Polyp enhancing level set evolution of colon wall: method and pilot study

Attraction Propagation: A User-Friendly Interactive Approach for Polyp Segmentation in Colonoscopy Images

The article raised a user-friendly interactive approach-Attraction Propagation (AP) in segmentation of colorectal polyps. Compared with other interactive approaches, the AP relied on only one foreground seed to get different shapes of polyps, and it can be compatible with pre-processing stage of Computer-Aided Diagnosis (CAD) under the systematically procedure of Optical Colonoscopy (OC). The experimental design was based on challenging distinct datasets that totally includes 1691 OC images, and the results demonstrated that no matter in accuracy or calculating speed, the AP performed better than the state-of-the-art.

AUTOMATIC DETECTION OF SIMILARITIES AND DIFFERENCES BETWEEN SMALL BOWEL POLYPS AND ULCERS WITH A DATA MINING APPROACH IN WIRELESS CAPSULE ENDOSCOPY VIDEOS

Over the past decade Wireless Capsule Endoscopy (WCE) technology has become a very useful tool for diagnosing diseases within the human digestive tract. Using WCE physicians can examine the digestive tract in a minimum invasive way searching for pathological abnormalities such as bleeding, polyps, ulcers and Crohn's disease. In order for WCE to be more effective for gastroenterologists, engineers have developed software methods to automatically detect these diseases at high successful rate. Using proposed a synergistic methodology for automatic discovering polyps (protrusions) and ulcers in WCE video frames, a data mining approach is used that offers useful information about ulcers, polyps and normal tissues and their visual similarities. Finally, results of the methodology are given and statistical comparisons are also presented relevant to other works.

Machine learning and radiology

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers.

Seeing is believing: video classification for computed tomographic colonography using multiple-instance learning

In this paper, we present development and testing results for a novel colonic polyp classification method for use as part of a computed tomographic colonography (CTC) computer-aided detection (CAD) system. Inspired by the interpretative methodology of radiologists using 3-D fly-through mode in CTC reading, we have developed an algorithm which utilizes sequences of images (referred to here as videos) for classification of CAD marks. For each CAD mark, we created a video composed of a series of intraluminal, volume-rendered images visualizing the detection from multiple viewpoints. We then framed the video classification question as a multiple-instance learning (MIL) problem. Since a positive (negative) bag may contain negative (positive) instances, which in our case depends on the viewing angles and camera distance to the target, we developed a novel MIL paradigm to accommodate this class of problems. We solved the new MIL problem by maximizing a L2-norm soft margin using semidefinite programming, which can optimize relevant parameters automatically. We tested our method by analyzing a CTC data set obtained from 50 patients from three medical centers. Our proposed method showed significantly better performance compared with several traditional MIL methods.

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.

Improved computer-aided detection of small polyps in CT colonography using interpolation for curvature estimation

PURPOSE

Surface curvatures are important geometric features for the computer-aided analysis and detection of polyps in CT colonography (CTC). However, the general kernel approach for curvature computation can yield erroneous results for small polyps and for polyps that lie on haustral folds. Those erroneous curvatures will reduce the performance of polyp detection. This paper presents an analysis of interpolation's effect on curvature estimation for thin structures and its application on computer-aided detection of small polyps in CTC.

METHODS

The authors demonstrated that a simple technique, image interpolation, can improve the accuracy of curvature estimation for thin structures and thus significantly improve the sensitivity of small polyp detection in CTC.

RESULTS

Our experiments showed that the merits of interpolating included more accurate curvature values for simulated data, and isolation of polyps near folds for clinical data. After testing on a large clinical data set, it was observed that sensitivities with linear, quadratic B-spline and cubic B-spline interpolations significantly improved the sensitivity for small polyp detection.

CONCLUSIONS

The image interpolation can improve the accuracy of curvature estimation for thin structures and thus improve the computer-aided detection of small polyps in CTC.

Improved curvature estimation for computer-aided detection of colonic polyps in CT colonography

RATIONALE AND OBJECTIVES

Current schemes for computer-aided detection (CAD) of colon polyps usually use kernel methods to perform curvature-based shape analysis. However, kernel methods may deliver spurious curvature estimations if the kernel contains two surfaces, because of the vanished gradient magnitudes. The aim of this study was to use the Knutsson mapping method to deal with the difficulty of providing better curvature estimations and to assess the impact of improved curvature estimation on the performance of CAD schemes.

MATERIALS AND METHODS

The new method was compared to two widely used kernel methods in terms of the performance of two stages of CAD: initial detection and true-positive and false-positive classification. The evaluation was conducted on a database of 130 computed tomographic scans from 67 patients. In these patient scans, there were 104 clinically significant polyps and masses >5 mm.

RESULTS

In the initial detection stage, the detection sensitivity of the three methods was comparable. In the classification stage, at a 90% sensitivity level on the basis of the input of this step, the new technique yielded 3.15 false-positive results per scan, demonstrating reductions in false-positive findings of 30.2% (P < .01) and 27.9% (P < .01) compared to the two kernel methods.

CONCLUSIONS

The new method can benefit CAD schemes with reduced false-positive rates, without sacrificing detection sensitivity.

Detection of small bowel polyps and ulcers in wireless capsule endoscopy videos

Over the last decade, wireless capsule endoscopy (WCE) technology has become a very useful tool for diagnosing diseases within the human digestive tract. Physicians using WCE can examine the digestive tract in a minimally invasive way searching for pathological abnormalities such as bleeding, polyps, ulcers, and Crohn's disease. To improve effectiveness of WCE, researchers have developed software methods to automatically detect these diseases at a high rate of success. This paper proposes a novel synergistic methodology for automatically discovering polyps (protrusions) and perforated ulcers in WCE video frames. Finally, results of the methodology are given and statistical comparisons are also presented relevant to other works.

CT colonography and non-polypoid colorectal neoplasms

Computed tomographic colonography (CTC) has been reported to be as effective as optical colonoscopy in the detection of significant adenomas. However, there are widely conflicting performance data in relation to detection of flat neoplasia. This article describes the potential and limitations of CTC and computer-aided diagnosis in the detection of flat neoplasms.

Detection and segmentation of colonic polyps on implicit isosurfaces by second principal curvature flow

Today's computer aided detection systems for computed tomography colonography (CTC) enable automated detection and segmentation of colorectal polyps. We present a paradigm shift by proposing a method that measures the amount of protrudedness of a candidate object in a scale adaptive fashion. One of the main results is that the performance of the candidate detection depends only on one parameter, the amount of protrusion. Additionally the method yields correct polyp segmentation without the need of an additional segmentation step. The supervised pattern recognition involves a clear distinction between size related features and features related to shape or intensity. A Mahalanobis transformation of the latter facilitates ranking of the objects using a logistic classifier. We evaluate two implementations of the method on 84 patients with a total of 57 polyps larger than or equal to 6 mm. We obtained a performance of 95% sensitivity at four false positives per scan for polyps larger than or equal to 6 mm.

EMPLOYING TOPOGRAPHICAL HEIGHT MAP IN COLONIC POLYP MEASUREMENT AND FALSE POSITIVE REDUCTION

CT Colonography (CTC) is an emerging minimally invasive technique for screening and diagnosing colon cancers. Computer Aided Detection (CAD) techniques can increase sensitivity and reduce false positives. Inspired by the way radiologists detect polyps via 3D virtual fly-through in CTC, we borrowed the idea from geographic information systems to employ topographical height map in colonic polyp measurement and false positive reduction. After a curvature based filtering and a 3D CT feature classifier, a height map is computed for each detection using a ray-casting algorithm. We design a concentric index to characterize the concentric pattern in polyp height map based on the fact that polyps are protrusions from the colon wall and round in shape. The height map is optimized through a multi-scale spiral spherical search to maximize the concentric index. We derive several topographic features from the map and compute texture features based on wavelet decomposition. We then send the features to a committee of support vector machines for classification. We have trained our method on 394 patients (71 polyps) and tested it on 792 patients (226 polyps). Results showed that we can achieve 95% sensitivity at 2.4 false positives per patient and the height map features can reduce false positives by more than 50%. We compute the polyp height and width measurements and correlate them with manual measurements. The Pearson correlations are 0.74 (p=0.11) and 0.75 (p=0.17) for height and width, respectively.