Shinsuke Saita

An extraction algorithm of pulmonary fissures from multislice CT image

Aging and smoking history increases number of pulmonary emphysema. Alveoli restoration destroyed by pulmonary emphysema is difficult and early direction is important. Multi-slice CT technology has been improving 3-D image analysis with higher body axis resolution and shorter scan time. And low-dose high accuracy scanning becomes available. Multi-slice CT image helps physicians with accurate measuring but huge volume of the image data takes time and cost. This paper is intended for computer added emphysema region analysis and proves effectiveness of proposed algorithm.

Influence of slice thickness on diagnoses of pulmonary nodules using low-dose CT: potential dependence of detection and diagnostic agreement on features and location of nodule.

RATIONALE AND OBJECTIVES The aims of this study were to assess the influence of slice thickness on the ability of radiologists to detect or not detect nodules and to agree or disagree on the diagnosis and also to investigate the potential dependence of these relations on the sizes, average computed tomographic (CT) values, and locations of the nodules. MATERIALS AND METHODS Six radiologists performed qualitative diagnostic readings of multislice CT images with a slice thickness of 2 or 10 mm obtained from 360 subjects. The nodules were diagnosed as nodules for further examination (NFEs), inactive nodules for no further examination (INNFEs), or no abnormality. The results of the diagnoses were cross-tabulated and quantitatively analyzed using the average CT values, sizes, and locations of the nodules with reference to the 2-mm slices. Multivariate logistic regression analyses were used to estimate the significant associations of these parameters with the ability of radiologists to detect or not detect nodules and to agree or disagree on the diagnosis. RESULTS Totals of 130 NFEs and 403 INNFEs for 2-mm slice thickness and 142 NFEs and 338 INNFEs for 10-mm slice thickness were diagnosed. Nodule classifications were as follows: the same diagnosis on both slice thickness images (67.6%), different diagnoses on two slice thickness images (21%), missed on 10-mm slice thickness images (10.6%), and misinterpreted on 10-mm slice thickness images (0.7%). Regarding detection and nondetection, NFE diagnoses were influenced by size (odds ratio [OR], 132.50; 95% confidence interval [CI], 4.77-4711) and the average CT value (OR, 27.20; 95% CI, 3.21-645.3), and INNFE diagnoses were influenced by size (OR, 16.10; 95% CI, 6.18-55.19) and the average CT value (OR, 7.67; 95% CI, 2.19-30.91). Regarding diagnostic agreement and disagreement, the NFE diagnoses were influenced by size (OR, 3.60; 95% CI, 1.29-11.04), nodule distance from the lung border (OR, 2.85; 95% CI, 1.27-6.65), and nodule location in the right upper lobe (OR, 0.07; 95% CI, 0.003-0.477), while the INNFE diagnoses were influenced by the average CT value (OR, 11.84; 95% CI, 3.33-55.86), size (OR, 0.42; 95% CI, 0.25-0.70), and nodule distance from the lung border (OR, 0.41; 95% CI, 0.25-0.66). CONCLUSIONS The influence of slice thickness on the ability of radiologists to detect or not detect nodules and to agree or disagree on the diagnosis was quantitatively evaluated. Detection and nondetection of NFEs and INNFEs are influenced by size and average CT value. Agreement and disagreement on NFE and INNFE diagnoses are influenced not only by size and average CT value but also, importantly, by the locations of nodules.

Original investigationInfluence of Slice Thickness on Diagnoses of Pulmonary Nodules Using Low-dose CT: Potential Dependence of Detection and Diagnostic Agreement on Features and Location of Nodule

RATIONALE AND OBJECTIVES The aims of this study were to assess the influence of slice thickness on the ability of radiologists to detect or not detect nodules and to agree or disagree on the diagnosis and also to investigate the potential dependence of these relations on the sizes, average computed tomographic (CT) values, and locations of the nodules. MATERIALS AND METHODS Six radiologists performed qualitative diagnostic readings of multislice CT images with a slice thickness of 2 or 10 mm obtained from 360 subjects. The nodules were diagnosed as nodules for further examination (NFEs), inactive nodules for no further examination (INNFEs), or no abnormality. The results of the diagnoses were cross-tabulated and quantitatively analyzed using the average CT values, sizes, and locations of the nodules with reference to the 2-mm slices. Multivariate logistic regression analyses were used to estimate the significant associations of these parameters with the ability of radiologists to detect or not detect nodules and to agree or disagree on the diagnosis. RESULTS Totals of 130 NFEs and 403 INNFEs for 2-mm slice thickness and 142 NFEs and 338 INNFEs for 10-mm slice thickness were diagnosed. Nodule classifications were as follows: the same diagnosis on both slice thickness images (67.6%), different diagnoses on two slice thickness images (21%), missed on 10-mm slice thickness images (10.6%), and misinterpreted on 10-mm slice thickness images (0.7%). Regarding detection and nondetection, NFE diagnoses were influenced by size (odds ratio [OR], 132.50; 95% confidence interval [CI], 4.77-4711) and the average CT value (OR, 27.20; 95% CI, 3.21-645.3), and INNFE diagnoses were influenced by size (OR, 16.10; 95% CI, 6.18-55.19) and the average CT value (OR, 7.67; 95% CI, 2.19-30.91). Regarding diagnostic agreement and disagreement, the NFE diagnoses were influenced by size (OR, 3.60; 95% CI, 1.29-11.04), nodule distance from the lung border (OR, 2.85; 95% CI, 1.27-6.65), and nodule location in the right upper lobe (OR, 0.07; 95% CI, 0.003-0.477), while the INNFE diagnoses were influenced by the average CT value (OR, 11.84; 95% CI, 3.33-55.86), size (OR, 0.42; 95% CI, 0.25-0.70), and nodule distance from the lung border (OR, 0.41; 95% CI, 0.25-0.66). CONCLUSIONS The influence of slice thickness on the ability of radiologists to detect or not detect nodules and to agree or disagree on the diagnosis was quantitatively evaluated. Detection and nondetection of NFEs and INNFEs are influenced by size and average CT value. Agreement and disagreement on NFE and INNFE diagnoses are influenced not only by size and average CT value but also, importantly, by the locations of nodules.

Classification algorithm of pulmonary vein and artery based on multi-slice CT image

Recently, multi-slice helical CT technology was developed. Unlike the conventional helical CT, we can obtain CT images of two or more slices with 1 time scan. Therefore, we can get many pictures with a clear contrast images and thin slice images in one time of scanning. The purpose of this presentation is to evaluate the proposed automatic extraction bronchus and pulmonary vein and artery on multi-slice CT images. The bronchus is extracted by application with region growing technique and the morphological filters, 3D distance transformation. These results indicate that the proposed algorithm provides the ability to accurately develop an automatic extraction algorithm of the bronchus on multi-slice CT images. In this report, we used pulmonary vein and artery marked by the doctor, It aims to discover an amount of the feature necessary for classifying the pulmonary vein and artery by using the anatomical feature. The classification of the pulmonary vein and artery is thought necessary information that it is state of tuber benign or malignity judgment. It is very important to separate the contact part of the lung blood vessel in classifying pulmonary vein and artery. Then, it aims to discover the feature of the contact part of the lung blood vessel in this report.

An automated distinction of DICOM images for lung cancer CAD system

Automated distinction of medical images is an important preprocessing in Computer-Aided Diagnosis (CAD) systems. The CAD systems have been developed using medical image sets with specific scan conditions and body parts. However, varied examinations are performed in medical sites. The specification of the examination is contained into DICOM textual meta information. Most DICOM textual meta information can be considered reliable, however the body part information cannot always be considered reliable. In this paper, we describe an automated distinction of DICOM images as a preprocessing for lung cancer CAD system. Our approach uses DICOM textual meta information and low cost image processing. Firstly, the textual meta information such as scan conditions of DICOM image is distinguished. Secondly, the DICOM image is set to distinguish the body parts which are identified by image processing. The identification of body parts is based on anatomical structure which is represented by features of three regions, body tissue, bone, and air. The method is effective to the practical use of lung cancer CAD system in medical sites.

Algorithm of pulmonary emphysema extraction using thoracic 3-D CT images

Recently, due to aging and smoking, emphysema patients are increasing. The restoration of alveolus which was destroyed by emphysema is not possible, thus early detection of emphysema is desired. We describe a quantitative algorithm for extracting emphysematous lesions and quantitatively evaluate their distribution patterns using low dose thoracic 3-D CT images. The algorithm identified lung anatomies, and extracted low attenuation area (LAA) as emphysematous lesion candidates. Applying the algorithm to thoracic 3-D CT images and then by follow-up 3-D CT images, we demonstrate its potential effectiveness to assist radiologists and physicians to quantitatively evaluate the emphysematous lesions distribution and their evolution in time interval changes.

Algorithm of pulmonary emphysema extraction using low dose thoracic 3D CT images

Recently, due to aging and smoking, emphysema patients are increasing. The restoration of alveolus which was destroyed by emphysema is not possible, thus early detection of emphysema is desired. We describe a quantitative algorithm for extracting emphysematous lesions and quantitatively evaluate their distribution patterns using low dose thoracic 3-D CT images. The algorithm identified lung anatomies, and extracted low attenuation area (LAA) as emphysematous lesion candidates. Applying the algorithm to 100 thoracic 3-D CT images and then by follow-up 3-D CT images, we demonstrate its potential effectiveness to assist radiologists and physicians to quantitatively evaluate the emphysematous lesions distribution and their evolution in time interval changes.

Computer aided diagnosis of osteoporosis using multi-slice CT images

The patients of osteoporosis comprised about 11 million people in Japan and it is one of the problems the aging society has. In order to prevent the osteoporosis, it is necessary to do early detection and treatment. The development of Multislice CT technology made it possible to perform the three dimensional (3-D) image analysis with higher body axis resolution and shorter scan time. The 3-D image analysis using multi-slice CT images of thoracic vertebra can be used as a support to diagnose osteoporosis and at the same time can be used for lung cancer screening which may lead to its early detection. We develop an automatic extraction algorithm of vertebra, and the analysis algorithm of the vertebral body using shape analysis and a bone density measurement for the computer aided diagnosis of osteoporosis.

Feature calculation for classification algorithm of pulmonary vein and artery

Multi-slice helical CT technology has been developed. Unlike the conventional helical CT, we can obtain CT images of two or more slices in 1 time scan. Therefore, we can get many images with a clear contrast and thin slice images in one time of scanning. The purpose of this presentation is to evaluate the proposed automatic extraction bronchus and pulmonary vein and artery on multi-slice CT images. The bronchus is extracted by application with region growing technique and the morphological filters, 3D distance transformation. These results indicate that the proposed algorithm provides the ability to accurately develop an automatic extraction algorithm of the bronchus on multi-slice CT images. In this report, we used pulmonary vein and artery marked by the doctor, It aims to discover an amount of the feature necessary for classifying the pulmonary vein and artery by using the anatomical feature. The classification of the pulmonary vein and artery is thought to be a necessary information for tumors benign or malignity judgment. In this report, the amount of the feature in which the flow of the automation is based is analyzed by using three dimension images of pulmonary vein and artery and bronchus obtained by the specialized physicians marking.

Automated anatomical labeling algorithm of bronchial branches based on multi-slice CT images

Multi-slice CT technology was developed, so, we can get clear contrast images and thin slice images. But doctors need to diagnosis many image, thus their load increases. Therefore, development of the algorithm that analyses lung internal-organs is expected. When doctors diagnose lung internal-organs, they understand it. So, detailed analyze of lung internal-organs is applicant to early detection of a nodule. Especially, analyzing bronchus provides that useful information of detection of airway disease and classification of the pulmonary vein and artery. In this paper, we describe a method for automated anatomical labeling algorithm of bronchial branches based on Multi-Slice CT images.