Computer aided diagnosis for severity assessment of pneumoconiosis using CT images

240,000 participants have a screening for diagnosis of pneumoconiosis every year in Japan. Radiograph is used for staging of severity in pneumoconiosis worldwide. This paper presents a method for quantitative assessment of severity in pneumoconiosis using both size and frequency of lung nodules that detected by thin-section CT images. This method consists of three steps. First, thoracic organs (body, ribs, spine, trachea, bronchi, lungs, heart, and pulmonary blood vessels) are segmented. Second, lung nodules that have radius over 1.5mm are detected. These steps used functions of our developed computer aided detection system of chest CT images. Third, severity in pneumoconiosis is quantified using size and frequency of lung nodules. This method was applied to nine pneumoconiosis patients. The initial results showed that proposed method can assess severity in pneumoconiosis quantitatively. This paper demonstrates effectiveness of our method in diagnosis and prognosis of pneumoconiosis in CT screening

Segmentation of aorta and main pulmonary artery of non-contrast CT images using U-Net for chronic thromboembolic pulmonary hypertension : evaluation of robustness to contacts with blood vessels

Enlargement of the pulmonary artery is a morphological abnormality of pulmonary hypertension patients. Diameters of the aorta and main pulmonary artery (MPA) are useful for predicting the presence of pulmonary hypertension. A major problem in the automatic segmentation of the aorta and MPA from non-contrast CT images is the invisible boundary caused by contact with blood vessels. In this study, we applied U-Net to the segmentation of the aorta and MPA from non-contrast CT images for normal and chronic thromboembolic pulmonary hypertension (CTEPH) cases and evaluated the robustness to the contacts between blood vessels. Our approach of the segmentation consists of three steps: (1) detection of trachea branch point, (2) cropping region of interest centered to the trachea branch point, and (3) segmentation of the aorta and MPA using U-Net. The segmentation performances were compared in seven methods: 2D U-Net, 2D U-Net with pre-trained VGG-16 encoder, 2D U-Net with pre-trained VGG-19 encoder, 2D Attention U-Net, 3D U-Net, an ensemble method of them, and our conventional method. The aorta and MPA segmentation methods using these U-Net achieved higher performance than a conventional method. Although the contact boundaries of blood vessels caused lower performance compared with the non-contact boundaries, the mean boundary distances were below about one pixel

Visualization and unsupervised clustering of emphysema progression using t-SNE analysis of longitudinal CT images and SNPs

Chronic obstructive pulmonary disease (COPD) is predicted to become the third leading cause of death worldwide by 2030. A longitudinal study using CT scans of COPD is useful to assess the changes in structural abnormalities. In this study, we performed visualization and unsupervised clustering of emphysema progression using t-distributed stochastic neighbor embedding (t-SNE) analysis of longitudinal CT images, smoking history, and SNPs. The procedure of this analysis is as follows: (1) automatic segmentation of lung lobes using 3D U-Net, (2) quantitative image analysis of emphysema progression in lung lobes, and (3) visualization and unsupervised clustering of emphysema progression using t-SNE. Nine explanatory variables were used for the clustering: genotypes at two SNPs (rs13180 and rs3923564), smoking history (smoking years, number of cigarettes per day, pack-year), and LAV distribution (LAV size and density in upper lobes, LAV size, and density in lower lobes). The objective variable was emphysema progression which was defined as the annual change in low attenuation volume (LAV%/year) using linear regression. The nine-dimensional space was transformed to two-dimensional space by t-SNE, and divided into three clusters by Gaussian mixture model. This method was applied to 37 smokers with 68.2 pack-years and 97 past smokers with 51.1 pack-years. The results demonstrated that this method could be effective for quantitative assessment of emphysema progression by SNPs, smoking history, and imaging features

Association analysis of SNPs with CT image-based phenotype of emphysema progression in heavy smokers

Chronic obstructive pulmonary disease (COPD) is predicted to become the third leading cause of death worldwide by 2030. Smoking is a well-known risk factor in the development of COPD. Association between COPD genes and smoking have been studied. This paper presents an association analysis of single nucleotide polymorphisms (SNPs) with a CT image-based phenotype of emphysema progression in heavy smokers. The emphysema progression was quantitatively represented by the annual increment of low attenuation volume (LAV) on CT scans for five years. 10 candidate SNPs were selected from 316 SNPs in 125 papers of genetic studies of COPD and lung cancer. The genotypes were determined by real-time polymerase chain reaction (PCR) using deoxyribonucleic acid (DNA) extracted from saliva samples. The association analysis was performed by Fisher's exact test and logistic regression analysis. This method was applied to a dataset with 144 participants (71 smokers, 61 past smokers, and 12 non-smokers). The results showed that the genotypes of rs3923564 and rs13180 SNPs were candidate SNPs associated with the CT image based-emphysema progression

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近年, 癌の光力学療法(PDT)が注目され, 精力的な研究が広範に進められている。現在, PDTの臨床試験に用いられている光増感剤としては, ヘマトポルフィリン誘導体(Hpd)が挙げられる。しかしながら, このHpdは, 高い組織透過性を有する600nm以上の可視光に対して弱い吸収を示すにとどまり, さらにHpd中の活性成分が腫瘍組織に取り込まれる割合は, 正常組織に比べてけた外れに大きいわけではない。したがって, より効果的なPDTを実施するにあたり, 適用される光増感剤としては, 組織透過性の高い赤色光領域に強い吸収を持ち, 腫瘍組織に対して高い選択性および親和性を具備するものが望まれる。このような理由で, 最近, 多種多様な新しい光増感剤が登場してきた。なかでも, メソ置換ポルフィリン, クロリン誘導体, バクテリオクロリンおよびフタロシアニンが, PDTにおける第二世代光増感剤として注目を集めている。There is currently worldwide activity in the development of a photodynamic therapy (PDT) for cancer. The sensitizer currently used in clinical trials of PDT for cancer consists of a mixture of hematoporphyrin derivatives (Hpd). Hpd, however, absorbs only weakly above 600nm where light exhibits the deepest penetration into tissues. Furthermore, the uptake by the tumour of the active component in Hpd is only marginally higher than that by most normal tissues. Thus it has been evident for some time that the effectiveness of PDT could be enhanced by the use of photosensitizers that absorb more strongly towards the red end of the light spectrum and are more selectively retained by neoplastic tissues. For these reasons, several new classes of photosensitizers for PDT have been suggested over the past few years. Among them, substituted porphyrins, chlorin analogs, bacteriochlorins, and phtalocyanines have received increasing attention as a second- gereration photosensitizer in PDT

Technical Note: Gray tracking in medical color displays-A report of Task Group 196

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