Sunyi Zheng1, Ludo J Cornelissen1, Xiaonan Cui2, Xueping Jing1, Raymond N J Veldhuis3, Matthijs Oudkerk4, Peter M A van Ooijen1. 1. Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, 9713 AV, Groningen, The Netherlands. 2. Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Centre of Cancer, 300060, Tianjin, China. 3. Faculty of Electrical Engineering, University of Twente, 7500 AE, Enschede, The Netherlands. 4. Faculty of Medical Science, University of Groningen, 9713 AV, Groningen, The Netherlands.
Abstract
PURPOSE: Early detection of lung cancer is of importance since it can increase patients' chances of survival. To detect nodules accurately during screening, radiologists would commonly take the axial, coronal, and sagittal planes into account, rather than solely the axial plane in clinical evaluation. Inspired by clinical work, the paper aims to develop an accurate deep learning framework for nodule detection by a combination of multiple planes. METHODS: The nodule detection system is designed in two stages, multiplanar nodule candidate detection, multiscale false positive (FP) reduction. At the first stage, a deeply supervised encoder-decoder network is trained by axial, coronal, and sagittal slices for the candidate detection task. All possible nodule candidates from the three different planes are merged. To further refine results, a three-dimensional multiscale dense convolutional neural network that extracts multiscale contextual information is applied to remove non-nodules. In the public LIDC-IDRI dataset, 888 computed tomography scans with 1186 nodules accepted by at least three of four radiologists are selected to train and evaluate our proposed system via a tenfold cross-validation scheme. The free-response receiver operating characteristic curve is used for performance assessment. RESULTS: The proposed system achieves a sensitivity of 94.2% with 1.0 FP/scan and a sensitivity of 96.0% with 2.0 FPs/scan. Although it is difficult to detect small nodules (i.e., <6 mm), our designed CAD system reaches a sensitivity of 93.4% (95.0%) of these small nodules at an overall FP rate of 1.0 (2.0) FPs/scan. At the nodule candidate detection stage, results show that the system with a multiplanar method is capable to detect more nodules compared to using a single plane. CONCLUSION: Our approach achieves good performance not only for small nodules but also for large lesions on this dataset. This demonstrates the effectiveness of our developed CAD system for lung nodule detection.
PURPOSE: Early detection of lung cancer is of importance since it can increase patients' chances of survival. To detect nodules accurately during screening, radiologists would commonly take the axial, coronal, and sagittal planes into account, rather than solely the axial plane in clinical evaluation. Inspired by clinical work, the paper aims to develop an accurate deep learning framework for nodule detection by a combination of multiple planes. METHODS: The nodule detection system is designed in two stages, multiplanar nodule candidate detection, multiscale false positive (FP) reduction. At the first stage, a deeply supervised encoder-decoder network is trained by axial, coronal, and sagittal slices for the candidate detection task. All possible nodule candidates from the three different planes are merged. To further refine results, a three-dimensional multiscale dense convolutional neural network that extracts multiscale contextual information is applied to remove non-nodules. In the public LIDC-IDRI dataset, 888 computed tomography scans with 1186 nodules accepted by at least three of four radiologists are selected to train and evaluate our proposed system via a tenfold cross-validation scheme. The free-response receiver operating characteristic curve is used for performance assessment. RESULTS: The proposed system achieves a sensitivity of 94.2% with 1.0 FP/scan and a sensitivity of 96.0% with 2.0 FPs/scan. Although it is difficult to detect small nodules (i.e., <6 mm), our designed CAD system reaches a sensitivity of 93.4% (95.0%) of these small nodules at an overall FP rate of 1.0 (2.0) FPs/scan. At the nodule candidate detection stage, results show that the system with a multiplanar method is capable to detect more nodules compared to using a single plane. CONCLUSION: Our approach achieves good performance not only for small nodules but also for large lesions on this dataset. This demonstrates the effectiveness of our developed CAD system for lung nodule detection.
Authors: Dong Ming Xu; Hester Gietema; Harry de Koning; René Vernhout; Kristiaan Nackaerts; Mathias Prokop; Carla Weenink; Jan-Willem Lammers; Harry Groen; Matthijs Oudkerk; Rob van Klaveren Journal: Lung Cancer Date: 2006-09-20 Impact factor: 5.705
Authors: Mingzhu Liang; Wei Tang; Dong Ming Xu; Artit C Jirapatnakul; Anthony P Reeves; Claudia I Henschke; David Yankelevitz Journal: Radiology Date: 2016-03-28 Impact factor: 11.105
Authors: Denise R Aberle; Amanda M Adams; Christine D Berg; William C Black; Jonathan D Clapp; Richard M Fagerstrom; Ilana F Gareen; Constantine Gatsonis; Pamela M Marcus; JoRean D Sicks Journal: N Engl J Med Date: 2011-06-29 Impact factor: 91.245
Authors: Samuel G Armato; Geoffrey McLennan; Luc Bidaut; Michael F McNitt-Gray; Charles R Meyer; Anthony P Reeves; Binsheng Zhao; Denise R Aberle; Claudia I Henschke; Eric A Hoffman; Ella A Kazerooni; Heber MacMahon; Edwin J R Van Beeke; David Yankelevitz; Alberto M Biancardi; Peyton H Bland; Matthew S Brown; Roger M Engelmann; Gary E Laderach; Daniel Max; Richard C Pais; David P Y Qing; Rachael Y Roberts; Amanda R Smith; Adam Starkey; Poonam Batrah; Philip Caligiuri; Ali Farooqi; Gregory W Gladish; C Matilda Jude; Reginald F Munden; Iva Petkovska; Leslie E Quint; Lawrence H Schwartz; Baskaran Sundaram; Lori E Dodd; Charles Fenimore; David Gur; Nicholas Petrick; John Freymann; Justin Kirby; Brian Hughes; Alessi Vande Casteele; Sangeeta Gupte; Maha Sallamm; Michael D Heath; Michael H Kuhn; Ekta Dharaiya; Richard Burns; David S Fryd; Marcos Salganicoff; Vikram Anand; Uri Shreter; Stephen Vastagh; Barbara Y Croft Journal: Med Phys Date: 2011-02 Impact factor: 4.071
Authors: Joan E Walter; Marjolein A Heuvelmans; Kevin Ten Haaf; Rozemarijn Vliegenthart; Carlijn M van der Aalst; Uraujh Yousaf-Khan; Peter M A van Ooijen; Kristiaan Nackaerts; Harry J M Groen; Geertruida H De Bock; Harry J de Koning; Matthijs Oudkerk Journal: Thorax Date: 2018-12-27 Impact factor: 9.139
Authors: Colin Jacobs; Eva M van Rikxoort; Keelin Murphy; Mathias Prokop; Cornelia M Schaefer-Prokop; Bram van Ginneken Journal: Eur Radiol Date: 2015-10-06 Impact factor: 5.315