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

Local Axial Scale-Attention for Universal Lesion Detection

Universal lesion detection (ULD) in computed tomography (CT) images is an important and challenging prerequisite for computer-aided diagnosis (CAD) to find abnormal tissue, such as tumors of lymph nodes, liver tumors, and lymphadenopathy. The key challenge is that lesions have a tiny size and high similarity with non-lesions, which can easily lead to high false positives. Specifically , non-lesions are nearby normal anatomy that include the bowel, vasculature, and mesentery, which decrease the conspicuity of small lesions since they are often hard to differentiate. In this study, we present a novel scale-attention module that enhances feature discrimination between lesion and non-lesion regions by utilizing the domain knowledge of radiologists to reduce false positives effectively. Inspired by the domain knowledge that radiologists tend to divide each CT image into multiple areas, then detect lesions in these smaller areas separately, a local axial scale-attention (LASA) module is proposed to re-weight each pixel in a feature map by aggregating local features from multiple scales adaptively. In addition, to keep the same weight, a combination of axial pixels in the height- and width-axes is designed, attached with position embedding. The model can be used in CNNs easily and flexibly. We test our method on the DeepLesion dataset. The sensitivities at 0.5, 1, 2, 4, 8, and 16 false positives (FPs) per image and average sensitivity at [0.5, 1, 2, 4] are calculated to evaluate the accuracy. The sensitivities are 78.30%, 84.96%, 89.86%, 93.14%, 95.36%, and 95.54% at 0.5, 1, 2, 4, 8, and 16 FPs per image; the average sensitivity is 86.56%, outperforming the former methods. The proposed method enhances feature discrimination between lesion and non-lesion regions by adding LASA modules. These encouraging results illustrate the potential advantage of exploiting the domain knowledge for lesion detection.

Radiomics-Based Machine Learning to Predict Recurrence in Glioma Patients Using Magnetic Resonance Imaging

OBJECTIVE

Recurrence is a major factor in the poor prognosis of patients with glioma. The aim of this study was to predict glioma recurrence using machine learning based on radiomic features.

METHODS

We recruited 77 glioma patients, consisting of 57 newly diagnosed patients and 20 patients with recurrence. After extracting the radiomic features from T2-weighted images, the data set was randomly divided into training (58 patients) and testing (19 patients) cohorts. An automated machine learning method (the Tree-based Pipeline Optimization Tool) was applied to generate 10 independent recurrence prediction models. The final model was determined based on the area under the curve (AUC) and average specificity. Moreover, an independent validation set of 20 patients with glioma was used to verify the model performance.

RESULTS

Recurrence in glioma patients was successfully predicting by machine learning using radiomic features. Among the 10 recurrence prediction models, the best model achieved an accuracy of 0.81, an AUC value of 0.85, and a specificity of 0.69 in the testing cohort, but an accuracy of 0.75 and an AUC value of 0.87 in the independent validation set.

CONCLUSIONS

Our algorithm that is generated by machine learning exhibits promising power and may predict recurrence noninvasively, thereby offering potential value for the early development of interventions to delay or prevent recurrence in glioma patients.

New Approach for Risk Estimation Algorithms of BRCA1/2 Negativeness Detection with Modelling Supervised Machine Learning Techniques

BRCA1/2 gene testing is a difficult, expensive, and time-consuming test which requires excessive work load. The identification of the BRCA1/2 gene mutations is significantly important in the selection of treatment and the risk of secondary cancer. We aimed to develop an algorithm considering all the clinical, demographic, and genetic features of patients for identifying the BRCA1/2 negativity in the present study. An experimental dataset was created with the collection of the all clinical, demographic, and genetic features of breast cancer patients for 20 years. This dataset consisted of 125 features of 2070 high-risk breast cancer patients. All data were numeralized and normalized for detection of the BRCA1/2 negativity in the machine learning algorithm. The performance of the algorithm was identified by studying the machine learning model with the test data. k nearest neighbours (KNN) and decision tree (DT) accuracy rates of 9 features involving Dataset 2 were found to be the most effective. The removal of the unnecessary data in the dataset by reducing the number of features was shown to increase the accuracy rate of algorithm compared with the DT. BRCA1/2 negativity was identified without performing the BRCA1/2 gene test with 92.88% accuracy within minutes in high-risk breast cancer patients with this algorithm, and the test associated result waiting stress, time, and money loss were prevented. That algorithm is suggested be useful in fast performing of the treatment plans of patients and accurately in addition to speeding up the clinical practice.

Development of a Social Network for People Without a Diagnosis (RarePairs): Evaluation Study

Background

Diagnostic delay in rare disease (RD) is common, occasionally lasting up to more than 20 years. In attempting to reduce it, diagnostic support tools have been studied extensively. However, social platforms have not yet been used for systematic diagnostic support. This paper illustrates the development and prototypic application of a social network using scientifically developed questions to match individuals without a diagnosis.

Objective

The study aimed to outline, create, and evaluate a prototype tool (a social network platform named RarePairs), helping patients with undiagnosed RDs to find individuals with similar symptoms. The prototype includes a matching algorithm, bringing together individuals with similar disease burden in the lead-up to diagnosis.

Methods

We divided our project into 4 phases. In phase 1, we used known data and findings in the literature to understand and specify the context of use. In phase 2, we specified the user requirements. In phase 3, we designed a prototype based on the results of phases 1 and 2, as well as incorporating a state-of-the-art questionnaire with 53 items for recognizing an RD. Lastly, we evaluated this prototype with a data set of 973 questionnaires from individuals suffering from different RDs using 24 distance calculating methods.

Results

Based on a step-by-step construction process, the digital patient platform prototype, RarePairs, was developed. In order to match individuals with similar experiences, it uses answer patterns generated by a specifically designed questionnaire (Q53). A total of 973 questionnaires answered by patients with RDs were used to construct and test an artificial intelligence (AI) algorithm like the k-nearest neighbor search. With this, we found matches for every single one of the 973 records. The cross-validation of those matches showed that the algorithm outperforms random matching significantly. Statistically, for every data set the algorithm found at least one other record (match) with the same diagnosis.

Conclusions

Diagnostic delay is torturous for patients without a diagnosis. Shortening the delay is important for both doctors and patients. Diagnostic support using AI can be promoted differently. The prototype of the social media platform RarePairs might be a low-threshold patient platform, and proved suitable to match and connect different individuals with comparable symptoms. This exchange promoted through RarePairs might be used to speed up the diagnostic process. Further studies include its evaluation in a prospective setting and implementation of RarePairs as a mobile phone app.

CT Texture Analysis and Machine Learning Improve Post-ablation Prognostication in Patients with Adrenal Metastases: A Proof of Concept

INTRODUCTION

To assess the performance of pre-ablation computed tomography texture features of adrenal metastases to predict post-treatment local progression and survival in patients who underwent ablation using machine learning as a prediction tool.

MATERIALS AND METHODS

This is a pilot retrospective study of patients with adrenal metastases undergoing ablation. Clinical variables were collected. Thirty-two texture features were extracted from manually segmented adrenal tumors. A univariate cox proportional hazard model was used for prediction of local progression and survival. A linear support vector machine (SVM) learning technique was applied to the texture features and clinical variables, with leave-one-out cross-validation. Receiver operating characteristic analysis and the area under the curve (AUC) were used to assess performance between using clinical variables only versus clinical variables and texture features.

RESULTS

Twenty-one patients (61% male, age 64.1 ± 10.3 years) were included. Mean time to local progression was 29.8 months. Five texture features exhibited association with progression (p < 0.05). The SVM model based on clinical variables alone resulted in an AUC of 0.52, whereas the SVM model that included texture features resulted in an AUC 0.93 (p = 0.01). Mean overall survival was 35 months. Fourteen texture features were associated with survival in the univariate model (p < 0.05). While the trained SVM model based on clinical variables resulted in an AUC of 0.68, the SVM model that included texture features resulted in an AUC of 0.93 (p = 0.024).

DISCUSSION

Pre-ablation texture analysis and machine learning improve local tumor progression and survival prediction in patients with adrenal metastases who undergo ablation.

Deep learning-based image restoration algorithm for coronary CT angiography

OBJECTIVES

The purpose of this study was to compare the image quality of coronary computed tomography angiography (CTA) subjected to deep learning-based image restoration (DLR) method with images subjected to hybrid iterative reconstruction (IR).

METHODS

We enrolled 30 patients (22 men, 8 women) who underwent coronary CTA on a 320-slice CT scanner. The images were reconstructed with hybrid IR and with DLR. The image noise in the ascending aorta, left atrium, and septal wall of the ventricle was measured on all images and the contrast-to-noise ratio (CNR) in the proximal coronary arteries was calculated. We also generated CT attenuation profiles across the proximal coronary arteries and measured the width of the edge rise distance (ERD) and the edge rise slope (ERS). Two observers visually evaluated the overall image quality using a 4-point scale (1 = poor, 4 = excellent).

RESULTS

On DLR images, the mean image noise was lower than that on hybrid IR images (18.5 ± 2.8 HU vs. 23.0 ± 4.6 HU, p < 0.01) and the CNR was significantly higher (p < 0.01). The mean ERD was significantly shorter on DLR than on hybrid IR images, whereas the mean ERS was steeper on DLR than on hybrid IR images. The mean image quality score for hybrid IR and DLR images was 2.96 and 3.58, respectively (p < 0.01).

CONCLUSIONS

DLR reduces the image noise and improves the image quality at coronary CTA.

KEY POINTS

• Deep learning-based image restoration is a new technique that employs the deep convolutional neural network for image quality improvement. • Deep learning-based restoration reduces the image noise and improves image quality at coronary CT angiography. • This method may allow for a reduction in radiation exposure.

Machine Learning for Medical Imaging

Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. ^©RSNA, 2017.

Progress in Fully Automated Abdominal CT Interpretation

OBJECTIVE

Automated analysis of abdominal CT has advanced markedly over just the last few years. Fully automated assessment of organs, lymph nodes, adipose tissue, muscle, bowel, spine, and tumors are some examples where tremendous progress has been made. Computer-aided detection of lesions has also improved dramatically.

CONCLUSION

This article reviews the progress and provides insights into what is in store in the near future for automated analysis for abdominal CT, ultimately leading to fully automated interpretation.

CT colonography with computer-aided detection: recognizing the causes of false-positive reader results

Computed tomography (CT) colonography is a screening modality used to detect colonic polyps before they progress to colorectal cancer. Computer-aided detection (CAD) is designed to decrease errors of detection by finding and displaying polyp candidates for evaluation by the reader. CT colonography CAD false-positive results are common and have numerous causes. The relative frequency of CAD false-positive results and their effect on reader performance on the basis of a 19-reader, 100-case trial shows that the vast majority of CAD false-positive results were dismissed by readers. Many CAD false-positive results are easily disregarded, including those that result from coarse mucosa, reconstruction, peristalsis, motion, streak artifacts, diverticulum, rectal tubes, and lipomas. CAD false-positive results caused by haustral folds, extracolonic candidates, diminutive lesions (<6 mm), anal papillae, internal hemorrhoids, varices, extrinsic compression, and flexural pseudotumors are almost always recognized and disregarded. The ileocecal valve and tagged stool are common sources of CAD false-positive results associated with reader false-positive results. Nondismissable CAD soft-tissue polyp candidates larger than 6 mm are another common cause of reader false-positive results that may lead to further evaluation with follow-up CT colonography or optical colonoscopy. Strategies for correctly evaluating CAD polyp candidates are important to avoid pitfalls from common sources of CAD false-positive results.

Smart Colonography for Distributed Medical Databases with Group Kernel Feature Analysis

Computer-Aided Detection (CAD) of polyps in Computed Tomographic (CT) colonography is currently very limited since a single database at each hospital/institution doesn't provide sufficient data for training the CAD system's classification algorithm. To address this limitation, we propose to use multiple databases, (e.g., big data studies) to create multiple institution-wide databases using distributed computing technologies, which we call smart colonography. Smart colonography may be built by a larger colonography database networked through the participation of multiple institutions via distributed computing. The motivation herein is to create a distributed database that increases the detection accuracy of CAD diagnosis by covering many true-positive cases. Colonography data analysis is mutually accessible to increase the availability of resources so that the knowledge of radiologists is enhanced. In this article, we propose a scalable and efficient algorithm called Group Kernel Feature Analysis (GKFA), which can be applied to multiple cancer databases so that the overall performance of CAD is improved. The key idea behind the proposed GKFA method is to allow the feature space to be updated as the training proceeds with more data being fed from other institutions into the algorithm. Experimental results show that GKFA achieves very good classification accuracy.

A comparison of material decomposition techniques for dual-energy CT colonography

In recent years, dual-energy computed tomography (DECT) has been widely used in the clinical routine due to improved diagnostics capability from additional spectral information. One promising application for DECT is CT colonography (CTC) in combination with computer-aided diagnosis (CAD) for detection of lesions and polyps. While CAD has demonstrated in the past that it is able to detect small polyps, its performance is highly dependent on the quality of the input data. The presence of artifacts such as beam-hardening and noise in ultra-low-dose CTC may severely degrade detection performances of small polyps. In this work, we investigate and compare virtual monochromatic images, generated by image-based decomposition and projection-based decomposition, with respect to CAD performance. In the image-based method, reconstructed images are firstly decomposed into water and iodine before the virtual monochromatic images are calculated. On the contrary, in the projection-based method, the projection data are first decomposed before calculation of virtual monochromatic projection and reconstruction. Both material decomposition methods are evaluated with regards to the accuracy of iodine detection. Further, the performance of the virtual monochromatic images is qualitatively and quantitatively assessed. Preliminary results show that the projection-based method does not only have a more accurate detection of iodine, but also delivers virtual monochromatic images with reduced beam hardening artifacts in comparison with the image-based method. With regards to the CAD performance, the projection-based method yields an improved detection performance of polyps in comparison with that of the image-based method.

Analysis of scalability of high-performance 3D image processing platform for virtual colonoscopy

One of the key challenges in three-dimensional (3D) medical imaging is to enable the fast turn-around time, which is often required for interactive or real-time response. This inevitably requires not only high computational power but also high memory bandwidth due to the massive amount of data that need to be processed. For this purpose, we previously developed a software platform for high-performance 3D medical image processing, called HPC 3D-MIP platform, which employs increasingly available and affordable commodity computing systems such as the multicore, cluster, and cloud computing systems. To achieve scalable high-performance computing, the platform employed size-adaptive, distributable block volumes as a core data structure for efficient parallelization of a wide range of 3D-MIP algorithms, supported task scheduling for efficient load distribution and balancing, and consisted of a layered parallel software libraries that allow image processing applications to share the common functionalities. We evaluated the performance of the HPC 3D-MIP platform by applying it to computationally intensive processes in virtual colonoscopy. Experimental results showed a 12-fold performance improvement on a workstation with 12-core CPUs over the original sequential implementation of the processes, indicating the efficiency of the platform. Analysis of performance scalability based on the Amdahl's law for symmetric multicore chips showed the potential of a high performance scalability of the HPC 3D-MIP platform when a larger number of cores is available.

Automatic rectum limit detection by anatomical markers correlation

Several diseases take place at the end of the digestive system. Many of them can be diagnosed by means of different medical imaging modalities together with computer aided detection (CAD) systems. These CAD systems mainly focus on the complete segmentation of the digestive tube. However, the detection of limits between different sections could provide important information to these systems. In this paper we present an automatic method for detecting the rectum and sigmoid colon limit using a novel global curvature analysis over the centerline of the segmented digestive tube in different imaging modalities. The results are compared with the gold standard rectum upper limit through a validation scheme comprising two different anatomical markers: the third sacral vertebra and the average rectum length. Experimental results in both magnetic resonance imaging (MRI) and computed tomography colonography (CTC) acquisitions show the efficacy of the proposed strategy in automatic detection of rectum limits. The method is intended for application to the rectum segmentation in MRI for geometrical modeling and as contextual information source in virtual colonoscopies and CAD systems.

Informatics in radiology: Measuring and improving quality in radiology: meeting the challenge with informatics

Quality is becoming a critical issue for radiology. Measuring and improving quality is essential not only to ensure optimum effectiveness of care and comply with increasing regulatory requirements, but also to combat current trends leading to commoditization of radiology services. A key challenge to implementing quality improvement programs is to develop methods to collect knowledge related to quality care and to deliver that knowledge to practitioners at the point of care. There are many dimensions to quality in radiology that need to be measured, monitored, and improved, including examination appropriateness, procedure protocol, accuracy of interpretation, communication of imaging results, and measuring and monitoring performance improvement in quality, safety, and efficiency. Informatics provides the key technologies that can enable radiologists to measure and improve quality. However, few institutions recognize the opportunities that informatics methods provide to improve safety and quality. The information technology infrastructure in most hospitals is limited, and they have suboptimal adoption of informatics techniques. Institutions can tackle the challenges of assessing and improving quality in radiology by means of informatics.

Sources of false positives in computer-assisted CT colonography

The application of computer-aided detection (CAD) is expected to improve reader sensitivity and to reduce inter-observer variance in computed tomographic (CT) colonography. However, current CAD systems display a large number of false-positive (FP) detections. The reviewing of a large number of FP CAD detections increases interpretation time, and it may also reduce the specificity and/or sensitivity of a computer-assisted reader. Therefore, it is important to be aware of the patterns and pitfalls of FP CAD detections. This pictorial essay reviews common sources of FP CAD detections that have been observed in the literature and in our experiments in computer-assisted CT colonography. Also the recommended computer-assisted reading technique is described.

CT colonography: avoiding traps and pitfalls

Computed tomographic colonography (CTC) is a reliable technique for detecting tumoral lesions in the colon. However, good performance of polyp detection is only achieved if experienced CTC radiologists combine meticulous interpretation with state-of-the-art CTC technique. To reach this experience level, CTC training is mandatory. With a considerably long and steep learning curve, it has been demonstrated that in inexperienced hands both technical failure and observer errors stand for the majority of missed lesions. The purpose of this pictorial review is to give an overview of traps and pitfalls in CTC imaging resulting in false negative and positive findings, and how to avoid them by application of state-of-the-art CTC technique and interpretation.

Computer-aided detection of polyps in CT colonography using logistic regression

We present a computer-aided detection (CAD) system for computed tomography colonography that orders the polyps according to clinical relevance. The CAD system consists of two steps: candidate detection and supervised classification. The characteristics of the detection step lead to specific choices for the classification system. The candidates are ordered by a linear logistic classifier (logistic regression) based on only three features: the protrusion of the colon wall, the mean internal intensity, and a feature to discard detections on the rectal enema tube. This classifier can cope with a small number of polyps available for training, a large imbalance between polyps and non-polyp candidates, a truncated feature space, unbalanced and unknown misclassification costs, and an exponential distribution with respect to candidate size in feature space. Our CAD system was evaluated with data sets from four different medical centers. For polyps larger than or equal to 6 mm we achieved sensitivities of respectively 95%, 85%, 85%, and 100% with 5, 4, 5, and 6 false positives per scan over 86, 48, 141, and 32 patients. A cross-center evaluation in which the system is trained and tested with data from different sources showed that the trained CAD system generalizes to data from different medical centers and with different patient preparations. This is essential to application in large-scale screening for colorectal polyps.

Automated detection and segmentation of large lesions in CT colonography

Computerized tomographic colonography is a minimally invasive technique for the detection of colorectal polyps and carcinoma. Computer-aided diagnosis (CAD) schemes are designed to help radiologists locating colorectal lesions in an efficient and accurate manner. Large lesions are often initially detected as multiple small objects, due to which such lesions may be missed or misclassified by CAD systems. We propose a novel method for automated detection and segmentation of all large lesions, i.e., large polyps as well as carcinoma. Our detection algorithm is incorporated in a classical CAD system. Candidate detection comprises preselection based on a local measure for protrusion and clustering based on geodesic distance. The generated clusters are further segmented and analyzed. The segmentation algorithm is a thresholding operation in which the threshold is adaptively selected. The segmentation provides a size measurement that is used to compute the likelihood of a cluster to be a large lesion. The large lesion detection algorithm was evaluated on data from 35 patients having 41 large lesions (19 of which malignant) confirmed by optical colonoscopy. At five false positive (FP) per scan, the classical system achieved a sensitivity of 78%, while the system augmented with the large lesion detector achieved 83% sensitivity. For malignant lesions, the performance at five FP/scan was increased from 79% to 95%. The good results on malignant lesions demonstrate that the proposed algorithm may provide relevant additional information for the clinical decision process.

Comparison of three different iodine-based bowel regimens for CT colonography

OBJECTIVE

The aim of this study was to compare the computed tomographic colonography (CTC) image quality and patient acceptance of three iodine-based faecal tagging bowel preparations in 60 patients undergoing the following regimens: a 2-day regimen of meal-time administration of iodine and phospho-soda (GFPH); a 2-day regimen of meal-time mild laxative, followed by iodine administered 2 h before CTC (SD); and a 2-day regimen of meal-time administration of iodine (GF).

METHODS

Two independent radiologists assessed tagging quality; quantitative measures included the tagged stool density, and computer-aided detection (CAD) false-positive rate.

RESULTS

The GFPH and SD regimens provided better subjective quality than GF (p < 0.001). The latter regimen resulted in a higher proportion of insufficiently tagged segments: the measured average stool density was less than 200 HU in 10.7% in all segments vs 3.6% for SD and <0.5% for GFPH, respectively. Insufficient tagging occurred mostly in the ascending colon and the caecum. The CAD false-positive rate increased following the trend: GFPH < SD < GF (p = 0.00012). GFPH was worse tolerated than SD (p < 0.05).

CONCLUSIONS

Considering preparation quality alone, GFPH was the best regimen, but SD provided the best balance between bowel preparation quality and patient acceptability.

Virtual tagging for laxative-free CT colonography: pilot evaluation

Laxative-free computed tomographic colonography (lfCTC) could significantly improve patient adherence to colorectal screening. However, the interpretation of lfCTC data is complicated by the presence of poorly tagged feces and partial-volume artifacts that imitate colorectal lesions. The authors developed a method for virtual tagging of such artifacts. A probabilistic model of colonic wall was developed, and virtual tagging was performed on artifacts that were identified by the model. The method was evaluated with 46 clinical lfCTC cases that were prepared with dietary fecal tagging only. Visual examples show that the method can label partial-volume artifacts, poorly tagged feces, nonadhering completely untagged feces, and artifacts such as rectal tubes. The effect of virtual tagging was evaluated by comparing the detection accuracy of a fully automated polyp detection scheme without and with the method. With virtual tagging, the per-lesion detection sensitivity was 100% for lesions > or = 10 mm (n = 4) with 3.8 false positives per patient (per two CT scan volumes) and 90% for lesions > or = 6 mm (n = 10) with 5.4 false positives per patient on average. The improvement in detection performance by virtual tagging was statistically significant (p = 0.03; JAFROC and JAFROC-1).

Digital bowel cleansing free colonic polyp detection method for fecal tagging CT colonography

RATIONALE AND OBJECTIVES

Fecal tagging computed tomographic colonography (ftCTC) reduces the discomfort and the inconvenience of patients associated with bowel cleansing procedures before CT scanning. In conventional colonic polyp detection techniques for ftCTC, a digital bowel cleansing (DBC) technique is applied to detect polyps in tagged fecal materials (TFM). However, DBC removes the surface of soft tissues and hampers polyp detection. We developed a colonic polyp detection method for CT colonographic examination that enables the detection of polyps surrounded by air and polyps surrounded by TFM without DBC.

MATERIALS AND METHODS

CT values inside the polyps surrounded by air and polyps surrounded by TFM tend to gradually increase (blob structure) and decrease (inverse-blob structure) from outward to inward, respectively. We developed blob and inverse-blob structure enhancement filters based on the eigenvalues of a Hessian matrix to detect polyps using their intensity characteristic. False-positive elimination is performed using three feature values: volume, maximum value of filter outputs, and standard deviation of CT values inside the polyp candidates.

RESULTS

The proposed method is applied to 104 cases of ftCTC images that include 57 polyps larger than 6 mm in diameter. The sensitivity of the method was 91.2% (52/57) with 11.4 false positives per case.

CONCLUSIONS

The proposed method detects polyps with high sensitivity and 11.4 false positives per case without adverse effects on the DBC.

Efficacy of barium-based fecal tagging for CT colonography: a comparison between the use of high and low density barium suspensions in a Korean population - a preliminary study

OBJECTIVE

This preliminarily study was designed to determine and to compare the efficacy of two commercially available barium-based fecal tagging agents for CT colonography (CTC) (high-density [40% w/v] and low-density [4.6% w/v] barium suspensions) in a population in Korea.

MATERIALS AND METHODS

In a population with an identified with an average-risk for colorectal cancer, 15 adults were administered three doses of 20 ml 40% w/v barium for fecal tagging (group I) and 15 adults were administered three doses of 200 ml 4.6% w/v barium (group II) for fecal tagging. Excluding five patients in group I and one patient in group II that left the study, ten patients in group I and 14 patients in group II were finally included in the analysis. Two experienced readers evaluated the CTC images in consensus regarding the degree of tagging of stool pieces 6 mm or larger. Stool pieces were confirmed with the use of standardized CTC criteria or the absence of matched lesions as seen on colonoscopy. The rates of complete fecal tagging were analyzed on a per-lesion and a per-segment basis and were compared between the patients in the two groups.

RESULTS

Per-lesion rates of complete fecal tagging were 52% (22 of 42; 95% CI, 37.7-66.6%) in group I and 78% (28 of 36; 95% CI, 61.7-88.5%) in group II. The difference between the two groups did not reach statistical significance (p = 0.285). The per-segment rates of complete tagging were 33% (6 of 18; 95% CI, 16.1%-56.4%) in group I and 60% (9 of 15; 95% CI, 35.7%-80.3%) in group II; again, the difference between the two groups did not reach statistical significance (p = 0.171).

CONCLUSION

Barium-based fecal tagging using both the 40% w/v and the 4.6% w/v barium suspensions showed moderate tagging efficacy. The preliminary comparison did not demonstrate a statistically significant difference in the tagging efficacy between the use of the two tagging agents, despite the tendency toward better tagging with the use of the 4.6% w/v barium suspension.

Computer-aided diagnosis: the emerging of three CAD systems induced by Japanese health care needs

The aim of this paper is to describe three emerging computer-aided diagnosis (CAD) systems induced by Japanese health care needs. CAD has been developing fast in the last two decades. The idea of using a computer to help in medical image diagnosis is not new. Some pioneer studies are dated back to the 1960s. In 1998, the first U.S. FDA (Food and Drug Administration) approved commercial CAD system, a film-digitized mammography system, was launched by R2 Technologies, Inc. The success was quickly repeated by a number of companies. The approval of Medicare CAD reimbursement in the U.S. in 2001 further boosted the industry. Today, CAD has its significance in the economy of the medical industry. FDA approved CAD products in the field of breast imaging (mammography, ultrasonography and breast MRI) and chest imaging (radiography and CT) can be seen. In Japan, as part of the "Knowledge Cluster Initiative" of the government, three computer-aided diagnosis (CAD) projects are hosted at the Gifu University since 2004. These projects are regarding the development of CAD systems for the early detection of (1) cerebrovascular diseases using brain MRI and MRA images by detecting lacunar infarcts, unruptured aneurysms, and arterial occlusions; (2) ocular diseases such as glaucoma, diabetic retinopathy, and hypertensive retinopathy using retinal fundus images; and (3) breast cancers using ultrasound 3-D volumetric whole breast data by detecting the breast masses. The projects are entering their final development stage. Preliminary results are presented in this paper. Clinical examinations will be started soon, and commercialized CAD systems for the above subjects will appear by the completion of this project.

Automated segmentation of hepatic vessels in non-contrast X-ray CT images

Hepatic-vessel trees are the key structures in the liver. Knowledge of the hepatic-vessel tree is required because it provides information for liver lesion detection in the computer-aided diagnosis (CAD) system. However, hepatic vessels cannot easily be distinguished from other liver tissues in plain CT images. Automated segmentation of hepatic vessels in plain (non-contrast) CT images is a challenging issue. In this paper, an approach to automatic segmentation of hepatic vessels is proposed. The approach consists of two processing steps: enhancement of hepatic vessels and hepatic-vessel extractions. Enhancement of the vessels was performed with two techniques: (1) histogram transformation based on a Gaussian function; (2) multi-scale line filtering based on eigenvalues of a Hessian matrix. After the enhancement of the vessels, candidates of hepatic vessels were extracted by a thresholding method. Small connected regions in the final results were considered as false positives and were removed. This approach was applied to 2 normal-liver cases for whom plain CT images were obtained. Hepatic vessels segmented from the contrast-enhanced CT images of the same patient were used as the ground truth in evaluation of the performance of the proposed approach. The index of separation ratio between the CT number distributions in hepatic vessels and other liver tissue regions was also used in the evaluation. A subjective evaluation of the hepatic-vessel extraction results based on the additional 16 plain CT cases was carried out for a further validation by a radiologist. The preliminary experimental results showed that the proposed method could enhance and segment the hepatic-vessel regions even in plain CT images.

Colonography by CT, MRI and PET/CT combined with conventional colonoscopy in colorectal cancer screening and staging

Colorectal cancer (CRC) remains a leading cancer killer worldwide. But the disease is both curable and preventable at an early stage. Regular CRC cancer screening has been shown to reduce the risk of dying from CRC. However, the importance of large-scale screening is only now starting to be appreciated. This article reviews a variety of imaging procedures available for detecting ulcerative colitis (UC) and Crohn’s disease (CD), polyps and CRC in their early stage and also presents details on various screening options. Detecting, staging and re-staging of patients with CRC also require multimodality, multistep imaging approaches. Staging and re-staging with conventional colonoscopy (CC), computer tomography colonography (CTC), magnetic resonance colonography (MRC) and positron emission tomography/computer tomography colonography (PET/CTC) are of paramount importance in determining the most appropriate therapeutic method and in predicting the risk of tumor recurrence and overall prognosis. The advantages and limitations of these modalities are also discussed.

Adaptive correction of the pseudo-enhancement of CT attenuation for fecal-tagging CT colonography

In fecal-tagging CT colonography (ftCTC), positive-contrast tagging agents are used for opacifying residual bowel materials to facilitate reliable detection of colorectal lesions. However, tagging agents that have high radiodensity tend to artificially elevate the observed CT attenuation of nearby materials toward that of tagged materials on Hounsfield unit (HU) scale. We developed an image-based adaptive density-correction (ADC) method for minimizing such pseudo-enhancement effect in ftCTC data. After the correction, we can confidently assume that soft-tissue materials and air are represented by their standard CT attenuations, whereas higher CT attenuations indicate tagged materials. The ADC method was optimized by use of an anthropomorphic phantom filled partially with three concentrations of a tagging agent. The effect of ADC on ftCTC was assessed visually and quantitatively by comparison of the accuracy of computer-aided detection (CAD) without and with the use of the ADC method in two different types of clinical ftCTC databases: 20 laxative ftCTC cases with 24 polyps, and 23 reduced-preparation ftCTC cases with 28 polyps. Visual evaluation indicated that ADC minimizes the observed pseudo-enhancement effect. With ADC, the free-response receiver operating characteristic curves indicating CAD performance in polyp detection yielded normalized partial area-under-curve values of 0.91 and 0.80 for the two databases, respectively, with statistically significant improvement over conventional thresholding-based approaches (p<0.05). The results indicate that ADC is a useful method for reducing the pseudo-enhancement effect and for improving CAD performance in CTC.