CT colonography: influence of 3D viewing and polyp candidate features on interpretation with computer-aided detection

Five levels of PACS modularity: integrating 3D and other advanced visualization tools

The current array of PACS products and 3D visualization tools presents a wide range of options for applying advanced visualization methods in clinical radiology. The emergence of server-based rendering techniques creates new opportunities for raising the level of clinical image review. However, best-of-breed implementations of core PACS technology, volumetric image navigation, and application-specific 3D packages will, in general, be supplied by different vendors. Integration issues should be carefully considered before deploying such systems. This work presents a classification scheme describing five tiers of PACS modularity and integration with advanced visualization tools, with the goals of characterizing current options for such integration, providing an approach for evaluating such systems, and discussing possible future architectures. These five levels of increasing PACS modularity begin with what was until recently the dominant model for integrating advanced visualization into the clinical radiologist's workflow, consisting of a dedicated stand-alone post-processing workstation in the reading room. Introduction of context-sharing, thin clients using server-based rendering, archive integration, and user-level application hosting at successive levels of the hierarchy lead to a modularized imaging architecture, which promotes user interface integration, resource efficiency, system performance, supportability, and flexibility. These technical factors and system metrics are discussed in the context of the proposed five-level classification scheme.

Machine Learning for Automated Polyp Detection in Computed Tomography Colonography

This chapter presents a comprehensive scheme for automated detection of colorectal polyps in computed tomography colonography (CTC) with particular emphasis on robust learning algorithms that differentiate polyps from non-polyp shapes. The authors’ automated CTC scheme introduces two orientation independent features which encode the shape characteristics that aid in classification of polyps and non-polyps with high accuracy, low false positive rate, and low computations making the scheme suitable for colorectal cancer screening initiatives. Experiments using state-of-the-art machine learning algorithms viz., lazy learning, support vector machines, and naïve Bayes classifiers reveal the robustness of the two features in detecting polyps at 100% sensitivity for polyps with diameter greater than 10 mm while attaining total low false positive rates, respectively, of 3.05, 3.47 and 0.71 per CTC dataset at specificities above 99% when tested on 58 CTC datasets. The results were validated using colonoscopy reports provided by expert radiologists.

Effect of computer-aided detection for CT colonography in a multireader, multicase trial

PURPOSE

To assess the effect of using computer-aided detection (CAD) in second-read mode on readers' accuracy in interpreting computed tomographic (CT) colonographic images.

MATERIALS AND METHODS

The contributing institutions performed the examinations under approval of their local institutional review board, with waiver of informed consent, for this HIPAA-compliant study. A cohort of 100 colonoscopy-proved cases was used: In 52 patients with findings positive for polyps, 74 polyps of 6 mm or larger were observed in 65 colonic segments; in 48 patients with findings negative for polyps, no polyps were found. Nineteen blinded readers interpreted each case at two different times, with and without the assistance of a commercial CAD system. The effect of CAD was assessed in segment-level and patient-level receiver operating characteristic (ROC) curve analyses.

RESULTS

Thirteen (68%) of 19 readers demonstrated higher accuracy with CAD, as measured with the segment-level area under the ROC curve (AUC). The readers' average segment-level AUC with CAD (0.758) was significantly greater (P = .015) than the average AUC in the unassisted read (0.737). Readers' per-segment, per-patient, and per-polyp sensitivity for all polyps of 6 mm or larger was higher (P < .011, .007, .005, respectively) for readings with CAD compared with unassisted readings (0.517 versus 0.465, 0.521 versus 0.466, and 0.477 versus 0.422, respectively). Sensitivity for patients with at least one large polyp of 10 mm or larger was also higher (P < .047) with CAD than without (0.777 versus 0.743). Average reader sensitivity also improved with CAD by more than 0.08 for small adenomas. Use of CAD reduced specificity of readers by 0.025 (P = .05).

CONCLUSION

Use of CAD resulted in a significant improvement in overall reader performance. CAD improves reader sensitivity when measured per segment, per patient, and per polyp for small polyps and adenomas and also reduces specificity by a small amount.

Improving the accuracy of CTC interpretation: computer-aided detection

Computer-aided polyp detection aims to improve the accuracy of the colonography interpretation. The computer searches the colonic wall to look for polyplike protrusions and presents a list of suspicious areas to a physician for further analysis. Computer-aided polyp detection has developed rapidly in the past decade in the laboratory setting and has sensitivities comparable with those of experts. Computer-aided polyp detection tends to help inexperienced readers more than experienced ones and may also lead to small reductions in specificity. In its currently proposed use as an adjunct to standard image interpretation, computer-aided polyp detection serves as a spellchecker rather than an efficiency enhancer.

Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM

The roles of physicists in medical imaging have expanded over the years, from the study of imaging systems (sources and detectors) and dose to the assessment of image quality and perception, the development of image processing techniques, and the development of image analysis methods to assist in detection and diagnosis. The latter is a natural extension of medical physicists' goals in developing imaging techniques to help physicians acquire diagnostic information and improve clinical decisions. Studies indicate that radiologists do not detect all abnormalities on images that are visible on retrospective review, and they do not always correctly characterize abnormalities that are found. Since the 1950s, the potential use of computers had been considered for analysis of radiographic abnormalities. In the mid-1980s, however, medical physicists and radiologists began major research efforts for computer-aided detection or computer-aided diagnosis (CAD), that is, using the computer output as an aid to radiologists-as opposed to a completely automatic computer interpretation-focusing initially on methods for the detection of lesions on chest radiographs and mammograms. Since then, extensive investigations of computerized image analysis for detection or diagnosis of abnormalities in a variety of 2D and 3D medical images have been conducted. The growth of CAD over the past 20 years has been tremendous-from the early days of time-consuming film digitization and CPU-intensive computations on a limited number of cases to its current status in which developed CAD approaches are evaluated rigorously on large clinically relevant databases. CAD research by medical physicists includes many aspects-collecting relevant normal and pathological cases; developing computer algorithms appropriate for the medical interpretation task including those for segmentation, feature extraction, and classifier design; developing methodology for assessing CAD performance; validating the algorithms using appropriate cases to measure performance and robustness; conducting observer studies with which to evaluate radiologists in the diagnostic task without and with the use of the computer aid; and ultimately assessing performance with a clinical trial. Medical physicists also have an important role in quantitative imaging, by validating the quantitative integrity of scanners and developing imaging techniques, and image analysis tools that extract quantitative data in a more accurate and automated fashion. As imaging systems become more complex and the need for better quantitative information from images grows, the future includes the combined research efforts from physicists working in CAD with those working on quantitative imaging systems to readily yield information on morphology, function, molecular structure, and more-from animal imaging research to clinical patient care. A historical review of CAD and a discussion of challenges for the future are presented here, along with the extension to quantitative image analysis.

Conspicuity of colorectal polyps at CT colonography: visual assessment, CAD performance, and the important role of polyp height

RATIONALE AND OBJECTIVES

The factors that influence the conspicuity of polyps on computed tomographic (CT) colonography (CTC) are poorly understood. The aim of this study is to compare radiologists' visual assessment of polyp conspicuity to quantitative image features and show the relationship between visual conspicuity and the detection of colonic polyps by computer-aided detection (CAD) on CTC.

METHODS

One polyp (size range 6-10 mm) was selected from the CTC examination of each of 29 patients from a larger cohort. All patients underwent oral contrast-enhanced CTC with same-day optical colonoscopy with segmental unblinding. The polyps were analyzed by a previously validated CAD system and placed into one of two groups (detected [n = 12] or not detected [n = 17] by CAD). The study population was intentionally enriched with polyps that were not detected by the CAD system. Four board-certified radiologists, blinded to the CAD results, reviewed two- and three-dimensional CTC images of the polyps and scored the conspicuity of the polyps using a 4-point scale (0 = least conspicuous, 3 = most conspicuous). Polyp height and width were measured by a trained observer. A t-test (two-tailed, unpaired equal variance) was done to determine statistical significance. Intra- and interobserver variabilities of the conspicuity scores were assessed using the weighted kappa test. Regression analysis was used to investigate the relationship of conspicuity to polyp height and width.

RESULTS

A statistically significant difference was found between the average conspicuity scores for polyps that were detected by CAD compared to those that were not (2.3 +/- 0.6 vs. 1.4 +/- 0.8) (P = .004). There was moderate intraobserver agreement of the conspicuity scores (weighted kappa 0.57 +/- 0.09). Interobserver agreement was fair (average weighted kappa for six pair-wise comparisons, 0.38 +/- 0.15). Conspicuity was correlated with manual measurement of polyp height (r(2) = 0.38-0.56, P < .001).

CONCLUSIONS

This CAD system tends to detect 6-10 mm polyps that are more visually conspicuous. Polyp height is a major determinant of visual conspicuity. The generalizability of these findings to other CAD systems is currently unknown. Nevertheless, CAD developers may need to specifically target flatter and less conspicuous polyps for CAD to better assist the radiologist to find polyps in this clinically important size category.

Virtual dissection CT colonography: evaluation of learning curves and reading times with and without computer-aided detection

PURPOSE

To prospectively evaluate the learning curves and reading times of inexperienced readers who used the virtual dissection reading method for retrospective computed tomographic (CT) colonography data sets, with and without concurrent computer-aided detection (CAD).

MATERIALS AND METHODS

An Institutional Review Board approved this study; informed consent was waived. Four radiologists without experience in CT colonography evaluated 100 optical colonoscopy-proved data sets of 100 patients (49 men, 51 women; mean age, 59 years +/- 13 [standard deviation]; range, 21-85 years) by using the virtual dissection reading method. Two readers used concurrent CAD. Data sets were read during five consecutive 1-day sessions (20 data sets per session). Polyp detection and false-positive rates, receiver operating characteristics (ROCs), and reading times were calculated for individual, CAD group, and non-CAD group readings. Diagnostic values were compared by calculating the 95% confidence intervals (CIs) around the relative risk. Areas under ROC curves (AUCs) (Hanley and McNeil for paired analysis and z statistics for unpaired analysis) and reading times (Wilcoxon signed rank test) were compared across the sessions, within each session and for the whole study.

RESULTS

The range of detection rates was 79 of 111 (.71 [95% CI: .61, .79]) to 91 of 111 (.82 [95% CI: .73, .88]). The range of false-positive rates was 17 of 111 (.15 [95% CI: .09, .23]) to 22 of 111 (.20 [95% CI: .12, .28]). All readers' AUCs rose from session 1 to session 4; this rise was significant (P < .05) for the non-CAD group. Only during session 1 was the CAD group AUC (.83) higher than the non-CAD group AUC (.54) (P < .05). Comparison of CAD and non-CAD reading times showed no significant difference for the whole study or during each session (P > .05).

CONCLUSION

The virtual dissection reading technique allows short learning curves, which may be improved by the concurrent use of CAD, without significant effect on average reading time.

Computer-aided detection for CT colonography: update 2007

Computed tomographic colonography (CTC) is an emerging technique for polyp detection in the colon. However, lesion detection can be challenging due to insufficient patient preparation, chosen CT technique or reader imperfection. The primary goal of computer-aided detection (CAD) for CTC is locating possible polyps, and presenting the reader with these polyp candidates. Other goals are sensitivity improvement and reduction of reading time and inter-observer variability. The multistep CAD procedure typically consists of segmentation of the colonic wall (e.g. region growing); selection of intermediate polyp candidates (curvature analysis, sphere fitting, normal analysis, slope density function ...); classification of final candidates for detection and listing suspicious polyps (location, size and volume). Remaining task for the radiologist is the validation or rejection of the polyp candidates. State-of-the-art CAD systems should require minimal or even no user interaction for the extraction of the colonic wall, offer a computation time less than 10-20 min and high sensitivity and specificity for different polyp sizes and shapes, with a low number of false positives. These systems have the potential to increase radiologist's performance and to decrease inter-reader variability. Besides CAD key techniques we also discuss new developments in CAD and describe recent applications facilitating CTC.

CT colonography with computer-aided detection as a second reader: observer performance study

PURPOSE

To evaluate the effect of computer-aided detection (CAD) as second reader on radiologists' diagnostic performance in interpreting computed tomographic (CT) colonographic examinations by using a primary two-dimensional (2D) approach, with segmental, unblinded optical colonoscopy as the reference standard.

MATERIALS AND METHODS

This HIPAA-compliant study was IRB-approved with written informed consent. Four board-certified radiologists analyzed 60 CT examinations with a commercially available review system. Two-dimensional transverse views were used for initial polyp detection, while three-dimensional (3D) endoluminal and 2D multiplanar views were available for problem solving. After initial review without CAD, the reader was shown CAD-identified polyp candidates. The readers were then allowed to add to or modify their original diagnoses. Polyp location, CT Colonography Reporting and Data System categorization, and reader confidence as to the likelihood of a candidate being a polyp were recorded before and after CAD reading. The area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity were estimated for CT examinations with and without CAD readings by using multireader multicase analysis.

RESULTS

Use of CAD led to nonsignificant average reader AUC increases of 0.03, 0.03, and 0.04 for patients with adenomatous polyps 6 mm or larger, 6-9 mm, and 10 mm or larger, respectively (P > or = .25); likewise, CAD increased average reader sensitivity by 0.15, 0.16, and 0.14 for those respective groups, with a corresponding decrease in specificity of 0.14. These changes achieved significance for the 6 mm or larger group (P < .01), 6-9 mm group (P < .02), and for specificity (P < .01), but not for the 10 mm or larger group (P > .16). The average reading time was 5.1 minutes +/- 3.4 (standard deviation) without CAD. CAD added an average of 3.1 minutes +/- 4.3 (62%) to each reading (supine and prone positions combined); average total reading time, 8.2 minutes +/- 5.8.

CONCLUSION

Use of CAD led to a significant increase in sensitivity for detecting polyps in the 6 mm or larger and 6-9 mm groups at the expense of a similar significant reduction in specificity.

A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer

Background

In the development of tissue classification methods, classifiers rely on significant differences between texture features extracted from normal and abnormal regions. Yet, significant differences can arise due to variations in the image acquisition method. For endoscopic imaging of the endometrium, we propose a standardized image acquisition protocol to eliminate significant statistical differences due to variations in: (i) the distance from the tissue (panoramic vs close up), (ii) difference in viewing angles and (iii) color correction.

Methods

We investigate texture feature variability for a variety of targets encountered in clinical endoscopy. All images were captured at clinically optimum illumination and focus using 720 × 576 pixels and 24 bits color for: (i) a variety of testing targets from a color palette with a known color distribution, (ii) different viewing angles, (iv) two different distances from a calf endometrial and from a chicken cavity. Also, human images from the endometrium were captured and analysed. For texture feature analysis, three different sets were considered: (i) Statistical Features (SF), (ii) Spatial Gray Level Dependence Matrices (SGLDM), and (iii) Gray Level Difference Statistics (GLDS). All images were gamma corrected and the extracted texture feature values were compared against the texture feature values extracted from the uncorrected images. Statistical tests were applied to compare images from different viewing conditions so as to determine any significant differences.

Results

For the proposed acquisition procedure, results indicate that there is no significant difference in texture features between the panoramic and close up views and between angles. For a calibrated target image, gamma correction provided an acquired image that was a significantly better approximation to the original target image. In turn, this implies that the texture features extracted from the corrected images provided for better approximations to the original images. Within the proposed protocol, for human ROIs, we have found that there is a large number of texture features that showed significant differences between normal and abnormal endometrium.

Conclusion

This study provides a standardized protocol for avoiding any significant texture feature differences that may arise due to variability in the acquisition procedure or the lack of color correction. After applying the protocol, we have found that significant differences in texture features will only be due to the fact that the features were extracted from different types of tissue (normal vs abnormal).

Computer-aided detection (CAD) using 360 degree virtual dissection: can CAD in a first reviewer paradigm be a reliable substitute for primary 2D or 3D search?

OBJECTIVE

The purpose of this study was to evaluate the feasibility of a new computer-aided detection (CAD) software program as a first reviewer for detecting colorectal polyps when applied to 360 degrees virtual dissection image display.

MATERIALS AND METHODS

Forty-one consecutive patients who underwent imaging without oral contrast material for stool tagging from a teaching file database constituted the patient population for this feasibility study. Using CT colonography equipped with CAD software, reviewers evaluated each possible polyp detected by the software using virtual dissection images combined with axial and 3D endoluminal views and compared the results with optical colonoscopy, the reference standard. Two experienced radiologists blinded to the reference standard findings interpreted the CAD detections to be true or false. The false detections were reviewed and categorized.

RESULTS

Sensitivities for polyps that were 6-9 mm were 78.3% (18/23) and 91.3% (21/23) for reviewers 1 and 2, respectively. For polyps > or = 1 cm, sensitivities were 94.9% (37/39) and 97.4% (38/39) for reviewers 1 and 2, respectively. Per-patient sensitivities for polyps > or = 6 and > or = 10 mm were 94.4% (34/36) and 95.1% (39/41) for reviewer 1 and 97.2% (35/36) and 97.6% (40/41) for reviewer 2, respectively. The average number of false detections per acquisition was 4.28. The average interpretation times were 4 minutes 26 seconds and 5 minutes 38 seconds for reviewers 1 and 2, respectively.

CONCLUSION

Colorectal polyp detection using CT colonography equipped with CAD and virtual dissection as a first reviewer is feasible. Detection rates are similar to colonoscopy. Interobserver variability is low and interpretation times are short. False-positive detections per patient are few in number.

Transparent rendering of intraluminal contrast for 3D polyp visualization at CT colonography

We developed a classifier that permits transparent rendering of both tagging material and air to facilitate interpretation of tagged computed tomographic (CT) colonography. With this technique, a reader can simultaneously appreciate polyps on endoluminal views both covered with tagging material and against air, along with unmodified 2-dimensional CT images. Evaluated with 49 polyps from 26 patients (data from public National Library of Medicine, Health Insurance Portability and Accountability Act compliant), 3 readers were able to determine the presence/absence of polyps in tagged locations with equivalent accuracy compared with polyps in air. This method offers an alternative way to visualize tagged CT colonography.

CAD in CT colonography without and with oral contrast agents: progress and challenges

Computed tomographic colonography (CTC), also known as virtual colonoscopy, is an emerging alternative technique for screening of colon cancers. CTC uses CT to provide a series of cross-sectional images of the colon for detection of polyps and masses. Fecal tagging is a means of labeling of residual feces by an oral contrast agent for improving the accuracy in the detection of polyps. Computer-aided diagnosis (CAD) for CTC automatically determines the locations of suspicious polyps and masses in CTC and presents them to radiologists, typically as a second opinion. Despite its relatively short history, CAD has become one of the mainstream techniques that could make CTC prime time for screening of colorectal cancer. Rapid technical developments have advanced CAD substantially during the last several years, and a fundamental scheme for the detection of polyps has been established, in which sophisticated 3D image processing, analysis, and display techniques play a pivotal role. The latest CAD systems indicate a clinically acceptable high sensitivity and a low false-positive rate, and observer studies have demonstrated the benefits of these systems in improving radiologists' detection performance. Some technical and clinical challenges, however, remain unresolved before CAD can become a truly useful tool for clinical practice. Also, new challenges are facing CAD as the methods for bowel preparation and image acquisition, such as tagging of fecal residue with oral contrast agents, and interpretation of CTC images evolve. This article reviews the current status and future challenges in CAD for CTC without and with fecal tagging.