more than ten years were first asked to read the images and mark all suspicious nodules independently by using computer toolkit IQQA-Chest, and to indicate the likelihood for each nodule detected. They were also asked to draw the boundary of the identified nodule manually on an enlarged region of interest, which was instantly analyzed by IQQA-Chest. Two sets of diagnostic reports, including the marked nodules, likelihood, manually drawn boundaries, quantitative measurements, and radiologists’ names, were automatically generated and stored by the computer system. One week later, the two radiologists read the same images together by using the same computer toolkit without referring to their previous reading results. Marking procedure was the same except that consensus was reached for each suspicious region. Statistical analysis tools provided in the IQQA-Chest were used to compare all the three sets of reading results.Results In the independent readings, Reader 1 detected 409 nodules with a mean diameter of 12.4 mm in 241 patients, and Reader 2 detected 401 nodules with a mean diameter of 12.6 mm in 253 patients. In the consensus reading, a total of 352 nodules with a mean diameter of 12.4 mm were detected in 220 patients. Totally, 42.3% of Reader 1’s and 45.1% of Reader 2’s marks were confirmed by the consensus reading. About 40% of each reader’s marks agreed with the other. There were only 130 (14.4%) out of the total 904 unique nodules were confirmed by both readers and the consensus reading. Moreover, 5.6% (51/904) of the marked regions were rated identical likelihood in all three readings. Statistical analysis showed significant differences between Readers 1 and 2, and between consensus and Reader 2 in determining the likelihood of the marks (P<0.01), but not between consensus and Reader 1. No significant difference in terms of size was observed in nodule segmentation between either two of the three readings. Conclusion Large variations in nodule marking and nodule-likelihood determination but not in nodule size were observed between experts as well as between single-person reading and consensus reading.展开更多
In this paper, we proposed a semi-automatic technique with a marker indicating the target to locate and segment nodules. For the lung nodule detection, we develop a Gabor texture feature by FCM （Fuzzy C Means） segme...In this paper, we proposed a semi-automatic technique with a marker indicating the target to locate and segment nodules. For the lung nodule detection, we develop a Gabor texture feature by FCM （Fuzzy C Means） segmentation. Given a marker indicating a rough location of the nodules, a decision process is followed by applying an ellipse fitting algorithm. From the ellipse mask, the foreground and background seeds for the random walk segmentation can be automatically obtained. Finally, the edge of the nodules is obtained by the random walk algorithm. The feasibility and effectiveness of the proposed method are evaluated with the various types of the nodules to identify the edges, so that it can be used to locate the nodule edge and its growth rate.展开更多
基金This work was jointly supported by the National Natural Science Foundation of China[grant number 41521004]the Gansu Provincial Special Fund Project for Guiding Scientific and Technological Innovation and Development[grant number 2019ZX-06].
文摘At the time of writing,coronavirus disease 2019(COVID-19)is seriously threatening human lives and health throughout the world.Many epidemic models have been developed to provide references for decision-making by governments and the World Health Organization.To capture and understand the characteristics of the epidemic trend,parameter optimization algorithms are needed to obtain model parameters.In this study,the authors propose using the Levenberg–Marquardt algorithm(LMA)to identify epidemic models.This algorithm combines the advantage of the Gauss–Newton method and gradient descent method and has improved the stability of parameters.The authors selected four countries with relatively high numbers of confirmed cases to verify the advantages of the Levenberg–Marquardt algorithm over the traditional epidemiological model method.The results show that the Statistical-SIR(Statistical-Susceptible–Infected–Recovered)model using LMA can fit the actual curve of the epidemic well,while the epidemic simulation of the traditional model evolves too fast and the peak value is too high to reflect the real situation.
文摘Objective To assess inter-observer variations of pulmonary nodule marking in routine clinical chest digital radiograph (DR) softcopy reading by using a lung nodule computer toolkit.Methods A total of 601 chest posterior-anterior DR images were randomly selected from routine outpatient screening in Peking Union Medical College Hospital. Two chest radiologists with experience more than ten years were first asked to read the images and mark all suspicious nodules independently by using computer toolkit IQQA-Chest, and to indicate the likelihood for each nodule detected. They were also asked to draw the boundary of the identified nodule manually on an enlarged region of interest, which was instantly analyzed by IQQA-Chest. Two sets of diagnostic reports, including the marked nodules, likelihood, manually drawn boundaries, quantitative measurements, and radiologists’ names, were automatically generated and stored by the computer system. One week later, the two radiologists read the same images together by using the same computer toolkit without referring to their previous reading results. Marking procedure was the same except that consensus was reached for each suspicious region. Statistical analysis tools provided in the IQQA-Chest were used to compare all the three sets of reading results.Results In the independent readings, Reader 1 detected 409 nodules with a mean diameter of 12.4 mm in 241 patients, and Reader 2 detected 401 nodules with a mean diameter of 12.6 mm in 253 patients. In the consensus reading, a total of 352 nodules with a mean diameter of 12.4 mm were detected in 220 patients. Totally, 42.3% of Reader 1’s and 45.1% of Reader 2’s marks were confirmed by the consensus reading. About 40% of each reader’s marks agreed with the other. There were only 130 (14.4%) out of the total 904 unique nodules were confirmed by both readers and the consensus reading. Moreover, 5.6% (51/904) of the marked regions were rated identical likelihood in all three readings. Statistical analysis showed significant differences between Readers 1 and 2, and between consensus and Reader 2 in determining the likelihood of the marks (P<0.01), but not between consensus and Reader 1. No significant difference in terms of size was observed in nodule segmentation between either two of the three readings. Conclusion Large variations in nodule marking and nodule-likelihood determination but not in nodule size were observed between experts as well as between single-person reading and consensus reading.
基金Acknowledgments This work was supported by the National Natural Science Foundation of China （Project Nos. 81000639 and 31000450）, China Postdoctoral Science Foundation （Project Nos. 20100470791 and 201104307）, and Program of the Pearl River Young Talents of Science and Technology in Guangzhou （No. 2012J2200041）.
文摘In this paper, we proposed a semi-automatic technique with a marker indicating the target to locate and segment nodules. For the lung nodule detection, we develop a Gabor texture feature by FCM （Fuzzy C Means） segmentation. Given a marker indicating a rough location of the nodules, a decision process is followed by applying an ellipse fitting algorithm. From the ellipse mask, the foreground and background seeds for the random walk segmentation can be automatically obtained. Finally, the edge of the nodules is obtained by the random walk algorithm. The feasibility and effectiveness of the proposed method are evaluated with the various types of the nodules to identify the edges, so that it can be used to locate the nodule edge and its growth rate.
