Wavelet method for CT colonography computer-aided polyp detectiona)

Abstract

Computed tomographic colonography (CTC) computer aided detection (CAD) is a new method to detect colon polyps. Colonic polyps are abnormal growths that may become cancerous. Detection and removal of colonic polyps, particularly larger ones, has been shown to reduce the incidence of colorectal cancer. While high sensitivities and low false positive rates are consistently achieved for the detection of polyps sized 1cm or larger, lower sensitivities and higher false positive rates occur when the goal of CAD is to identify “medium”-sized polyps, 6-9 mm in diameter. Such medium-sized polyps may be important for clinical patient management. We have developed a wavelet-based postprocessor to reduce false positives for this polyp size range. We applied the wavelet-based postprocessor to CTC CAD findings from 44 patients in whom 45 polyps with sizes of 6-9 mm were found at segmentally unblinded optical colonoscopy and visible on retrospective review of the CT colonography images. Prior to the application of the wavelet-based postprocessor, the CTC CAD system detected 33 of the polyps (sensitivity 73.33%) with 12.4 false positives per patient, a sensitivity comparable to that of expert radiologists. Four-fold cross-validation (CV) with 5000 bootstraps showed that the wavelet-based postprocessor could reduce the false positives by 56.61% (p < 0.001), to 5.38 per patient (95% confidence interval [4.41, 6.34]), without significant sensitivity degradation (32/45, 71.11%, 95% confidence interval [66.39%, 75.74], p = 0.1713). We conclude that this wavelet-based postprocessor can substantially reduce the false positive rate of our CTC CAD for this important polyp size range.

Computer-Aided Diagnosis of Skin Lesions Using Conventional Digital Photography: A Reliability and Feasibility Study

“…With the development of computer-aided image analysis technologies, physicians may obtain an objective “second opinion” from computer-aided detection (CAD) or computer-aided diagnosis (CADx) software to refine their diagnoses [12] . In clinical practice, CAD has been widely used in the field of lesion detection, such as breast lesion detection in mammography [13] – [15] , lung nodule detection on chest radiographs or CT scans [16] – [18] , and polyp detection in CT colonography [19] , [20] . CADx has also been applied to the analysis of nuclear medicine images [21] , [22] , skin lesions [23] – [26] , and histopathological images [27] – [29] .…”

Machine Learning in Computer-Aided Diagnosis of the Thorax and Colon in CT: A Survey

“…They removed FPs due to rectal tubes by using a single 3D MTANN [127] and multiple sources of FPs by developing and using a mixture of expert 3D MTANNs [20]. Li et al [128] developed a classification method based on an SVM classifier with wavelet-based features. Wang et al [129] improved the SVM performance by using nonlinear dimensionality reduction (i.e., a diffusion map and locally linear embedding).…”

Increasing computer-aided detection specificity by projection features for CT colonography

“…[6][7][8][9] Up to now, several research groups have developed various CADpolyp schemes. [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] Although some of these schemes reported acceptable detection sensitivities, challenges still remain to hinder them from being used in clinical practice. 27,28 One of the challenges is the high detection rate of false positives ͑FPs͒, which distracts the physicians' attention during their image interpretation and lowers their performance.…”