The mortality rate of colorectal cancer in Japan is increasing year by year. The mortality rate is 3rd in males and 1st in death rate in females. However, it is possible to raise the 5-year survival rate to over 80% by discovering and resecting it early. Colorectal cancer is a malignant tumor that spreads and spreads. There are various types of metastasis, but the most important factor in predicting the prognosis of early colorectal cancer patients is lymph node metastasis. Generally, it is said that the greater the lymph node diameter, the higher the possibility of positive for metastasis. However, there are cases where metastasis is also confirmed in those with small lymph node diameters. The purpose of this study is to detect the metastatic lymph nodes using spatiotemporal features of triplet-phase CT images (arterial phase, portal vein phase, equilibrium phase). This method consists of 1) lymph node extraction, 2) metastatic lymph node classification and 3) quantitative assessment of metastatic lymph node. The method was applied to 33 cases of rectal cancer. For quantitative analysis of lymph node metastasis, logistic regression analysis is used to identify the image feature dominant in lymph node metastasis.
Liver segmentation is the basis for computer-based planning of hepatic surgical interventions. In diagnosis and analysis of hepatic diseases and surgery planning, automatic segmentation of liver has high importance. Blood vessel (BV) has showed high performance at liver segmentation. In our previous work, we developed a semi-automatic method that segments the liver through the portal phase abdominal CT images in two stages. First stage was interactive segmentation of abdominal blood vessels (ABVs) and subsequent classification into hepatic (HBVs) and non-hepatic (non-HBVs). This stage had 5 interactions that include selective threshold for bone segmentation, selecting two seed points for kidneys segmentation, selection of inferior vena cava (IVC) entrance for starting ABVs segmentation, identification of the portal vein (PV) entrance to the liver and the IVC-exit for classifying HBVs from other ABVs (non-HBVs). Second stage is automatic segmentation of the liver based on segmented ABVs as described in [4]. For full automation of our method we developed a method [5] that segments ABVs automatically tackling the first three interactions. In this paper, we propose full automation of classifying ABVs into HBVs and non- HBVs and consequently full automation of liver segmentation that we proposed in [4]. Results illustrate that the method is effective at segmentation of the liver through the portal abdominal CT images.
In abdominal disease diagnosis and various abdominal surgeries planning, segmentation of abdominal blood vessel (ABVs) is a very imperative task. Automatic segmentation enables fast and accurate processing of ABVs. We proposed a fully automatic approach for segmenting ABVs through contrast enhanced CT images by a hybrid of 3D region growing and 4D curvature analysis. The proposed method comprises three stages. First, candidates of bone, kidneys, ABVs and heart are segmented by an auto-adapted threshold. Second, bone is auto-segmented and classified into spine, ribs and pelvis. Third, ABVs are automatically segmented in two sub-steps: (1) kidneys and abdominal part of the heart are segmented, (2) ABVs are segmented by a hybrid approach that integrates a 3D region growing and 4D curvature analysis. Results are compared with two conventional methods. Results show that the proposed method is very promising in segmenting and classifying bone, segmenting whole ABVs and may have potential utility in clinical use.
To investigate the actual usefulness of computer-aided detection (CADe) of polyps as a second reader, we conducted a
free-response observer performance study with radiologists in the detection of “difficult” polyps in CT colonography
(CTC) from a multicenter clinical trial. The “difficult” polyps were defined as the ones that had been “missed” by
radiologists in the clinical trial or rated “difficult” in our retrospective review. Our advanced CADe scheme utilizing
massive-training artificial neural networks (MTANNs) technology was sensitive and specific to the “difficult” polyps.
Four board-certified abdominal radiologists participated in this observer study. They were instructed, first without and
then with our CADe, to indicate the location of polyps and their confidence level regarding the presence of polyps. Our database contains 20 patients with 23 polyps including 14 false-negative (FN) and 7 “difficult” polyps and 10 negative patients. With CADe, the average by-polyp sensitivity of radiologists was improved from 53 to 63% at a statistically significant level (P=0.037). Thus, our CADe scheme utilizing the MTANN technology improved the diagnostic
performance of radiologists, including expert readers, in the detection of “difficult” polyps in CTC.
In this paper, we propose an automated incision line determination method for virtual unfolded view generation
of the stomach from 3D abdominal CT images. The previous virtual unfolding methods of the stomach
required a lot of manual operations such as determination of the incision line, which heavily tasks an operator.
In general, an incision line along the greater curvature of the stomach is used for making pathological
specimen. In our method, an incision line is automatically determined by projecting a centerline of the
stomach onto the gastric surface from a projection line. The projection line is determined by using positions
of the cardia and the pylorus, that can be easily specified by two mouse clicks. The process of our method
is performed as follows. We extract the stomach region using a thresholding and a labeling processes. We
apply a thinning process to the stomach region, and then we extract the longest line from the result of the
thinning process. We obtain a centerline of the stomach region by smoothing the longest line by using a
Bezier curve. The incision line is calculated by projecting the centerline onto the gastric surface from the
projection line. We applied the proposed method to 19 cases of CT images. We automatically determined
incision lines. Experimintal results showed our method was able to determine incision lines along the greater
curvature for most of 19 cases.
CT colonography is a radiology test that looks at people's large intestines(colon). CT colonography can screen many
options of colon cancer. This test is used to detect polyps or cancers of the colon. CT colonography is safe and reliable. It
can be used if people are too sick to undergo other forms of colon cancer screening.
In our research, we proposed a method for automatic segmentation of the colon from abdominal computed Tomography
(CT) images. Our multistage detection method extracted colon and spited colon into different parts according to the
colon anatomy information. We found that among the five segmented parts of the colon, sigmoid (20%) and rectum
(50%) are more sensitive toward polyps and masses than the other three parts. Our research focused on detecting the
colon by the individual diagnosis of sigmoid and rectum. We think it would make the rapid and easy diagnosis of colon
in its earlier stage and help doctors for analysis of correct position of each part and detect the colon rectal cancer much
easier.
Recently, CT colonography has been recognized as an effective option for evaluating colorectal polyps in the USA. We have applied this technique to preoperative staging of colorectal cancer patients with a contrast-enhanced multi-detector row CT (MDCT). The use of manipulated multi-planar reconstruction (MPR) views in contrast-enhanced MDCT colonography proved advantageous for detecting lymph node metastases. Furthermore, 3-dimensional (3D) endoluminal images with Hansfield-transparency settings allowed vascular views of the colorectal wall for identification of invasive colorectal cancers. Using endoluminal images, increase in flow and pooling of blood related to angiogenesis of invasive cancer could be demonstrated, not only in the lymph nodes but also in the colorectal wall. Both MPR views and 3D endoluminal images can be acquired from the same 3D volumetric data generated by helical scanning in MDCT colonography, and both have great potential as modalities for computer-aided diagnosis (CAD) using blood flow information. Therefore the use of CAD can be expected to improve radiologists' diagnostic performance with regard to colorectal cancer.
KEYWORDS: Imaging systems, Cameras, Radiography, Signal to noise ratio, Modulation transfer functions, Sensors, Medical imaging, Fluoroscopy, X-ray detectors, Image quality
A new DR system using a large-area flat panel detector (FPD) with a 40 by 30 cm active area and a 194 micrometers pixel pitch, has been developed to compare with a conventional image intensifier and charge-coupled device camera type DR system. After measuring basic characteristics of the new DR system such as signal-to-noise ratio, modulation transfer function, and detective quantum efficiency, we applied the FPD to a Gastro-Intestinal study with contrast media, and discussed its potential for clinical use with a medical doctor. In radiography mode, the new DR system with a large-are FPD has superior image quality compared with the conventional I.I.- CCD camera type DR system because of high SNR and DQE. In fluoroscopy mode, the SNR of the new DR system at the exposure range of over 2(mu) R/frame is similar with the conventional I.I.-CCD camera type DR system. As a result, we considered that new DR system with a large-area FPD could be applied to a clinical study replacing an I.I.-CCD camera type. In the evaluation using various clinical images taken with the new DR system by a medical doctor, the new DR system with a large-are FPD performed sufficiently for a GI study.
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