Evaluation of dual-wavelength excitation autofluorescence imaging of colorectal tumours with a high-sensitivity CMOS imager: a cross-sectional study.

BACKGROUND: It is important to devise efficient and easy methods of detecting colorectal tumours to reduce mortality from colorectal cancer. Dual-wavelength excitation autofluorescence intensity can be used to visualize colorectal tumours. Therefore, we evaluated dual-wavelength excitation autofluorescence images of colorectal tumours obtained with a newly developed, high-sensitivity complementary metal-oxide-semiconductor (CMOS) imager. METHODS: A total 107 colorectal tumours (44 adenomas, 43 adenocarcinomas with intramucosal invasion, and 20 sessile serrated adenoma/polyps [SSA/Ps]) in 98 patients who underwent endoscopic tumour resection were included. The specimens were irradiated with excitation light at 365 nm and 405 nm, and autofluorescence images measured with a 475 ± 25-nm band pass filter were obtained using a new, high-sensitivity CMOS imager. Ratio images (F365ex/F405ex) were created to evaluate the lesion brightness compared with that of normal mucosa, and specimens were categorized into a no signal or high signal group. RESULTS: Adenomas and adenocarcinomas were depicted in 87 ratio images, with 86.2% (n = 75) in the High signal group. SSA/P was depicted in 20 ratio images, with 70.0% (n = 14) in the High signal group. CONCLUSIONS: Dual-wavelength excitation autofluorescence images of colorectal tumours can be acquired using our high-sensitivity CMOS imager, and are useful in detecting colorectal tumours.

A computer system to be used with laser-based endoscopy for quantitative diagnosis of early gastric cancer.

GOALS: To evaluate the usefulness of a newly devised computer system for use with laser-based endoscopy in differentiating between early gastric cancer, reddened lesions, and surrounding tissue. BACKGROUND: Narrow-band imaging based on laser light illumination has come into recent use. We devised a support vector machine (SVM)-based analysis system to be used with the newly devised endoscopy system to quantitatively identify gastric cancer on images obtained by magnifying endoscopy with blue-laser imaging (BLI). We evaluated the usefulness of the computer system in combination with the new endoscopy system. STUDY: We evaluated the system as applied to 100 consecutive early gastric cancers in 95 patients examined by BLI magnification at Hiroshima University Hospital. We produced a set of images from the 100 early gastric cancers; 40 flat or slightly depressed, small, reddened lesions; and surrounding tissues, and we attempted to identify gastric cancer, reddened lesions, and surrounding tissue quantitatively. RESULTS: The average SVM output value was 0.846 ± 0.220 for cancerous lesions, 0.381 ± 0.349 for reddened lesions, and 0.219 ± 0.277 for surrounding tissue, with the SVM output value for cancerous lesions being significantly greater than that for reddened lesions or surrounding tissue. The average SVM output value for differentiated-type cancer was 0.840 ± 0.207 and for undifferentiated-type cancer was 0.865 ± 0.259. CONCLUSIONS: Although further development is needed, we conclude that our computer-based analysis system used with BLI will identify gastric cancers quantitatively.

Labeling colorectal NBI zoom-videoendoscope image sequences with MRF and SVM.

In this paper, we propose a sequence labeling method by using SVM posterior probabilities with a Markov Random Field (MRF) model for colorectal Narrow Band Imaging (NBI) zoom-videoendoscope. Classifying each frame of a video sequence by SVM classifiers independently leads to an output sequence which is unstable and hard to understand by endoscopists. To make it more stable and readable, we use an MRF model to label the sequence of posterior probabilities. In addition, we introduce class asymmetry for the NBI images in order to keep and enhance frames where there is a possibility that cancers might have been detected. Experimental results with NBI video sequences demonstrate that the proposed MRF model with class asymmetry performs much better than a model without asymmetry.

Clinical usefulness of classification by transabdominal ultrasonography for detection of small-bowel stricture.

OBJECTIVE: To assess the clinical usefulness of transabdominal ultrasonography (TUS) for detection of small-bowel stricture. PATIENTS AND METHODS: Subjects were 796 patients undergoing double-balloon endoscopy (DBE), December 2003-October 2011. All underwent TUS prior to DBE. The TUS findings were classified by type as intestinal narrowing and distension at the oral side (Type A); extensive bowel wall thickening (Type B); focal bowel wall thickening (Type C) or no abnormality detected (Type D). We compared TUS findings against DBE findings with respect to small-bowel stricture, defined as failure of the enteroscope to pass through the small bowel. RESULTS: Small-bowel stricture was detected by DBE in 11.3% (90/796) of patients. Strictures resulted from Crohn's disease (n = 36), intestinal tuberculosis (n = 24), malignant lymphoma (n = 9), ischemic enteritis (n = 6), NSAID ulcer (n = 5), radiation enteritis (n = 2), surgical anastomosis (n = 2) and other abnormalities (n = 6). Stricture was detected by TUS in 93.3% (84/90) of patients, and each such stricture fell into one of the three types of TUS abnormality. The remaining 6 strictures were detected only by DBE. DBE-identified strictures corresponded to TUS findings as follows: 100% (43/43) to Type A, 59.1% (29/49) to Type B, 14.8% (12/81) to Type C and 1% (6/623) to Type D. Correspondence between stricture and the Type A classification (vs. Types B, C and D) was significantly high, as was correspondence between stricture and Type B (vs. Types C and D). CONCLUSIONS: TUS was shown to be useful for detecting small-bowel stricture. We recommend performing TUS first when a small-bowel stricture is suspected.

Quantitative identification of mucosal gastric cancer under magnifying endoscopy with flexible spectral imaging color enhancement.

BACKGROUND AND AIM: Magnifying endoscopy with flexible spectral imaging color enhancement (FICE) is clinically useful in diagnosing gastric cancer and determining treatment options; however, there is a learning curve. Accurate FICE-based diagnosis requires training and experience. In addition, objectivity is necessary. Thus, a software program that can identify gastric cancer quantitatively was developed. METHODS: A bag-of-features framework with densely sampled scale-invariant feature transform descriptors to magnifying endoscopy images of 46 mucosal gastric cancers was applied. Computer-based findings were compared with histologic findings. The probability of gastric cancer was calculated by means of logistic regression, and sensitivity and specificity of the system were determined. RESULTS: The average probability was 0.78 ± 0.25 for the images of cancer and 0.31 ± 0.25 for the images of noncancer tissue, with a significant difference between the two groups. An optimal cut-off point of 0.59 was determined on the basis of the receiver operating characteristic curves. The computer-aided diagnosis system yielded a detection accuracy of 85.9% (79/92), sensitivity for a diagnosis of cancer of 84.8% (39/46), and specificity of 87.0% (40/46). CONCLUSION: Further development of this system will allow for quantitative evaluation of mucosal gastric cancers on magnifying gastrointestinal endoscopy images obtained with FICE.

Computer-aided colorectal tumor classification in NBI endoscopy using local features.

An early detection of colorectal cancer through colorectal endoscopy is important and widely used in hospitals as a standard medical procedure. During colonoscopy, the lesions of colorectal tumors on the colon surface are visually inspected by a Narrow Band Imaging (NBI) zoom-videoendoscope. By using the visual appearance of colorectal tumors in endoscopic images, histological diagnosis is presumed based on classification schemes for NBI magnification findings. In this paper, we report on the performance of a recognition system for classifying NBI images of colorectal tumors into three types (A, B, and C3) based on the NBI magnification findings. To deal with the problem of computer-aided classification of NBI images, we explore a local feature-based recognition method, bag-of-visual-words (BoW), and provide extensive experiments on a variety of technical aspects. The proposed prototype system, used in the experiments, consists of a bag-of-visual-words representation of local features followed by Support Vector Machine (SVM) classifiers. A number of local features are extracted by using sampling schemes such as Difference-of-Gaussians and grid sampling. In addition, in this paper we propose a new combination of local features and sampling schemes. Extensive experiments with varying the parameters for each component are carried out, for the performance of the system is usually affected by those parameters, e.g. the sampling strategy for the local features, the representation of the local feature histograms, the kernel types of the SVM classifiers, the number of classes to be considered, etc. The recognition results are compared in terms of recognition rates, precision/recall, and F-measure for different numbers of visual words. The proposed system achieves a recognition rate of 96% for 10-fold cross validation on a real dataset of 908 NBI images collected during actual colonoscopy, and 93% for a separate test dataset.