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Automatic detection of pulmonary nodules on CT images with YOLOv3: development and evaluation using simulated and patient data.

Publication ,  Journal Article
Liu, C; Hu, S-C; Wang, C; Lafata, K; Yin, F-F
Published in: Quantitative imaging in medicine and surgery
October 2020

To develop a high-efficiency pulmonary nodule computer-aided detection (CAD) method for localization and diameter estimation.The developed CAD method centralizes a novel convolutional neural network (CNN) algorithm, You Only Look Once (YOLO) v3, as a deep learning approach. This method is featured by two distinct properties: (I) an automatic multi-scale feature extractor for nodule feature screening, and (II) a feature-based bounding box generator for nodule localization and diameter estimation. Two independent studies were performed to train and evaluate this CAD method. One study comprised of a computer simulation that utilized computer-based ground truth. In this study, 300 CT scans were simulated by Cardiac-torso (XCAT) digital phantom. Spherical nodules of various sizes (i.e., 3-10 mm in diameter) were randomly implanted within the lung region of the simulated images-the second study utilized human-based ground truth in patients. The CAD method was developed by CT scans sourced from the LIDC-IDRI database. CT scans with slice thickness above 2.5 mm were excluded, leaving 888 CT images for analysis. A 10-fold cross-validation procedure was implemented in both studies to evaluate network hyper-parameterization and generalization. The overall accuracy of the CAD method was evaluated by the detection sensitivities, in response to average false positives (FPs) per image. In the patient study, the detection accuracy was further compared against 9 recently published CAD studies using free-receiver response operating characteristic (FROC) curve analysis. Localization and diameter estimation accuracies were quantified by the mean and standard error between the predicted value and ground truth.The average results among the 10 cross-validation folds in both studies demonstrated the CAD method achieved high detection accuracy. The sensitivity was 99.3% (FPs =1), and improved to 100% (FPs =4) in the simulation study. The corresponding sensitivities were 90.0% and 95.4% in the patient study, displaying superiority over several conventional and CNN-based lung nodule CAD methods in the FROC curve analysis. Nodule localization and diameter estimation errors were less than 1 mm in both studies. The developed CAD method achieved high computational efficiency: it yields nodule-specific quantitative values (i.e., number, existence confidence, central coordinates, and diameter) within 0.1 s for 2D CT slice inputs.The reported results suggest that the developed lung pulmonary nodule CAD method possesses high accuracies of nodule localization and diameter estimation. The high computational efficiency enables its potential clinical application in the future.

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Published In

Quantitative imaging in medicine and surgery

DOI

EISSN

2223-4306

ISSN

2223-4292

Publication Date

October 2020

Volume

10

Issue

10

Start / End Page

1917 / 1929

Related Subject Headings

  • 5102 Atomic, molecular and optical physics
  • 4003 Biomedical engineering
  • 0299 Other Physical Sciences
  • 0205 Optical Physics
  • 0204 Condensed Matter Physics
 

Citation

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ICMJE
MLA
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Liu, C., Hu, S.-C., Wang, C., Lafata, K., & Yin, F.-F. (2020). Automatic detection of pulmonary nodules on CT images with YOLOv3: development and evaluation using simulated and patient data. Quantitative Imaging in Medicine and Surgery, 10(10), 1917–1929. https://doi.org/10.21037/qims-19-883
Liu, Chenyang, Shen-Chiang Hu, Chunhao Wang, Kyle Lafata, and Fang-Fang Yin. “Automatic detection of pulmonary nodules on CT images with YOLOv3: development and evaluation using simulated and patient data.Quantitative Imaging in Medicine and Surgery 10, no. 10 (October 2020): 1917–29. https://doi.org/10.21037/qims-19-883.
Liu C, Hu S-C, Wang C, Lafata K, Yin F-F. Automatic detection of pulmonary nodules on CT images with YOLOv3: development and evaluation using simulated and patient data. Quantitative imaging in medicine and surgery. 2020 Oct;10(10):1917–29.
Liu, Chenyang, et al. “Automatic detection of pulmonary nodules on CT images with YOLOv3: development and evaluation using simulated and patient data.Quantitative Imaging in Medicine and Surgery, vol. 10, no. 10, Oct. 2020, pp. 1917–29. Epmc, doi:10.21037/qims-19-883.
Liu C, Hu S-C, Wang C, Lafata K, Yin F-F. Automatic detection of pulmonary nodules on CT images with YOLOv3: development and evaluation using simulated and patient data. Quantitative imaging in medicine and surgery. 2020 Oct;10(10):1917–1929.

Published In

Quantitative imaging in medicine and surgery

DOI

EISSN

2223-4306

ISSN

2223-4292

Publication Date

October 2020

Volume

10

Issue

10

Start / End Page

1917 / 1929

Related Subject Headings

  • 5102 Atomic, molecular and optical physics
  • 4003 Biomedical engineering
  • 0299 Other Physical Sciences
  • 0205 Optical Physics
  • 0204 Condensed Matter Physics