In radiology, computer-aided detection (CADe), also called computer-aided diagnosis (CADx), are procedures in medicine that assist doctors in the interpretation of medical images. Imaging techniques in X-ray, MRI, and Ultrasound diagnostics yield a great deal of information, which the radiologist has to analyze and evaluate comprehensively in a short time. CAD systems help scan digital images, e.g. from computed tomography, for typical appearances and to highlight conspicuous sections, such as possible diseases.
CAD is an interdisciplinary technology combining elements of artificial intelligence and computer vision with radiological image processing. A typical application is the detection of a tumor. For instance, some hospitals use CAD to support preventive medical check-ups in mammography (diagnosis of breast cancer), the detection of polyps in the colon, and lung cancer.
Computer-aided detection (CADe) systems are usually confined to marking conspicuous structures and sections. Computer-aided diagnosis (CADx) systems evaluate the conspicuous structures. For example, in mammography CAD highlights micro calcification clusters and hyperdense structures in the soft tissue. This allows the radiologist to draw conclusions about the condition of the pathology. Another application is CADq, which quantifies, e.g., the size of a tumor or the tumor's behavior in contrast medium uptake. Computer-aided simple triage (CAST) is another type of CAD, which performs a fully automatic initial interpretation and triage of studies into some meaningful categories (e.g. negative and positive). CAST is particularly applicable in emergency diagnostic imaging, where a prompt diagnosis of critical, life-threatening condition is required.
Although CAD has been used in clinical environments for over 40 years, CAD cannot and may not substitute the doctor, but rather plays a supporting role. The doctor (generally a radiologist) is always responsible for the final interpretation of a medical image.
- 1 Computer-aided diagnosis topics
- 2 Applications
- 3 References
Computer-aided diagnosis topics
CAD is fundamentally based on highly complex pattern recognition. X-ray images are scanned for suspicious structures. Normally a few thousand images are required to optimize the algorithm. Digital image data are copied to a CAD server in a DICOM-format and are prepared and analyzed in several steps.
1. Preprocessing for
- Reduction of artifacts (bugs in images)
- Image noise reduction
- Leveling (harmonization) of image quality for clearing the image's different basic conditions e.g. different exposure parameter.
2. Segmentation for
- Differentiation of different structures in the image, e.g. heart, lung, ribcage, possible round lesions
- Matching with anatomic databank
3. Structure/ROI (Region of Interest) Analyze Every detected region is analyzed individually for special characteristics:
- Form, size and location
- Reference to close-by structures / ROIs
- Average greylevel value analyze within a ROI
- Proportion of greylevels to border of the structure inside the ROI
4. Evaluation / classification After the structure is analyzed, every ROI is evaluated individually (scoring) for the probability of a TP. Therefore, the procedures are:
- Nearest-Neighbor Rule
- Minimum distance classifier
- Cascade Classifier
- Bayesian Classifier
- Artificial Neural Network
- Radial basis function network (RBF)
If the detected structures have reached a certain threshold level, they are highlighted in the image for the radiologist. Depending on the CAD system these markings can be permanently or temporary saved. The latter's advantage is that only the markings which are approved by the radiologist are saved. False hits should not be saved, because an examination at a later date becomes more difficult then.
Sensitivity and specificity
CAD systems seek to highlight suspicious structures. Today's CAD systems cannot detect 100% of pathological changes. The hit rate (sensitivity) can be up to 90% depending on system and application. A correct hit is termed a True Positive (TP), while the incorrect marking of healthy sections constitutes a False Positive (FP). The less FPs indicated, the higher the specificity is. A low specificity reduces the acceptance of the CAD system because the user has to identify all of these wrong hits. The FP-rate in lung overview examinations (CAD Chest) could be reduced to 2 per examination. In other segments (e.g. CT lung examinations) the FP-rate could be 25 or more. In CAST systems the FP rate must be extremely low (less than 1 per examination) to allow a meaningful study triage.
Absolute detection rate
The absolute detection rate of the radiologist is an alternative metric to sensitivity and specificity. Overall, results of clinical trials about sensitivity, specificity, and the absolute detection rate can vary markedly. Each study result depends on its basic conditions and has to be evaluated on those terms. The following facts have a strong influence:
- Retrospective or prospective design
- Quality of the used images
- Condition of the x-ray examination
- Radiologist's experience and education
- Type of lesion
- Size of the considered lesion
CAD is used in the diagnosis of Pathological Brain Detection (PBD), breast cancer, lung cancer, colon cancer, prostate cancer, bone metastases, coronary artery disease, congenital heart defect, and Alzheimer's disease.
Chaplot et al. was the first to use Discrete Wavelet Transform (DWT) coefficients to detect pathological brains. Maitra and Chatterjee employed the Slantlet transform, which is an improved version of DWT. Their feature vector of each image is created by considering the magnitudes of Slantlet transform outputs corresponding to six spatial positions chosen according to a specific logic.
In 2010, Wang and Wu presented a forward neural network (FNN) based method to classify a given MR brain image as normal or abnormal. The parameters of FNN were optimized via adaptive chaotic particle swarm optimization (ACPSO). Results over 160 images showed that the classification accuracy was 98.75%.
In 2011, Wu and Wang proposed using DWT for feature extraction, PCA for feature reduction, and FNN with scaled chaotic artificial bee colony (SCABC) as classifier.
In 2013, Saritha et al. were the first to apply wavelet entropy (WE) to detect pathological brains. Saritha also suggested to use spider-web plots. Later, Zhang et al. proved removing spider-web plots did not influence the performance. Genetic pattern search method was applied to identify abnormal brain from normal controls. Its classification accuracy was reported as 95.188%. Das et al. proposed to use Ripplet transform. Zhang et al. proposed to use particle swarm optimization (PSO). Kalbkhani et al. suggested to use GARCH model.
In 2014, El-Dahshan et al. suggested to use pulse coupled neural network.
In 2015, Zhou et al. suggested to apply naive Bayes classifier to detect pathological brains.
CAD is used in screening mammography (X-ray examination of the female breast). Screening mammography is used for the early detection of breast cancer. CAD is especially established in US and the Netherlands and is used in addition to human evaluation, usually by a radiologist. The first CAD system for mammography was developed in a research project at the University of Chicago. Today it is commercially offered by iCAD and Hologic. There are currently some non-commercial projects being developed, such as Ashita Project, a gradient-based screening software by Alan Hshieh, as well. However, while achieving high sensitivities, CAD systems tend to have very low specificity and the benefits of using CAD remain uncertain. Some studies suggest a positive impact on mammography screening programs, but others show no improvement. A 2008 systematic review on computer-aided detection in screening mammography concluded that CAD does not have a significant effect on cancer detection rate, but does undesirably increase recall rate (i.e. the rate of false positives). However, it noted considerable heterogeneity in the impact on recall rate across studies.
Procedures to evaluate mammography based on magnetic resonance imaging exist too.
Lung cancer (bronchial carcinoma)
In the diagnosis of lung cancer, computed tomography with special three-dimensional CAD systems are established and considered as gold standard. At this a volumetric dataset with up to 3,000 single images is prepared and analyzed. Round lesions (lung cancer, metastases and benign changes) from 1 mm are detectable. Today all well-known vendors of medical systems offer corresponding solutions.
Early detection of lung cancer is valuable. The 5-year-survival-rate of lung cancer has stagnated in the last 30 years and is now at approximately just 15%. Lung cancer takes more victims than breast cancer, prostate cancer and colon cancer together. This is due to the asymptomatic growth of this cancer. In the majority of cases it is too late for a successful therapy if the patient develops first symptoms (e.g. chronic croakiness or hemoptysis). But if the lung cancer is detected early (mostly by chance), there is a survival rate at 47% according to the American Cancer Society. At the same time the standard x-ray-examination of the lung is the most frequently x-ray examination with a 50% share. Indeed, the random detection of lung cancer in the early stage (stage 1) in the x-ray image is difficult. It is a fact that round lesions vary from 5–10 mm are easily overlooked. The routine application of CAD Chest Systems may help to detect small changes without initial suspicion. Philips was the first vendor to present a CAD for early detection of round lung lesions on x-ray images.
CAD is available for detection of colorectal polyps in the colon. Polyps are small growths that arise from the inner lining of the colon. CAD detects the polyps by identifying their characteristic "bump-like" shape. To avoid excessive false positives, CAD ignores the normal colon wall, including the haustral folds. In early clinical trials, CAD helped radiologists find more polyps in the colon than they found prior to using CAD.
Coronary artery disease
CAD is available for the automatic detection of significant (causing more than 50% stenosis) coronary artery disease in coronary CT angiography (CCTA) studies. A low false positives rate (60-70% specificity per patient) allows using it as a computer-aided simple triage (CAST) tool distinguishing between positive and negative studies and yielding a preliminary report. This, for example, can be used for chest pain patients' triage in an emergency setting.
Congenital heart defect
Early detection of pathology can be the difference between life and death. CADe can be done by auscultation with a digital stethoscope and specialized software, also known as Computer-aided auscultation. Murmurs, irregular heart sounds, caused by blood flowing through a defective heart, can be detected with high sensitivity and specificity. Computer-aided auscultation is sensitive to external noise and bodily sounds and requires an almost silent environment to function accurately.
CADs can be used to identify subjects with Alzheimer's and mild cognitive impairment from normal elder controls.
In 2014, Padma et al. used combined wavelet statistical texture features to segment and classify AD benign and malignant tumor slices. Zhang et al. found kernel support vector machine decision tree had 80% classification accuracy, with an average computation time of 0.022s for each image classification.
Eigenbran is a novel brain feature that can help to detect AD. The results showed polynomial kernel SVM achieved accuracy of 92.36±0.94, sensitivity of 83.48±3.27, specificity of 94.90±1.09, and precision of 82.28±2.78. The polynomial KSVM performs better than linear SVM and RBF kernel SVM.
CADx is available for nuclear medicine images. Commercial CADx systems for the diagnosis of bone metastases in whole-body bone scans and coronary artery disease in myocardial perfusion images exist.
- Wollenweber T., Janke B., Teichmann A., Freund M. (2007). "Korrelation zwischen histologischem Befund und einem Computer-assistierten Detektionssystem (CAD) für die Mammografie.". Geburtsh Frauenheilk 67: 135–141. doi:10.1055/s-2006-955983.
- Chaplot, S., L.M. Patnaik, and N.R. Jagannathan, Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomedical Signal Processing and Control, 2006. 1(1): p. 86-92.
- Maitra, M. and A. Chatterjee, A Slantlet transform based intelligent system for magnetic resonance brain image classification. Biomedical Signal Processing and Control, 2006. 1(4): p. 299-306.
- Wang, S.; Wu, W. (2010). "A Novel Method for Magnetic Resonance Brain Image Classification based on Adaptive Chaotic PSO". Progress in Electromagnetics Research 109: 325–343.
- Zhang, Yudong; Wu, L. (2011). "Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm". Progress in Electromagnetics Research 2011: 65–79.
- Saritha, M., K.P. Joseph, and A.T. Mathew, Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network. Pattern Recognition Letters, 2013. 34(16): p. 2151-2156.
- Zhang, Yudong; Dong, Zhengchao; Ji, Genlin (2015). "Effect of spider-web-plot in MR brain image classification". Pattern Recognition Letters 62: 14–16. doi:10.1016/j.patrec.2015.04.016.
- Zhang, Y.; Wang, S.; Ji, G.; Dong, Z. (2013). "Genetic Pattern Search and its Application to Brain Image Classification". Mathematical Problems in Engineering 2013: 1–8. doi:10.1155/2013/580876.
- Das S., Chowdhury M., Kundu M.K. (2013). "Brain MR Image Classification Using Multiscale Geometric Analysis of Ripplet". Progress in Electromagnetics Research-Pier 137: 1–17. doi:10.2528/pier13010105.
- Zhang, Y.; Wang, S. (2013). "An MR Brain Images Classifier System via Particle Swarm Optimization and Kernel Support Vector Machine". The Scientific World Journal 2013: 9. doi:10.1155/2013/130134.
- Kalbkhani H., Shayesteh M.G., Zali-Vargahan B. (2013). "Robust algorithm for brain magnetic resonance image (MRI) classification based on GARCH variances series". Biomedical Signal Processing and Control 8 (6): 909–919. doi:10.1016/j.bspc.2013.09.001.
- El-Dahshan E.S.A., Mohsen H.M., Revett K.; et al. (2014). "Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm". Expert Systems with Applications 41 (11): 5526–5545. doi:10.1016/j.eswa.2014.01.021.
- Zhou, Xing-Xing (2015). "Detection of Pathological Brain in MRI Scanning Based on Wavelet-Entropy and Naive Bayes Classifier". Bioinformatics and Biomedical Engineering: 201–209. doi:10.1007/978-3-319-16483-0_20.
- Gilbert FJ, Astley SM, Gillan MG, Agbaje OF, Wallis MG, James J, Boggis CR, Duffy SW (2008). "Single Reading with Computer-Aided Detection for Screening Mammography" (PDF). The New England Journal of Medicine 359: 1675–1684. doi:10.1056/NEJMoa0803545. PMID 18832239.
- Effect of Computer-Aided Detection on Independent Double Reading of Paired Screen-Film and Full-Field Digital Screening Mammograms Per Skaane, Ashwini Kshirsagar, Sandra Stapleton, Kari Young and Ronald A. Castellino
- Taylor P, Champness J, Given-Wilson R, Johnston K, Potts H (2005). "Impact of computer-aided detection prompts on the sensitivity and specificity of screening mammography". Health Technology Assessment 9 (6): 1–70. doi:10.3310/hta9060.
- Fenton JJ, Taplin SH, Carney PA, Abraham L, Sickles EA, D'Orsi C; et al. (2007). "Influence of computer-aided detection on performance of screening mammography" (PDF). N Engl J Med 356 (14): 1399–409. doi:10.1056/nejmoa066099.
- Taylor P, Potts HWW (2008). "Computer aids and human second reading as interventions in screening mammography: Two systematic reviews to compare effects on cancer detection and recall rate". European Journal of Cancer 44: 798–807. doi:10.1016/j.ejca.2008.02.016.
- Wu N, Gamsu G, Czum J, Held B, Thakur R, Nicola G (Mar 2006). "Detection of small pulmonary nodules using direct digital radiography and picture archiving and communication systems.". J Thorac Imaging 21 (1): 27–31. doi:10.1097/01.rti.0000203638.28511.9b. PMID 16538152.
- xLNA (x-Ray Lung Nodule Assessment)
- Petrick N, Haider M, Summers RM, Yeshwant SC, Brown L, Iuliano EM, Louie A, Choi JR, Pickhardt PJ (Jan 2008). "CT colonography with computer-aided detection as a second reader: observer performance study". Radiology 246 (1): 148–56. doi:10.1148/radiol.2453062161. PMID 18096536. Erratum in: Radiology. 2008 Aug;248(2):704. doi:10.1148/radiol.2453062161 PMID 18096536
- Halligan S, Altman DG, Mallett S, Taylor SA, Burling D, Roddie M, Honeyfield L, McQuillan J, Amin H, Dehmeshki J (Dec 2006). "Computed tomographic colonography: assessment of radiologist performance with and without computer-aided detection". Gastroenterology 131 (6): 1690–9. doi:10.1053/j.gastro.2006.09.051. PMID 17087934.
- Arnoldi E., Gebregziabher M., Schoepf U. J., Goldenberg R., Ramos-Duran L., Zwerner P. L., Nikolaou K., Reiser M. F., Costello P., Thilo C. (2010). "Automated computer-aided stenosis detection at coronary CT angiography: initial experience". European Radiology 20 (5): 1160–7. doi:10.1007/s00330-009-1644-7. PMID 19890640.
- Halpern E. J., Halpern D. J. (2011). "Diagnosis of coronary stenosis with CT angiography: comparison of automated computer diagnosis with expert readings". Academic Radiology 18 (3): 324–33. doi:10.1016/j.acra.2010.10.014. PMID 21215663.
- Kang KW, Chang HJ, Shim H, Kim YJ, Choi BW, Yang WI, Shim JY, Ha J, Chung N (2012). "Feasibility of an automatic computer-assisted algorithm for the detection of significant coronary artery disease in patients presenting with acute chest pain". Eur J Radiol 81 (4): e640–6. doi:10.1016/j.ejrad.2012.01.017. PMID 22304980.
- Padma, A. and R. Sukanesh, Segmentation and Classification of Brain CT Images Using Combined Wavelet Statistical Texture Features. Arabian Journal for Science and Engineering, 2014. 39(2): p. 767-776.
- Zhang, Yudong; Wang, Shuihua; Dong, Zhengchao (2014). "Classification of Alzheimer Disease Based on Structural Magnetic Resonance Imaging by Kernel Support Vector Machine Decision Tree". Progress in Electromagnetics Research - Pier 144: 185–191.
- Dong, Z.C. (2015). "Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning". Frontiers in Computational Neuroscience 66 (9): 1–15. doi:10.3389/fncom.2015.00066.
- EXINI Diagnostics