Login Register Cart 0
Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Review Article

A Survey on Machine Learning and Deep Learning-based Computer-Aided Methods for Detection of Polyps in CT Colonography

Author(s): Niharika Hegde, M. Shishir, S. Shashank, P. Dayananda* and Mrityunjaya V. Latte

Volume 17, Issue 1, 2021

Published on: 15 April, 2020

Page: [3 - 15] Pages: 13

DOI: 10.2174/2213335607999200415141427

Price: $65

Abstract

Background: Colon cancer generally begins as a neoplastic growth of tissue, called polyps, originating from the inner lining of the colon wall. Most colon polyps are considered harmless but over the time, they can evolve into colon cancer, which, when diagnosed in later stages, is often fatal. Hence, time is of the essence in the early detection of polyps and the prevention of colon cancer.

Methods: To aid this endeavor, many computer-aided methods have been developed, which use a wide array of techniques to detect, localize and segment polyps from CT Colonography images. In this paper, a comprehensive state-of-the-art method is proposed and categorize this work broadly using the available classification techniques using Machine Learning and Deep Learning.

Conclusion: The performance of each of the proposed approach is analyzed with existing methods and also how they can be used to tackle the timely and accurate detection of colon polyps.

Keywords: CT Colonography (CTC), polyps, Deep Learning, Machine Learning (ML), Computer Aided Detection (CADe), CNN.

Graphical Abstract

[1]
Gu J, Poirson A. Computer-aided diagnosis (CAD) for colonoscopy. Medical Imaging: Computer-Aided Diagnosis 2007.
[2]
Doi K. Current status and future potential of computer-aided diagnosis in medical imaging. Br J Radiol 2005; 78(1): S3-19.
[http://dx.doi.org/10.1259/bjr/82933343]
[3]
Chan HP, Sahiner B, Helvie MA, et al. Improvement of radiologists’ characterization of mammographic masses by using computer-aided diagnosis: an ROC study. Radiology 1999; 212(3): 817-27.
[http://dx.doi.org/10.1148/radiology.212.3.r99au47817] [PMID: 10478252]
[4]
Li F, Aoyama M, Shiraishi J, et al. Radiologists’ performance for differentiating benign from malignant lung nodules on high-resolution CT using computer-estimated likelihood of malignancy. AJR Am J Roentgenol 2004; 183(5): 1209-15.
[http://dx.doi.org/10.2214/ajr.183.5.1831209] [PMID: 15505279]
[5]
Printz C. US Preventive Services Task Force issues final recommendations on breast cancer screening. Cancer 2016; 122(12): 1803-3.
[http://dx.doi.org/10.1002/cncr.30115]
[6]
Li F, Arimura H, Suzuki K, et al. Computer-aided detection of peripheral lung cancers missed at CT: ROC analyses without and with localization. Radiology 2005; 237(2): 684-90.
[http://dx.doi.org/10.1148/radiol.2372041555] [PMID: 16244277]
[7]
Suzuki K, Hori M, McFarland E, et al. Can CAD help improve the performance of radiologists in detection of difficult polyps in CT colonography? Proc RSNA Annual Meeting. 872.Chicago, IL. 2009; p.
[8]
Suzuki K. Machine learning in computer-aided diagnosis of the thorax and colon in ct: a survey ieice transactions on information and systems 2013; (4): 772-83.
[http://dx.doi.org/10.1587/transinf.E96.D.772]
[9]
Jerebko A K, Malley J D, Franaszek M, Summers R M. Support vector machines committee classification method for computer-aided polyp detection in CT colonography. Acad Radiol. 2005; 12: pp. (4)479-86.
[http://dx.doi.org/10.1016/j.acra.2004.04.024]
[10]
Polikar R. Ensemble learning ensemble. Mach Learn 2012; 1-34.
[http://dx.doi.org/10.1155/2015/471371]
[11]
Nguyen TT, Huang JZ, Nguyen TT. Unbiased feature selection in learning random forests for high-dimensional data. ScientificWorld Journal 2015; 2015471371.
[http://dx.doi.org/10.1155/2015/471371] [PMID: 25879059]
[12]
Yao J, Li J, Summers RM. Employing topographical height map in colonic polyp measurement and false positive reduction. Pattern Recognit 2009; 42(6): 1029-40.
[http://dx.doi.org/10.1016/j.patcog.2008.09.034] [PMID: 19578483]
[13]
Zhu H, Liang Z, Pickhardt PJ, et al. Increasing computer-aided detection specificity by projection features for CT colonography. Med Phys 2010; 37(4): 1468-81.
[http://dx.doi.org/10.1118/1.3302833] [PMID: 20443468]
[14]
Song B, Zhang G, Zhu W, Liang Z. ROC operating point selection for classification of imbalanced data with application to computer-aided polyp detection in CT colonography. Int J CARS 2014; 9(1): 79-89.
[http://dx.doi.org/10.1007/s11548-013-0913-8] [PMID: 23797823]
[15]
Ma M, Wang H, Song B, et al. Multiple kernel learning with adaptive kernel method for computer-aided detection of colonic polyps. IEEE Nuclear Science Symposium and Medical Imaging Conference.
[16]
Xu JW, Suzuki K. Massive-training support vector regression and Gaussian process for false-positive reduction in computer-aided detection of polyps in CT colonography. Med Phys 2011; 38(4): 1888-902.
[http://dx.doi.org/10.1118/1.3562898] [PMID: 21626922]
[17]
Cheng D, Ting W, Chen Y, et al. Colorectal Polyps Detection Using Texture Features and Support Vector Machine Advances in Mass Data Analysis of Images and Signals in Medicine. Biotechnology, Chemistry and Food Industry Lecture Notes in Computer Science 2008; pp. 62-72.
[18]
Lee SH, Näppi JJ, Yoshida H. Comparative Performance of State-of-the-Art Classifiers in Computer-Aided Detection for CT Colonography. Lecture Notes in Computer Science Abdominal Imaging. computational and clinical applications 2012; pp. 78-87.
[http://dx.doi.org/10.1007/978-3-642-33612-6_9]
[19]
Zheng Y, Yang X, Beddoe G. Reduction of false positives in polyp detection using Weighted Support Vector Machines. 2007 29th Annual International conference of the IEEE Engineering in Medicine and Biology Society.
[http://dx.doi.org/10.1109/IEMBS.2007.4353322]
[20]
Wang S, Yao J, Summers RM. Improved classifier for computer-aided polyp detection in CT colonography by nonlinear dimensionality reduction. Med Phys 2008; 35(4): 1377-86.
[http://dx.doi.org/10.1118/1.2870218] [PMID: 18491532]
[21]
Stephen P, Jaganathan S. Linear regression for pattern recognition. International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE).
[http://dx.doi.org/10.1109/ICGCCEE.2014.6921393]
[22]
Ravesteijn VF, van Wijk C, Vos FM, et al. Computer-aided detection of polyps in CT colonography using logistic regression. IEEE Trans Med Imaging 2010; 29(1): 120-31.
[http://dx.doi.org/10.1109/TMI.2009.2028576] [PMID: 19666332]
[23]
Näppi J, Yoshida H. Automated detection of polyps with CT colonography: evaluation of volumetric features for reduction of false-positive findings. Acad Radiol 2002; 9(4): 386-97.
[24]
Kaladhar D. SVGK. The Elements of Statistical Learning in Colon Cancer Datasets: Data Mining, Inference and Prediction. Algorithms Research 2013; 2(1): 8-17.
[http://dx.doi.org/10.5923/j.algorithms.20130201.02]
[25]
Romero E, González F. FFrom Biomedical Image Analysis to Biomedical Image Understanding Using Machine Learning. 2010.
[http://dx.doi.org/10.4018/978-1-60566-956-4.ch001]
[26]
Yamashita R, Nishio M, Do R K, Togashi K. An overview and application in radiology. Insights into Imaging, Convolution neural networks 2018; 9(4): 611-29.
[http://dx.doi.org/10.1007/s13244-018-0639-9]
[27]
Suzuki K, Zhang J, Xu J. Massive-training artificial neural network coupled with Laplacian-eigenfunction-based dimensionality reduction for computer-aided detection of polyps in CT colonography. IEEE Trans Med Imaging 2010; 29(11): 1907-17.
[http://dx.doi.org/10.1109/TMI.2010.2053213] [PMID: 20570766]
[28]
Chen Y, Ren Y, Fu L, Xiong J, et al. A 3D Convolutional Neural Network Framework for Polyp Candidates Detection on the Limited Dataset of CT Colonography 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[29]
Godkhindi AM, Gowda RM. Automated detection of polyps in CT colonography images using deep learning algorithms in colon cancer diagnosis. International Conference on Energy, Communication, Data Analytics and Soft Computing.
[http://dx.doi.org/10.1109/ICECDS.2017.8389744]
[30]
Yoshida H, Näppi J. Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps. IEEE Trans Med Imaging 2001; 20(12): 1261-74.
[http://dx.doi.org/10.1109/42.974921] [PMID: 11811826]
[31]
Acar B, Beaulieu CF, Göktürk SB, et al. Edge displacement field-based classification for improved detection of polyps in CT colonography. IEEE Trans Med Imaging 2002; 21(12): 1461-7.
[http://dx.doi.org/10.1109/TMI.2002.806405] [PMID: 12588030]
[32]
Li J, Van Uitert R, Yao J, et al. Wavelet method for CT colonography computer-aided polyp detection. Med Phys 2008; 35(8): 3527-38.
[http://dx.doi.org/10.1118/1.2938517] [PMID: 18777913]
[33]
Jerebko AK, Summers RM, Malley JD, Franaszek M, Johnson CD. Computer-assisted detection of colonic polyps with CT colonography using neural networks and binary classification trees. Med Phys 2003; 30(1): 52-60.
[http://dx.doi.org/10.1118/1.1528178] [PMID: 12557979]
[34]
Roth HR, Lu L, Liu J, et al. Improving Computer-Aided Detection Using newline Convolutional Neural Networks and Random View Aggregation. IEEE Trans Med Imaging 2016; 35(5): 1170-81.
[http://dx.doi.org/10.1109/tmi.2015.2482920]
[35]
Tachibana R, Näppi J J, et al. H. Deep Learning Electronic Cleansing for Single- and Dual-Energy CT ColonographyRadioGraphics 2018; 38(7): 2034-50.
[http://dx.doi.org/10.1148/rg.2018170173]
[36]
Ma M, Wang H, Song B, et al. Random forest based computer-aided detection of polyps in CT colonography. IEEE Nuclear Science Symposium and Medical Imaging Conference.
[37]
Hu Y, Liang Z, Song B, et al. Texture Feature Extraction and Analysis for Polyp Differentiation via Computed Tomography Colonography. IEEE Trans Med Imaging 2016; 35(6): 1522-31.
[http://dx.doi.org/10.1109/TMI.2016.2518958] [PMID: 26800530]
[38]
Kiss G, Cleynenbreugel J V, Thomeer M, Suetens P, Marchal G. Computer-aided diagnosis in virtual colonography via combination of surface normal and sphere fitting methods European Radiology In: 2001; 12: pp. (1)77-81.
[http://dx.doi.org/10.1007/s003300101040]
[39]
Gokturk S, Tomasi C, Acar B, et al. A statistical 3-D pattern processing method for computer-aided detection of polyps in CT colonography. IEEE Trans Med Imaging 2001; 20(12): 1251-60.
[http://dx.doi.org/10.1109/42.974920]
[40]
Sato Y, Westin C, Bhalerao A, et al. Tissue classification based on 3D local intensity structures for volume rendering. IEEE Trans Vis Comput Graph 2000; 6(2): 160-80.
[http://dx.doi.org/10.1109/2945.856997]
[41]
Vishwanathan S, Murty MN. SSVM: A simple SVM algorithm. Proceedings of the 2002 International Joint Conference on Neural Networks.
[http://dx.doi.org/10.1109/ijcnn.2002.1007516]

Rights & Permissions Print Cite
Article Metrics
25
© 2024 Bentham Science Publishers | Privacy Policy