Abstract
Our objective is to investigate the reliability and usefulness of anatomic point–based lung zone segmentation on chest radiographs (CXRs) as a reference standard framework and to evaluate the accuracy of automated point placement. Two hundred frontal CXRs were presented to two radiologists who identified five anatomic points: two at the lung apices, one at the top of the aortic arch, and two at the costophrenic angles. Of these 1000 anatomic points, 161 (16.1%) were obscured (mostly by pleural effusions). Observer variations were investigated. Eight anatomic zones then were automatically generated from the manually placed anatomic points, and a prototype algorithm was developed using the point-based lung zone segmentation to detect cardiomegaly and levels of diaphragm and pleural effusions. A trained U-Net neural network was used to automatically place these five points within 379 CXRs of an independent database. Intra- and inter-observer variation in mean distance between corresponding anatomic points was larger for obscured points (8.7 mm and 20 mm, respectively) than for visible points (4.3 mm and 7.6 mm, respectively). The computer algorithm using the point-based lung zone segmentation could diagnostically measure the cardiothoracic ratio and diaphragm position or pleural effusion. The mean distance between corresponding points placed by the radiologist and by the neural network was 6.2 mm. The network identified 95% of the radiologist-indicated points with only 3% of network-identified points being false-positives. In conclusion, a reliable anatomic point–based lung segmentation method for CXRs has been developed with expected utility for establishing reference standards for machine learning applications.
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Abbreviations
- CXR:
-
Chest radiograph
- PA:
-
Posteroanterior
- AP:
-
Anteroposterior
- CPA:
-
Costophrenic angle
References
Giger ML, Chan HP, Boone J: Anniversary paper: history and status of CAD and quantitative image analysis: the role of medical physics and AAPM. Med. Phys. 35:5799-5820, 2008
Greenspan H, van Ginneken B, Summers RM: Deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans Med Imaging. 35:1153-1159, 2016
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van de Laak JAWM, van Ginneken B, Sánchez CI: A survey on deep learning in medical image analysis. Medical Imaging Analysis. 42:60-88, 2017
Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, Kadoury S, Tang A: Deep learning: a primer for radiologists1. RadioGraphics. 37:2113-2131, 2017
Li F, Engelmann R, Armato III SG, MacMahon H: Computer-aided nodule detection system: results in an unselected series of consecutive chest radiographs. Acad Radiol. 22:475-480, 2015
Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM: ChestX-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. arXiv: 1705.02314v5 [cs.CV] 14 Dec 2017
Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, Ding D, Bagul A, Ball RL, Langlotz C, Shpanskaya K, Lungren MP, Ng AY: CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv: 1711.05225v3 [cs.CV] 25 Dec 2017
Candemir S, Rajaraman S, Thoma G, Antani S: Deep learning for grading cardiomegaly severity in chest x-rays: an investigation. IEEE Life Sciences Conference (LSC). https://doi.org/10.1109/lsc.2018.8572113 2018
Pan I, Agarwal S, Merck D: Generalizable inter-institutional classification of abnormal chest radiographs using efficient convolutional neural networks. J Digit Imaging. 32:888-896, 2019
Cicero M, Bilbily A, Colak E, Dowdell T, Gray B, Perampaladas K, Barfett J: Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs. Investigative Radiology. 52:281-287, 2017
Nam JG, Park S, Hwang EJ, Lee JH, Jin KN, Lim KY, Vu TH, Sohn JH, Hwang S, Goo JM, Park CM: Development and validation of deep learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs. Radiology. 290:218-228, 2019
Hwang EJ, Park S, Jin KN, Kim JI, Choi SY, Lee JH, Goo JM, Aum J, Yim JJ, Cohen JG, Ferretti GR, Park CM: Development and validation of a deep learning–based automated detection algorithm for major thoracic diseases on chest radiographs. JAMA Network Open. 2(3):e191095. https://doi.org/10.1001/jamanetworkopen.2019.1095
Annarumma M, Withey SJ, Bakewell RJ, Pesce E, Goh V, Montana G: Automated triaging of adult chest radiographs with deep artificial neural networks. Radiology. 291:196–202, 2019
Dunnmon JA, Yi D, Langlotz CP, Ré C, Rubin DL, Lungren MP: Assessment of convolutional neural networks for automated classification of chest radiographs. Radiology. 290:537–544, 2019
Seah JCY, Tang JSN, Kitchen A, Gaillard F, Dixon AF: Chest radiographs in congestive heart failure: visualizing neural network learning. Radiology. 290:514–522, 2019
Jaeger S, Candemir S, Antani S, Wáng YXJ, Lu PX, Thoma G: Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quant Imaging Med Surg. 4(6):475-477, 2014
Candemir S, Jaeger S, Palaniappan K, Musco JP, Singh RK, Xue Z, Karargyris A, Antani S, Thoma G, McDonald CJ: Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans Med Imaging. 33:577-590, 2014
Maduskar P, Philipsen RHHM, Melendez J, Scholten E, Chanda D, Ayles H, Sánchez CI, van Ginneken B: Automatic detection of pleural effusion in chest radiographs. Medical Imaging Analysis. 28:22-32, 2016
Lakhani P, Sundaram B: Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 284:574-582, 2017
Santosh KC, Antani S: Automated chest x-ray screening: can lung region symmetry help detect pulmonary abnormalities. IEEE Trans Med Imaging. 37:1168-1177, 2018
Kermany DS, Goldbaum M, Cai W, Valentim CCS, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F, Dong J, Prasadha MK, Pei J, Ting MYL, Zhu J, Li C, Hewett S, Dong J, Ziyar I, Shi A, Zhang R, Zheng L, Hou R, Shi W, Fu X, Duan Y, Huu VAN, Wen C, Zhang ED, Zhang CL, L O, Wang X, Singer MA, Sun X, Xu J, Tafreshi A, Lewis A, Xia H, Zhang K: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 172:1122–1131, 2018
Rajaraman S, Candemir S, Kim I, Thoma G, Antani S: Visualization and interpretation of convolutional neural network predictions in detecting pneumonia in pediatric chest radiographs. Appl. Sci. 8, 1715; https://doi.org/10.3390/app8101715, 2018
The Radiological Society of North America: RSNA recognizes winners of pneumonia detection machine learning challenge. Released: November 26, 2018
Callen AL, Dupont SM, Price A, Laguna B, McCoy D, Do B, Talbott J, Kohli M, Narvid J: Between always and never: evaluating uncertainty in radiology reports using natural language processing. J Digit Imaging. https://doi.org/10.1007/s10278-020-00379-1, 2020
Ronneberger O, Fischer P, Brox T: U-Net: convolutional networks for biomedical image segmentation. arXiv: 1505.04597v3 [cs.CV] 18 May 2015
Ahmad WSHHW, Zaki WMDW, Fauzi MFA: Lung segmentation on standard and mobile chest radiographs using oriented Gaussian derivatives filter. BioMedical Engineering. OnLine 14:20, 2015
Yang W, Liu Y, Lin L, Yun Z, Lu Z, Feng Q, Chen W: Lung field segmentation in chest radiographs from boundary maps by a structured edge detector. IEEE Journal of Biomedical and Health Informatics. 22:842-851, 2018
Shiraishi J, Katsuragawa S, Ikezoe J, Matsumoto T, Kobayashi T, Komatsu K, Matsui M, Fujita H, Kodera Y, Doi K: Development of a digital image database for chest radiographs with and without a lung nodule: receiver characteristic analysis of radiologists’ detection of pulmonary nodules. AJR Am J Roentgenol. 174:71–74, 2000
Kao EF, Kuo YT, Hsu JS, Chou MC, Liu GC: Zone-based analysis for automated detection of abnormalities in chest radiographs. Med. Phys. 38:4241-4250, 2011
Armato III SG, Giger ML, MacMahon H: Computerized delineation and analysis of costopherenic angles in digital chest radiographs. Acad Radiol. 5:329-335, 1998
Mammarappallil JG, Anderson SA, Danelson KA, Stitzel JA, Chiles C: Estimation of pleural fluid volumes on chest radiography using computed tomography volumetric analysis: an update of the visual prediction rule. J Thorac Imaging. 30:336-339, 2015
Liu H, Wang L, Nan Y, Jin F, Wang Q, Pu J: SDFN: Segmentation-based deep fusion network for thoracic disease classification in chest X-ray images. Computerized Medical Imaging and Graphics. 75:66-73, 2019
Philipsen RHHM, Maduskar P, Hogeweg L, Melendez J, Sanchez CI, van Ginneken B: Localized energy-based normalization of medical images: application to chest radiography. IEEE Trans Med Imaging. 34:1965-1975, 2015
Candemir S, Jaeger S, Antani S, Bagci U, Folio LR, Xu Z, Thoma G: Atlas-based rib-bone detection in chest X-rays. Computerized Medical Imaging and Graphics. 51:32-39, 2016
Lu MT, Ivanov A, Mayrhofer T, Hosny A, Aerts HJW, Hoffmann U: Deep learning to assess long-term mortality from chest radiographs. JAMA Network Open. 2(7): e197416. https://doi.org/10.1001/jamanetworkopen.2019.7416
Sahiner B, Pezeshk A, Hadjiiski LM, Wang X, Drukker K, Cha KH, Summers RM, Giger ML: Deep learning in medical imaging and radiation therapy. Med Phys. Jan;46(1):e1-e36. https://doi.org/10.1002/mp.13264. Epub 2018 Nov 20. PMID: 30367497, 2019
Renard F, Guedria S, Palma ND, Vuileerme N. Variability and reproducibility in deep learning for medical image segmentation. Sci Rep 10, 13724. https://doi.org/10.1038/s41598-020-69920-0, 2020
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MLG is a stockholder in R2 technology/Hologic and QView, receives royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi and Toshiba, and is a cofounder of and equity holder in Quantitative Insights (now Qlarity Imaging). It is the University of Chicago Conflict of Interest Policy that investigators disclose publicly actual or potential significant financial interest that would reasonably appear to be directly and significantly affected by the research activities. HM is a shareholder of Hologic and a consultant for Riverain Technologies. FL, SGA, RE, LL, MLG, and HM receive royalties from multiple companies through The University of Chicago (UC Tech).
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Li, F., Armato, S.G., Engelmann, R. et al. Anatomic Point–Based Lung Region with Zone Identification for Radiologist Annotation and Machine Learning for Chest Radiographs. J Digit Imaging 34, 922–931 (2021). https://doi.org/10.1007/s10278-021-00494-7
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DOI: https://doi.org/10.1007/s10278-021-00494-7