Research on pulmonary nodule recognition algorithm based on micro-variation amplification_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

ZHANG Zirui ¹ , JIAO Zichen ² , SHI Xiaoming ¹ ,  WANG Tao ^1,2

1. Department of Thoracic Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, P. R. China;
2. Department of Thoracic Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, 210008, P. R. China;

Corresponding author：

WANG Tao, Email: wangtao_pumc@live.cn

Keywords：

Pulmonary nodules; preoperative localization; micro-variation amplification; computer vision; multiscale detection

DOI：

10.7507/1007-4848.202409021

Video：

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Objective To develop an innovative recognition algorithm that aids physicians in the identification of pulmonary nodules. Methods Sixteen patients with pulmonary nodules who underwent thoracoscopic surgery at the Department of Thoracic Surgery, Affiliated Drum Tower Hospital of Nanjing University Medical School in December 2023, were enrolled in the study. The cohort included 9 males and 7 females, with an average age of (54.9±14.9) years. Chest surface exploration data were collected at a rate of 60 frames per second and a resolution of 1 920×1 080. Frame images were saved at regular intervals for subsequent block processing. An algorithm database for lung nodule recognition was developed using the collected data. Results In the optimized multi-topology convolutional network model, the test results demonstrated an accuracy rate of 94.39% for recognition tasks. Furthermore, the integration of micro-variation amplification technology into the convolutional network model enhanced the accuracy of lung nodule identification to 96.90%. A comprehensive evaluation of the performance of these two models yielded an overall recognition accuracy of 95.59%. Based on these findings, we conclude that the proposed network model is well-suited for the task of lung nodule recognition, with the convolutional network incorporating micro-variation amplification technology exhibiting superior accuracy. Conclusion Compared to traditional methods, our proposed technique significantly enhances the accuracy of lung nodule identification and localization, aiding surgeons in locating lung nodules during thoracoscopic surgery.

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11.	方新, 陈山, 臧林. 肺磨玻璃结节CT三维重建精确定位在胸腔镜肺段切除中的应用. 中华肺部疾病杂志(电子版), 2021, 14(6): 764-766.Fang X, Chen S, Zang L. Application of CT three dimensional reconstruction and accurate positioning of pulmonary ground glass nodules in thoracoscopic segmental pneumonectomy. Chin J Lung Dis (Electronic Edition), 2021, 14(6): 764-766.
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1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2024, 74(3): 229-263.
2. Sihoe ADL. Video-assisted thoracoscopic surgery as the gold standard for lung cancer surgery. Respirology, 2020, 25 Suppl 2: 49-60.
3. Li Y, Wu X, Yang P, et al. Machine learning for lung cancer diagnosis, treatment, and prognosis. Genomics Proteomics Bioinformatics, 2022, 20(5): 850-866.
4. Murphy K, van Ginneken B, Schilham AM, et al. A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification. Med Image Anal, 2009, 13(5): 757-770.
5. Jacobs C, van Rikxoort EM, Twellmann T, et al. Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images. Med Image Anal, 2014, 18(2): 374-384.
6. Setio AA, Jacobs C, Gelderblom J, et al. Automatic detection of large pulmonary solid nodules in thoracic CT images. Med Phys, 2015, 42(10): 5642-5653.
7. Zhang S, Sun F, Wang N, et al. Computer-aided diagnosis (CAD) of pulmonary nodule of thoracic CT image using transfer learning. J Digit Imaging, 2019, 32(6): 995-1007.
8. Huang X, Sun W, Tseng TB, et al. Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks. Comput Med Imaging Graph, 2019, 74: 25-36.
9. Chen X, Duan Q, Wu R, et al. Segmentation of lung computed tomography images based on SegNet in the diagnosis of lung cancer. J Radiat Res App Sci, 2021, 14(1): 396-403.
10. Chowdary J, Suganya G, Premalatha M, et al. Impact of machine learning models in pneumonia diagnosis with features extracted from chest X-rays using VGG16. Turkish J Comp Mathem Edu, 2021, 12(5): 1521-30.
11. 方新, 陈山, 臧林. 肺磨玻璃结节CT三维重建精确定位在胸腔镜肺段切除中的应用. 中华肺部疾病杂志(电子版), 2021, 14(6): 764-766.Fang X, Chen S, Zang L. Application of CT three dimensional reconstruction and accurate positioning of pulmonary ground glass nodules in thoracoscopic segmental pneumonectomy. Chin J Lung Dis (Electronic Edition), 2021, 14(6): 764-766.
12. Yang C, Zhao D, Zhou X, et al. A comparative study of video-assisted thoracoscopic resection versus thoracotomy for neurogenic tumours arising at the thoracic apex. Interact Cardiovasc Thorac Surg, 2015, 20(1): 35-39.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

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