Postmortem temporal chest CT and its pathological correlation in piglets.

Post-mortem computed tomography (PMCT) is a useful tool to investigate the cause of death. To appropriately use PMCT for cause-of-death analysis, it is necessary to know natural courses after death such as hypostasis in the lungs. We aimed to investigate the natural time-course change of postmortem chest CT findings and its pathological correlation in piglets.

A quantitative analysis of progressive fibrosing interstitial lung disease on computed tomography for the assessment of decreased vital capacity.

Background: The results of a quantitative analysis of computed tomography (CT) of interstitial lung disease (ILD) using a computer-aided detection (CAD) technique were correlated with the results of pulmonary function tests.

Purpose: To evaluate the correlation between a quantitative analysis of CT of progressive fibrosing interstitial lung disease (PF-ILD) including idiopathic pulmonary fibrosis (IPF) and non-IPF, which can manifest progressive pulmonary fibrosis and the vital capacity (VC), and to identify indicators for the assessment of a decreased VC.

Material And Methods: A total of 73 patients (46 patients with IPF and 27 patients with non-IPF) were included in this study.

Treatment of Inflammatory Bowel Disease with Biologics in Japan: A Single-Center, Retrospective Pharmacoeconomic Study.

Biologics are essential for treating inflammatory bowel disease (IBD); however, only a few studies have validated cost-effective treatment options and patient factors for biologic use using real-world data from Japanese patients with IBD. Here, we aimed to provide pharmacoeconomic evidence to support clinical decisions for IBD treatment using biologics. We assessed 183 cases (127 patients) of IBD treated with biologics between November 2004 and September 2021.

A low prognostic nutritional index is associated with increased remote infections within 30 days of colorectal surgery: A retrospective cohort study.

Objective: This study aimed to identify risk factors for remote infection (RI) within 30 days after colorectal surgery.

Methods: This retrospective study included 660 patients who underwent colorectal surgery at Yamaguchi University Hospital or Ube Kosan Central Hospital between April 2015 and March 2019. Using electronic medical records, we identified the incidence of surgical site infection and RI within 30 days after surgery and obtained information on associated factors.

Segmentation of Lung Nodules on CT Images Using a Nested Three-Dimensional Fully Connected Convolutional Network.

In computer-aided diagnosis systems for lung cancer, segmentation of lung nodules is important for analyzing image features of lung nodules on computed tomography (CT) images and distinguishing malignant nodules from benign ones. However, it is difficult to accurately and robustly segment lung nodules attached to the chest wall or with ground-glass opacities using conventional image processing methods. Therefore, this study aimed to develop a method for robust and accurate three-dimensional (3D) segmentation of lung nodule regions using deep learning.

Deep Learning for Pulmonary Image Analysis: Classification, Detection, and Segmentation.

Image-based computer-aided diagnosis (CAD) algorithms by the use of convolutional neural network (CNN) which do not require the image-feature extractor are powerful compared with conventional feature-based CAD algorithms which require the image-feature extractor for classification of lung abnormalities. Moreover, computer-aided detection and segmentation algorithms by the use of CNN are useful for analysis of lung abnormalities. Deep learning will improve the performance of CAD systems dramatically.

Pulmonary Textures Classification via a Multi-Scale Attention Network.

Precise classification of pulmonary textures is crucial to develop a computer aided diagnosis (CAD) system of diffuse lung diseases (DLDs). Although deep learning techniques have been applied to this task, the classification performance is not satisfied for clinical requirements, since commonly-used deep networks built by stacking convolutional blocks are not able to learn discriminative feature representation to distinguish complex pulmonary textures. For addressing this problem, we design a multi-scale attention network (MSAN) architecture comprised by several stacked residual attention modules followed by a multi-scale fusion module.

Automatic classification of lung nodules on MDCT images with the temporal subtraction technique.

Purpose: A temporal subtraction (TS) image is obtained by subtracting a previous image, which is warped to match the structures of the previous image and the related current image. The TS technique removes normal structures and enhances interval changes such as new lesions and substitutes in existing abnormalities from a medical image. However, many artifacts remaining on the TS image can be detected as false positives.

Characterization of a new carmovirus isolated from an Adonis plant.

An isometric virus was isolated from a cultivated Adonis plant (A. ramosa). The purified virus particle is 28 nm in diameter and is composed of a single coat protein and a single RNA genome of 3,991 nucleotides.

Unsupervised class labeling of diffuse lung diseases using frequent attribute patterns.

Purpose: For realizing computer-aided diagnosis (CAD) of computed tomography (CT) images, many pattern recognition methods have been applied to automatic classification of normal and abnormal opacities; however, for the learning of accurate classifier, a large number of images with correct labels are necessary. It is a very time-consuming and impractical task for radiologists to give correct labels for a large number of CT images. In this paper, to solve the above problem and realize an unsupervised class labeling mechanism without using correct labels, a new clustering algorithm for diffuse lung diseases using frequent attribute patterns is proposed.

Determination of lung regions on chest ct images with diffuse lung diseases by use of anatomical structures and pulmonary textures.

To analyze diffuse lung diseases based on chest region computed tomography (CT) imaging by using a computer-aided diagnosis (CAD) system, it is necessary to first determine the lung regions subject to analysis. The lung regions can be selected relatively easily for healthy individuals, by applying a threshold. Selecting an area by using a threshold-based method can be difficult when dealing with lungs with diffuse lung diseases, owing to the abnormal opacities that characterize the diseases.

A sparse representation based method to classify pulmonary patterns of diffuse lung diseases.

We applied and optimized the sparse representation (SR) approaches in the computer-aided diagnosis (CAD) to classify normal tissues and five kinds of diffuse lung disease (DLD) patterns: consolidation, ground-glass opacity, honeycombing, emphysema, and nodule. By using the K-SVD which is based on the singular value decomposition (SVD) and orthogonal matching pursuit (OMP), it can achieve a satisfied recognition rate, but too much time was spent in the experiment. To reduce the runtime of the method, the K-Means algorithm was substituted for the K-SVD, and the OMP was simplified by searching the desired atoms at one time (OMP1).

Texture analysis on (18)F-FDG PET/CT images to differentiate malignant and benign bone and soft-tissue lesions.

Objective: The purpose is to develop and evaluate the ability of the computer-aided diagnosis (CAD) methods that apply texture analysis and pattern classification to differentiate malignant and benign bone and soft-tissue lesions on 18F-fluorodeoxy-glucose positron emission tomography/computed tomography ((18)F-FDG PET/CT) images.

Methods: Subjects were 103 patients with 59 malignant and 44 benign bone and soft tissue lesions larger than 25 mm in diameter. Variable texture parameters of standardized uptake values (SUV) and CT Hounsfield unit values were three-dimensionally calculated in lesional volumes-of-interest segmented on PET/CT images.

Development and evaluation of statistical shape modeling for principal inner organs on torso CT images.

The shapes of the inner organs are important information for medical image analysis. Statistical shape modeling provides a way of quantifying and measuring shape variations of the inner organs in different patients. In this study, we developed a universal scheme that can be used for building the statistical shape models for different inner organs efficiently.

Semantic characteristics prediction of pulmonary nodule using Artificial Neural Networks.

Since it is difficult to choose which computer calculated features are effective to predict the malignancy of pulmonary nodules, in this study, we add a semantic-level of Artificial Neural Networks (ANNs) structure to improve intuition of features selection. The works of this study include two: 1) seeking the relationships between computer-calculated features and medical semantic concepts which could be understood by human; 2) providing an objective assessment method to predict the malignancy from semantic characteristics. We used 60 thoracic CT scans collected from the Lung Image Database Consortium (LIDC) database, in which the suspicious lesions had been delineated and annotated by 4 radiologists independently.

Classification of diffuse lung diseases patterns by a sparse representation based method on HRCT images.

This paper describes a computer-aided diagnosis (CAD) method to classify diffuse lung diseases (DLD) patterns on HRCT images. Due to the high variety and complexity of DLD patterns, the performance of conventional methods on recognizing DLD patterns featured by geometrical information is limited. In this paper, we introduced a sparse representation based method to classify normal tissues and five types of DLD patterns including consolidation, ground-glass opacity, honeycombing, emphysema and nodular.

Particle system based adaptive sampling on spherical parameter space to improve the MDL method for construction of statistical shape models.

Minimum description length (MDL) based group-wise registration was a state-of-the-art method to determine the corresponding points of 3D shapes for the construction of statistical shape models (SSMs). However, it suffered from the problem that determined corresponding points did not uniformly spread on original shapes, since corresponding points were obtained by uniformly sampling the aligned shape on the parameterized space of unit sphere. We proposed a particle-system based method to obtain adaptive sampling positions on the unit sphere to resolve this problem.

Classification of diffuse lung disease patterns on high-resolution computed tomography by a bag of words approach.

Visual inspection of diffuse lung disease (DLD) patterns on high-resolution computed tomography (HRCT) is difficult because of their high complexity. We proposed a bag of words based method on the classification of these textural patters in order to improve the detection and diagnosis of DLD for radiologists. Six kinds of typical pulmonary patterns were considered in this work.

Computer analysis system of the physician-patient consultation process.

Purpose: Measurements of the quality of physician-patient communication are important in assessing patient outcomes, but the quality of communication is difficult to quantify. The aim of this paper is to develop a computer analysis system for the physician-patient consultation process (CASC), which will use a quantitative method to quantify and analyze communication exchanges between physicians and patients during the consultation process.

Design/methodology/approach: CASC is based on the concept of narrative-based medicine using a computer-mediated communication (CMC) technique from a cognitive dialog processing system.

Computer-aided diagnosis of lung cancer: definition and detection of ground-glass opacity type of nodules by high-resolution computed tomography.

Purpose: The ground-glass opacity (GGO) of lung cancer is identified only subjectively on computed tomography (CT) images as no quantitative characteristic has been defined for GGOs. We sought to define GGOs quantitatively and to differentiate between GGOs and solid-type lung cancers semiautomatically with a computer-aided diagnosis (CAD).

Methods And Materials: High-resolution CT images of 100 pulmonary nodules (all peripheral lung cancers) were collected from our clinical records.

Variation and phylogeny of Fusarium oxysporum isolates based on nucleotide sequences of polygalacturonase genes.

The nucleotide sequences of two endopolygalacturonase genes (pg1 and pg5) and two exopolygalacturonase genes (pgx1 and pgx4), which encode members of a major family of secreted cell-wall-degrading enzymes (CWDEs), were compared to detect the extent of genetic variation among isolates of Fusarium oxysporum. The nucleotide variation rate in exons was 0.23-0.