Publications

1. Nakai H, Suman G, Adamo DA, Navin PJ, Bookwalter CA, LeGout JD, Chen FK, Wellnitz CV, Silva AC, Thomas JV, Kawashima A, Fan JW, Froemming AT, Lomas DJ, Humphreys MR, Dora C, Korfiatis P, Takahashi N. Natural language processing pipeline to extract prostate cancer-related information from clinical notes. Eur Radiol. 2024 Dec; 34 (12):7878-7891 Epub 2024 June 06
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2. Cortes P, Mistretta T, Jackson B, Olson CG, Salih AM, Stancampiano FF, Korfiatis P, Klug JR, Harris DM, Echols J, Carter RE, Ji B, Hardway HD, Wallace MB, Kumbhari V, Bi Y. Association Between Body Composition Measured by Artificial Intelligence and Long-Term Sequelae After Acute Pancreatitis. Dig Dis Sci. 2024 Nov; 69 (11):4290-4301 Epub 2024 Oct 22
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3. Wang P, Kline TL, Missert AD, Cook CJ, Callstrom MR, Chan A, Hartman RP, Kelm ZS, Korfiatis P. A Classification-Based Adaptive Segmentation Pipeline: Feasibility Study Using Polycystic Liver Disease and Metastases from Colorectal Cancer CT Images. J Imaging Inform Med. 2024 Oct; 37 (5):2186-2194 Epub 2024 Apr 08
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4. Kuanar S, Cai J, Nakai H, Nagayama H, Takahashi H, LeGout J, Kawashima A, Froemming A, Mynderse L, Dora C, Humphreys M, Klug J, Korfiatis P, Erickson B, Takahashi N. Transition-zone PSA-density calculated from MRI deep learning prostate zonal segmentation model for prediction of clinically significant prostate cancer. Abdom Radiol (NY). 2024 Oct; 49 (10):3722-3734 Epub 2024 June 19
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5. Cao W, Howe BM, Ramanathan S, Rhodes NG, Korfiatis P, Amrami KK, Spinner RJ, Kline TL. Non-traumatic brachial plexopathy identification from routine MRIs: Retrospective studies with deep learning networks. Eur J Radiol. 2024 Sep 18; 181:111744 Epub 2024 Sept 18
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6. Cai JC, Nakai H, Kuanar S, Froemming AT, Bolan CW, Kawashima A, Takahashi H, Mynderse LA, Dora CD, Humphreys MR, Korfiatis P, Rouzrokh P, Bratt AK, Conte GM, Erickson BJ, Takahashi N. Fully Automated Deep Learning Model to Detect Clinically Significant Prostate Cancer at MRI. Radiology. 2024 Aug; 312 (2):e232635
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7. Cao W, Howe BM, Wright DE, Ramanathan S, Rhodes NG, Korfiatis P, Amrami KK, Spinner RJ, Kline TL. Abnormal Brachial Plexus Differentiation from Routine Magnetic Resonance Imaging: An AI-based Approach. Neuroscience. 2024 May 14; 546:178-187 Epub 2024 Mar 21
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8. Mukherjee S, Korfiatis P, Patnam NG, Trivedi KH, Karbhari A, Suman G, Fletcher JG, Goenka AH. Assessing the robustness of a machine-learning model for early detection of pancreatic adenocarcinoma (PDA): evaluating resilience to variations in image acquisition and radiomics workflow using image perturbation methods. Abdom Radiol (NY). 2024 Mar; 49 (3):964-974 Epub 2024 Jan 04
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9. Korfiatis P, Suman G, Patnam NG, Trivedi KH, Karbhari A, Mukherjee S, Cook C, Klug JR, Patra A, Khasawneh H, Rajamohan N, Fletcher JG, Truty MJ, Majumder S, Bolan CW, Sandrasegaran K, Chari ST, Goenka AH. Automated Artificial Intelligence Model Trained on a Large Data Set Can Detect Pancreas Cancer on Diagnostic Computed Tomography Scans As Well As Visually Occult Preinvasive Cancer on Prediagnostic Computed Tomography Scans. Gastroenterology. 2023 Dec; 165 (6):1533-1546.e4 Epub 2023 Aug 30
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10. Gregory AV, Denic A, Moustafa A, Dasaraju PG, Poudyal B, Augustine JJ, Mullan AF, Korfiatis P, Rule AD, Kline TL. The Number and Size of Individual Kidney Medullary Pyramids is Associated with Clinical Characteristics, Kidney Biopsy Findings, and CKD Outcomes among Kidney Donors. J Am Soc Nephrol. 2023 Oct 1; 34 (10):1752-1763 Epub 2023 Aug 10
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11. Mukherjee S, Korfiatis P, Khasawneh H, Rajamohan N, Patra A, Suman G, Singh A, Thakkar J, Patnam NG, Trivedi KH, Karbhari A, Chari ST, Truty MJ, Halfdanarson TR, Bolan CW, Sandrasegaran K, Majumder S, Goenka AH. Bounding box-based 3D AI model for user-guided volumetric segmentation of pancreatic ductal adenocarcinoma on standard-of-care CTs. Pancreatology. 2023 Aug; 23 (5):522-529 Epub 2023 May 26
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12. Gottlich HC, Gregory AV, Sharma V, Khanna A, Moustafa AU, Lohse CM, Potretzke TA, Korfiatis P, Potretzke AM, Denic A, Rule AD, Takahashi N, Erickson BJ, Leibovich BC, Kline TL. Effect of Dataset Size and Medical Image Modality on Convolutional Neural Network Model Performance for Automated Segmentation: A CT and MR Renal Tumor Imaging Study. J Digit Imaging. 2023 Aug; 36 (4):1770-1781 Epub 2023 Mar 17
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13. Potretzke TA, Korfiatis P, Blezek DJ, Edwards ME, Klug JR, Cook CJ, Gregory AV, Harris PC, Chebib FT, Hogan MC, Torres VE, Bolan CW, Sandrasegaran K, Kawashima A, Collins JD, Takahashi N, Hartman RP, Williamson EE, King BF, Callstrom MR, Erickson BJ, Kline TL. Clinical Implementation of an Artificial Intelligence Algorithm for Magnetic Resonance-Derived Measurement of Total Kidney Volume. Mayo Clin Proc. 2023 May; 98 (5):689-700 Epub 2023 Mar 16
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14. Nandakumar B, Baffour F, Abdallah NH, Kumar SK, Dispenzieri A, Buadi FK, Dingli D, Lacy MQ, Hayman SR, Kapoor P, Leung N, Fonder A, Hobbs M, Hwa YL, Muchtar E, Warsame R, Kourelis TV, Go RS, Kyle RA, Gertz MA, Rajkumar SV, Klug J, Korfiatis P, Gonsalves WI. Sarcopenia identified by computed tomography imaging using a deep learning-based segmentation approach impacts survival in patients with newly diagnosed multiple myeloma. Cancer. 2023 Feb 1; 129 (3):385-392 Epub 2022 Nov 22
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15. Cao W, Howe B, Rhodes N, Ramanathan S, Korfiatis P, Amrami K, Spinner R, Kline T. A texture neural network to predict the abnormal brachial plexus from routine magnetic resonance imaging. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2023; 14227 LNCS:470-80
    
16. Gottlich HC, Korfiatis P, Gregory AV, Kline TL. AI in the Loop: functionalizing fold performance disagreement to monitor automated medical image segmentation workflows. Front Radiol. 2023; 3:1223294 Epub 2023 Sept 15
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17. Mukherjee S, Patra A, Khasawneh H, Korfiatis P, Rajamohan N, Suman G, Majumder S, Panda A, Johnson MP, Larson NB, Wright DE, Kline TL, Fletcher JG, Chari ST, Goenka AH. Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis. Gastroenterology. 2022 Nov; 163 (5):1435-1446.e3 Epub 2022 July 01
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18. Wright DE, Mukherjee S, Patra A, Khasawneh H, Korfiatis P, Suman G, Chari ST, Kudva YC, Kline TL, Goenka AH. Radiomics-based machine learning (ML) classifier for detection of type 2 diabetes on standard-of-care abdomen CTs: a proof-of-concept study. Abdom Radiol (NY). 2022 Nov; 47 (11):3806-3816 Epub 2022 Sept 10
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19. Shrestha P, Poudyal B, Yadollahi S, E Wright D, V Gregory A, D Warner J, Korfiatis P, C Green I, L Rassier S, Mariani A, Kim B, Laughlin-Tommaso SK, L Kline T. A systematic review on the use of artificial intelligence in gynecologic imaging - Background, state of the art, and future directions. Gynecol Oncol. 2022 Sep; 166 (3):596-605 Epub 2022 July 29
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20. Khasawneh H, Patra A, Rajamohan N, Suman G, Klug J, Majumder S, Chari ST, Korfiatis P, Goenka AH. Volumetric Pancreas Segmentation on Computed Tomography: Accuracy and Efficiency of a Convolutional Neural Network Versus Manual Segmentation in 3D Slicer in the Context of Interreader Variability of Expert Radiologists. J Comput Assist Tomogr. 2022 Sep 1 Epub 2022 Sept 01
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21. Korfiatis P, Denic A, Edwards ME, Gregory AV, Wright DE, Mullan A, Augustine J, Rule AD, Kline TL. Automated Segmentation of Kidney Cortex and Medulla in CT Images: A Multisite Evaluation Study. J Am Soc Nephrol. 2022 Feb; 33 (2):420-430 Epub 2021 Dec 07
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22. Blezek DJ, Olson-Williams L, Missert A, Korfiatis P. AI Integration in the Clinical Workflow. J Digit Imaging. 2021 Dec; 34 (6):1435-1446 Epub 2021 Oct 22
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23. Bratt A, Blezek DJ, Ryan WJ, Philbrick KA, Rajiah P, Tandon YK, Walkoff LA, Cai JC, Sheedy EN, Korfiatis P, Williamson EE, Erickson BJ, Collins JD. Deep Learning Improves the Temporal Reproducibility of Aortic Measurement. J Digit Imaging. 2021 Oct; 34 (5):1183-1189 Epub 2021 May 28
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24. Suman G, Patra A, Korfiatis P, Majumder S, Chari ST, Truty MJ, Fletcher JG, Goenka AH. Quality gaps in public pancreas imaging datasets: Implications & challenges for AI applications. Pancreatology. 2021 Aug; 21 (5):1001-1008 Epub 2021 Apr 02
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25. Panda A, Korfiatis P, Suman G, Garg SK, Polley EC, Singh DP, Chari ST, Goenka AH. Two-stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra-reader and inter-reader reliability at full and reduced radiation dose on an external dataset. Med Phys. 2021 May; 48 (5):2468-2481 Epub 2021 Mar 16
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26. Lok UW, Huang C, Gong P, Tang S, Yang L, Zhang W, Kim Y, Korfiatis P, Blezek DJ, Lucien F, Zheng R, Trzasko JD, Chen S. Fast super-resolution ultrasound microvessel imaging using spatiotemporal data with deep fully convolutional neural network. Phys Med Biol. 2021 Mar 23; 66 (7)
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27. Suman G, Panda A, Korfiatis P, Edwards ME, Garg S, Blezek DJ, Chari ST, Goenka AH. Development of a volumetric pancreas segmentation CT dataset for AI applications through trained technologists: a study during the COVID 19 containment phase. Abdom Radiol (NY). 2020 Dec; 45 (12):4302-4310 Epub 2020 Sept 16
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28. Weston AD, Korfiatis P, Philbrick KA, Conte GM, Kostandy P, Sakinis T, Zeinoddini A, Boonrod A, Moynagh M, Takahashi N, Erickson BJ. Complete abdomen and pelvis segmentation using U-net variant architecture. Med Phys. 2020 Nov; 47 (11):5609-5618 Epub 2020 Oct 07
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29. Bell LC, Semmineh N, An H, Eldeniz C, Wahl R, Schmainda KM, Prah MA, Erickson BJ, Korfiatis P, Wu C, Sorace AG, Yankeelov TE, Rutledge N, Chenevert TL, Malyarenko D, Liu Y, Brenner A, Hu LS, Zhou Y, Boxerman JL, Yen YF, Kalpathy-Cramer J, Beers AL, Muzi M, Madhuranthakam AJ, Pinho M, Johnson B, Quarles CC. Evaluating the Use of rCBV as a Tumor Grade and Treatment Response Classifier Across NCI Quantitative Imaging Network Sites: Part II of the DSC-MRI Digital Reference Object (DRO) Challenge. Tomography. 2020 Jun; 6 (2):203-208
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30. Petrova L, Korfiatis P, Petr O, LaChance DH, Parney I, Buckner JC, Erickson BJ. Cerebral blood volume and apparent diffusion coefficient - Valuable predictors of non-response to bevacizumab treatment in patients with recurrent glioblastoma. J Neurol Sci. 2019 Oct 15; 405:116433 Epub 2019 Aug 23
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31. Philbrick KA, Weston AD, Akkus Z, Kline TL, Korfiatis P, Sakinis T, Kostandy P, Boonrod A, Zeinoddini A, Takahashi N, Erickson BJ. RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning. J Digit Imaging. 2019 Aug; 32 (4):571-581
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32. Korfiatis P, Erickson B. Deep learning can see the unseeable: predicting molecular markers from MRI of brain gliomas. Clin Radiol. 2019 May; 74 (5):367-373 Epub 2019 Mar 05
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33. Bell LC, Semmineh N, An H, Eldeniz C, Wahl R, Schmainda KM, Prah MA, Erickson BJ, Korfiatis P, Wu C, Sorace AG, Yankeelov TE, Rutledge N, Chenevert TL, Malyarenko D, Liu Y, Brenner A, Hu LS, Zhou Y, Boxerman JL, Yen YF, Kalpathy-Cramer J, Beers AL, Muzi M, Madhuranthakam AJ, Pinho M, Johnson B, Quarles CC. Evaluating Multisite rCBV Consistency from DSC-MRI Imaging Protocols and Postprocessing Software Across the NCI Quantitative Imaging Network Sites Using a Digital Reference Object (DRO). Tomography. 2019 Mar; 5(1):110-117.
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34. Weston AD, Korfiatis P, Kline TL, Philbrick KA, Kostandy P, Sakinis T, Sugimoto M, Takahashi N, Erickson BJ. Automated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning. Radiology. 2019 Mar; 290 (3):669-679 Epub 2018 Dec 11
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35. Philbrick KA, Yoshida K, Inoue D, Akkus Z, Kline TL, Weston AD, Korfiatis P, Takahashi N, Erickson BJ. What Does Deep Learning See? Insights From a Classifier Trained to Predict Contrast Enhancement Phase From CT Images. AJR Am J Roentgenol. 2018 Dec; 211 (6):1184-1193 Epub 2018 Nov 07
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36. Schmainda KM, Prah MA, Rand SD, Liu Y, Logan B, Muzi M, Rane SD, Da X, Yen YF, Kalpathy-Cramer J, Chenevert TL, Hoff B, Ross B, Cao Y, Aryal MP, Erickson B, Korfiatis P, Dondlinger T, Bell L, Hu L, Kinahan PE, Quarles CC. Multisite Concordance of DSC-MRI Analysis for Brain Tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project. AJNR Am J Neuroradiol. 2018 Jun; 39 (6):1008-1016 Epub 2018 May 24
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37. Bae Y, Kumarasamy K, Ali IM, Korfiatis P, Akkus Z, Erickson BJ. Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI. J Digit Imaging. 2018 Apr; 31 (2):252-261
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38. Erickson BJ, Korfiatis P, Kline TL, Akkus Z, Philbrick K, Weston AD. Deep Learning in Radiology: Does One Size Fit All? J Am Coll Radiol. 2018 Mar; 15 (3 Pt B):521-526 Epub 2018 Jan 31
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39. Kline TL, Edwards ME, Garg I, Irazabal MV, Korfiatis P, Harris PC, King BF, Torres VE, Venkatesh SK, Erickson BJ. Quantitative MRI of kidneys in renal disease. Abdom Radiol (NY). 2018 Mar; 43 (3):629-638
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40. Perry LA, Korfiatis P, Agrawal JP, Erickson BJ. Increased signal intensity within glioblastoma resection cavities on fluid-attenuated inversion recovery imaging to detect early progressive disease in patients receiving radiotherapy with concomitant temozolomide therapy. Neuroradiology. 2018 Jan; 60 (1):35-42 Epub 2017 Nov 04
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41. Kline TL, Korfiatis P, Edwards ME, Bae KT, Yu A, Chapman AB, Mrug M, Grantham JJ, Landsittel D, Bennett WM, King BF, Harris PC, Torres VE, Erickson BJ, CRISP Investigators. Image texture features predict renal function decline in patients with autosomal dominant polycystic kidney disease. Kidney Int. 2017 Nov; 92 (5):1206-1216 Epub 2017 May 20
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42. Korfiatis P, Kline TL, Lachance DH, Parney IF, Buckner JC, Erickson BJ. Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status. J Digit Imaging. 2017 Oct; 30 (5):622-628
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43. Kline TL, Korfiatis P, Edwards ME, Blais JD, Czerwiec FS, Harris PC, King BF, Torres VE, Erickson BJ. Performance of an Artificial Multi-observer Deep Neural Network for Fully Automated Segmentation of Polycystic Kidneys. J Digit Imaging. 2017 Aug; 30 (4):442-448
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44. Erickson BJ, Korfiatis P, Akkus Z, Kline T, Philbrick K. Toolkits and Libraries for Deep Learning. J Digit Imaging. 2017 Aug; 30 (4):400-405
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45. Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine Learning for Medical Imaging. Radiographics. 2017 Mar-Apr; 37 (2):505-515 Epub 2017 Feb 17
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46. Peter R, Korfiatis P, Blezek D, Oscar Beitia A, Stepan-Buksakowska I, Horinek D, Flemming KD, Erickson BJ. A quantitative symmetry-based analysis of hyperacute ischemic stroke lesions in noncontrast computed tomography. Med Phys. 2017 Jan; 44 (1):192-199 Epub 2017 Jan 08
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47. Korfiatis P, Kline TL, Erickson BJ. Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning. Tomography. 2016 Dec; 2 (4):334-340
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48. Korfiatis P, Kline TL, Kelm ZS, Carter RE, Hu LS, Erickson BJ. Dynamic Susceptibility Contrast-MRI Quantification Software Tool: Development and Evaluation. Tomography. 2016 Dec; 2 (4):448-456
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49. Kline TL, Edwards ME, Korfiatis P, Akkus Z, Torres VE, Erickson BJ. Semiautomated Segmentation of Polycystic Kidneys in T2-Weighted MR Images. AJR Am J Roentgenol. 2016 Sep; 207 (3):605-13 Epub 2016 June 24
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50. Korfiatis P, Kline TL, Coufalova L, Lachance DH, Parney IF, Carter RE, Buckner JC, Erickson BJ. MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas. Med Phys. 2016 Jun; 43 (6):2835-2844
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51. Kline TL, Irazabal MV, Ebrahimi B, Hopp K, Udoji KN, Warner JD, Korfiatis P, Mishra PK, Macura SI, Venkatesh SK, Lerman LO, Harris PC, Torres VE, King BF, Erickson BJ. Utilizing magnetization transfer imaging to investigate tissue remodeling in a murine model of autosomal dominant polycystic kidney disease. Magn Reson Med. 2016 Apr; 75 (4):1466-73 Epub 2015 May 13
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52. Kline TL, Korfiatis P, Edwards ME, Warner JD, Irazabal MV, King BF, Torres VE, Erickson BJ. Automatic total kidney volume measurement on follow-up magnetic resonance images to facilitate monitoring of autosomal dominant polycystic kidney disease progression. Nephrol Dial Transplant. 2016 Feb; 31 (2):241-8 Epub 2015 Aug 31
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53. Hu LS, Kelm Z, Korfiatis P, Dueck AC, Elrod C, Ellingson BM, Kaufmann TJ, Eschbacher JM, Karis JP, Smith K, Nakaji P, Brinkman D, Pafundi D, Baxter LC, Erickson BJ. Impact of Software Modeling on the Accuracy of Perfusion MRI in Glioma. AJNR Am J Neuroradiol. 2015 Dec; 36 (12):2242-9 Epub 2015 Sept 10
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54. Korfiatis PD, Kline TL, Blezek DJ, Langer SG, Ryan WJ, Erickson BJ. MIRMAID: A Content Management System for Medical Image Analysis Research. Radiographics. 2015 Sep-Oct; 35 (5):1461-8 Epub 2015 Aug 18
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55. Akkus Z, Sedlar J, Coufalova L, Korfiatis P, Kline TL, Warner JD, Agrawal J, Erickson BJ. Semi-automated segmentation of pre-operative low grade gliomas in magnetic resonance imaging. Cancer Imaging. 2015 Aug 14; 15 (1):12
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56. Vlachopoulos G, Korfiatis P, Skiadopoulos S, Kazantzi A, Kalogeropoulou C, Pratikakis I, Costaridou L. Selecting registration schemes in case of interstitial lung disease follow-up in CT. MedPhys. 2015; 42(8):4511.
    
57. Kelm ZS, Korfiatis PD, Lingineni RK, Daniels JR, Buckner JC, Lachance DH, Parney IF, Carter RE, Erickson BJ. Variability and accuracy of different software packages for dynamic susceptibility contrast magnetic resonance imaging for distinguishing glioblastoma progression from pseudoprogression. J Med Imaging (Bellingham). 2015 Apr; 2 (2):026001 Epub 2015 May 26
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58. Korfiatis P, Erickson B. The basics of diffusion and perfusion imaging in brain tumors. Appl Radiol. 2014 Jul; 43 (7):22-29 Epub 2014 July 04
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59. Kazantzi A, Costaridou L, Skiadopoulos S, Korfiatis P, Karahaliou A, Daoussis D, Andonopoulos A, Kalogeropoulou C. Automated 3D interstitial lung disease extent quantification: performance evaluation and correlation to PFTs. J Digit Imaging. 2014; 27(3):280.
    
60. Georgakopoulos I, Tsantis S, Georgakopoulos P, Korfiatis P, Fanti E, Martelli M, Costaridou L, Petsas T, Panayiotakis G, Martelli FS. The impact of Platelet Rich Plasma (PRP) in osseointegration of oral implants in dental panoramic radiography: texture based evaluation. Clin Cases Miner Bone Metab. 2014 Jan; 11: (1)59-66.
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61. Korfiatis PD, Kalogeropoulou C, Karahaliou AN, Kazantzi AD, Costaridou L. Vessel tree segmentation in presence of interstitial lung disease in MDCT. IEEE Transactions Information Technology Biomedicine. 2011; 15(3):214.
    
62. Daoussis D, Liossis SN, Tsamandas AC, Kalogeropoulou C, Kazantzi A, Korfiatis P, Yiannopoulos G, Andonopoulos AP. Is there a role for B-cell depletion as therapy for scleroderma? A case report and review of the literature. Semin Arthritis Rheum. 2010 Oct; 40: (2)127-36.
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63. Arikidis NS, Karahaliou A, Skiadopoulos S, Korfiatis P, Likaki E, Panayiotakis G, Costaridou L. Size-adapted microcalcification segmentation in mammography utilizing scale-space signatures Computerized Medical Imaging and Graphics.2010;34:(6)487.
    
64. Korfiatis PD, Karahaliou AN, Kazantzi AD, Kalogeropoulou C, Costaridou L. Texture-based identification and characterization of interstitial pneumonia patterns in lung multidetector CT. IEEE Transactions Information Technology Biomedicine. 2010; 14(3):675.
    
65. Korfiatis P, Kalogeropoulou C, Daousis D, Petsas T, Andonopoulos A, Costaridou L. Exploiting unsupervised and supervised classification for segmentation of the pathological lung in CT. J Instrum. 2009; 4(07).
    
66. Korfiatis P, Kalogeropoulou C, Karahaliou A, Kazantzi A, Skiadopoulos S, Costaridou L. Texture classification-based segmentation of lung affected by interstitial pneumonia in high-resolution CT. MedPhys. 2008; 35(12):5290.
    
67. Korfiatis P, Skiadopoulos S, Sakellaropoulos P, Kalogeropoulou C, Costaridou L. Combining 2D wavelet edge highlighting and 3D thresholding for lung segmentation in thin-slice CT. Br J Radiol. 2007 Dec; 80: (960)996-1004.
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