Yoko Kominami

Carbon dioxide insufflation is useful for obtaining clear images of the bile duct during peroral cholangioscopy (with video)

BACKGROUND Peroral cholangioscopy (POCS) is useful for the diagnosis of various bile duct lesions. However, it is often difficult to obtain clear images because of bile or biliary sludge in the bile duct, even after vigorous irrigation of the bile duct with saline solution. Therefore, this study investigated whether inflation with carbon dioxide (CO(2)) yields clearer images of the bile duct than conventional saline solution irrigation during POCS. OBJECTIVE To evaluate the clinical utility and safety of CO(2) insufflation into the bile duct to obtain clear images in POCS observations by comparing this method with conventional saline solution irrigation. SETTING A single center. DESIGN Case-control study. PATIENTS Nineteen patients with suspected biliary diseases. INTERVENTIONS CO(2) insufflation into the bile duct during POCS. MAIN OUTCOME MEASUREMENTS The quality and safety of this method. RESULT The quality of the images of the bile duct lumen with CO(2) insufflation (10 patients) was significantly superior to those with saline solution irrigation (9 patients) in both clarity (P < .05) and color (P < .05). In particular, extremely clear images could be obtained from the middle part of common bile duct to the right and left hepatic duct. No serious POCS-related complications occurred. There was no significant change in the venous partial pressure of the CO(2) level during the procedure. LIMITATIONS The number of patients examined was small. CONCLUSIONS CO(2) insufflation is useful for obtaining clear images of the bile duct during POCS, which makes it possible to determine the qualitative diagnosis and the extent of various bile duct lesions.

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.

Calibration of a 3D endoscopic system based on active stereo method for shape measurement of biological tissues and specimen.

For endoscopic medical treatment, measuring the size and shape of the lesion, such as a tumor, is important for the improvement of diagnostic accuracy. We are developing a system to measure the shapes and sizes of living tissue by active stereo method using a normal endoscope on which a micro pattern projector is attached. In order to perform 3D reconstruction, estimating the intrinsic and extrinsic parameters of the endoscopic camera and the pattern projector is required. Particularly, calibration of the pattern projector is difficult. In this paper, we propose a simultaneous estimation method of both intrinsic and extrinsic parameters of the pattern projector. This simplifies the calibration procedure required in practical scenes. Furthermore, we have developed an efficient user interface to intuitively operate the calibration and reconstruction procedures. Using the developed system, we measured the shape of an internal tissue of the soft palate of a human and a biological specimen.

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

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.

SVM-MRF segmentation of colorectal NBI endoscopic images.

In this paper we investigate a method for segmentation of colorectal Narrow Band Imaging (NBI) endoscopic images with Support Vector Machine (SVM) and Markov Random Field (MRF). SVM classifiers recognize each square patch of an NBI image and output posterior probabilities that represent how likely the given patch falls into a certain label. To prevent the spatial inconsistency between adjacent patches and encourage segmented regions to have smoother shapes, MRF is introduced by using the posterior outputs of SVMs as a unary term of MRF energy function. Segmentation results of 1191 NBI images are evaluated in experiments in which SVMs were trained with 480 trimmed NBI images and the MRF energy was minimized by an α - β swap Graph Cut.

Transfer learning for Bag-of-Visual words approach to NBI endoscopic image classification

We address a problem of endoscopic image classification taken by different (e.g., old and new) endoscopies. Our proposed method formulates the problem as a constraint optimization that estimates a linear transformation between feature vectors (or Bag-of-Visual words histograms) in a framework of transfer learning. Experimental results show that the proposed method works much better than the case without feature transformation.

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.

Image segmentation of pyramid style identifier based on Support Vector Machine for colorectal endoscopic images

With the increase of colorectal cancer patients in recent years, the needs of quantitative evaluation of colorectal cancer are increased, and the computer-aided diagnosis (CAD) system which supports doctors diagnosis is essential. In this paper, a hardware design of type identification module in CAD system for colorectal endoscopic images with narrow band imaging (NBI) magnification is proposed for real-time processing of full high definition image (1920 × 1080 pixel). A pyramid style image segmentation with SVMs for multi-size scan windows, which can be implemented on an FPGA with small circuit area and achieve high accuracy, is proposed for actual complex colorectal endoscopic images.

FPGA implementation of feature extraction for colorectal endoscopic images with NBI magnification

In this study, we have proposed an improvement for feature extraction in computer-aided diagnosis (CAD) system for colorectal endoscopic images with narrow-band imaging (NBI) magnification. Dense Scale-Invariant Feature Transform (D-SIFT) is used in the feature extraction. It is necessary to consider a trade-off between the precision of the feature extraction and speedup by the FPGA implementation for processing of real time full high definition image. In this paper, we reduced the number of dimensions for feature representation in hardware implementation purpose.

FPGA implementation of type identifier for colorectal endoscopie images with NBI magnification

With the increase of colorectal cancer patients in recent years, the needs of quantitative evaluation of colorectal cancer are increased, and the computer-aided diagnosis (CAD) system which supports doctors diagnosis is essential. In this paper, a hardware design of type identification module in CAD system for colorectal endoscopie images with narrow band imaging (NBI) magnification [1] is proposed for real-time processing of full high definition (Full HD) image (1920 × 1080 pixel). In this paper, 2-step Identifier with SVM to realize a 3-class identification, which occupies small circuit area and achieves high accuracy, is proposed.