期刊文献+

基于卷积神经网络的胸片肺结节检测 被引量:8

Lung nodules detection via convolutional neural networks in chest radiographs
下载PDF
导出
摘要 针对目前胸片的肺结节检测方案的检出率较低,且存在大量的假阳性的问题,提出了一种新的基于卷积神经网络(CNN)的肺结节检测方案。增强肺结节区域的图像信号;选择正、负样本训练卷积神经网络模型,检测结节时用滑动窗口的方法对增强后的图片进行处理得到候选区域;根据候选区域的面积排除假阳性。方案中省略了传统方法中的肺区分割步骤,避免了因此可能丢失的肺结节图像。在日本放射技术学会(JSRT)数据库上测试结果显示,系统在平均每幅图5.0个假阳性水平下敏感度为86%,对不明显和非常不明显的结节检出率达到了84%,优于当前相关文献报道的方法。 Aiming at the problem that detection rate of lung nodules detection scheme based on rabat is low and has a lot of false positives,propose a new nodules detection scheme based on convolutional neural network( CNN).In the scheme,enhance chest radiograph,and then pick positive and negative samples to train the CNN. Process the enhanced image using sliding windows method with the pre-trained network to get the region of interest( ROI),and exclude the false positives by using the size of the ROI at last. The proposed scheme omits the procedure of segmentation of lung field in traditional schemes. And this can avoid losing nodules which are excluded by the segmentation procedure. The JSRT database is used to evaluate the system. The scheme achieves a sensitivity of86 % for all nodules and a detection rate of 84 % with 5. 0 FPs per radiograph for very subtle and extremely subtle nodules which outperform the current reported methods.
出处 《传感器与微系统》 CSCD 2017年第12期153-156,共4页 Transducer and Microsystem Technologies
基金 国家自然科学基金资助项目(61170120)
关键词 肺结节 医学图像处理 胸片 卷积神经网络 lung nodules medical image processing chest radiographs (CXRs) convolutional neural network (CNN)
  • 相关文献

参考文献2

二级参考文献16

  • 1Tarver T. Cancer Facts & Figures 2012. American Cancer Society (ACS). J Consum Health Internet, 2012,16(3):366-367.
  • 2Austin JH, Romney BM, Goldsmith LS. Missed bronchogenic carcinoma: Radiographic finding in 27 patients with a potentially respectable lesion evident in etrospect. Radiology, 1992,182(1):115-122.
  • 3Shiraishi J, Katsuragawa S, Ikezoe J, et al. Development of a digital image database for chest radiographs with and without a lung nodule: Receiver operating characteristic analysis of radiologist\'s detection of pulmonary nodules. AJR Am J Roentgenol,2000,174(1):71-74.
  • 4Zhang X, Chen L, Pan L, et al. Study on the image segmentation based on ICA and watershed algorithm. Intelligent Computation Technology and Automation. : ICTA, Fifth International Conference on, 2012:505-508.
  • 5Wei J, Hagihara Y, Shimizu A, et al. Optimal image feature set for detecting lung nodules on chest X-ray images. Cars Computer Assisted Radiology & Surgery, 2002:706-711.
  • 6Hardie RC, Rogers SK, Wilson T, et al. Performance analysis of a new computer aided detection system for identifying lung nodules on chest radiographs. Med Image Anal, 2008,12:240-258.
  • 7Loog M, van Ginneken B, Schilham AM. Filter learning: Application to suppression of bony structures from chest radiographs. Med Image Anal, 2006,10(6):826-840.
  • 8Matsumoto T, Yoshimura H, Doi K, et al. Image feature analysis of false-positive diagnoses produced by automated detection of lung nodules. Invest Radiol, 1992,27(8):587-597.
  • 9Keserci B, Yoshida H. Computerized detection of pulmonary nodules in chest radiographs based on morphological features and wavelet snake model. Med Image Anal, 2002,6(4):431-447.
  • 10Yoshida H. Local contralateral subtraction based on bilateral symmetry of lung for reduction of false positives in computerized detection of pulmonary nodules. IEEE Trans Biomed Eng, 2004,51(5):778-789.

共引文献12

同被引文献43

引证文献8

二级引证文献37

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部