Tatsuro Hayashi

Automatic recognition of lung lobes and fissures from multislice CT images

Computer-aided diagnosis (CAD) has been expected to help radiologists to improve the accuracy of abnormality detection and reduce the burden during CT image interpretations. In order to realize such functions, automated segmentations of the target organ regions are always required by CAD systems. This paper describes a fully automatic processing procedure, which is designed to identify inter-lobe fissures and divide lung into five lobe regions. The lung fissures are disappeared very fuzzy and indefinite in CT images, so that it is very difficult to extract fissures directly based on its CT values. We propose a method to solve this problem using the anatomy knowledge of human lung. We extract lung region firstly and then recognize the structures of lung vessels and bronchus. Based on anatomy knowledge, we classify the vessels and bronchus on a lobe-by-lobe basis and estimate the boundary of each lobe region as the initial fissure locations. Within those locations, we extract lung fissures precisely based on an edge detection method and divide lung regions into five lung lobes lastly. The performance of the proposed method was evaluated using 9 patient cases of high-resolution multi-slice chest CT images; the improvement has been confirmed with the reliable recognition results.

Classification of teeth in cone-beam CT using deep convolutional neural network

Dental records play an important role in forensic identification. To this end, postmortem dental findings and teeth conditions are recorded in a dental chart and compared with those of antemortem records. However, most dentists are inexperienced at recording the dental chart for corpses, and it is a physically and mentally laborious task, especially in large scale disasters. Our goal is to automate the dental filing process by using dental x-ray images. In this study, we investigated the application of a deep convolutional neural network (DCNN) for classifying tooth types on dental cone-beam computed tomography (CT) images. Regions of interest (ROIs) including single teeth were extracted from CT slices. Fifty two CT volumes were randomly divided into 42 training and 10 test cases, and the ROIs obtained from the training cases were used for training the DCNN. For examining the sampling effect, random sampling was performed 3 times, and training and testing were repeated. We used the AlexNet network architecture provided in the Caffe framework, which consists of 5 convolution layers, 3 pooling layers, and 2 full connection layers. For reducing the overtraining effect, we augmented the data by image rotation and intensity transformation. The test ROIs were classified into 7 tooth types by the trained network. The average classification accuracy using the augmented training data by image rotation and intensity transformation was 88.8%. Compared with the result without data augmentation, data augmentation resulted in an approximately 5% improvement in classification accuracy. This indicates that the further improvement can be expected by expanding the CT dataset. Unlike the conventional methods, the proposed method is advantageous in obtaining high classification accuracy without the need for precise tooth segmentation. The proposed tooth classification method can be useful in automatic filing of dental charts for forensic identification.

Analysis of bone mineral density distribution at trabecular bones in thoracic and lumbar vertebrae using X-ray CT images.

The number of participants in thoracic or abdominal examinations using multi-detector-row CT (MDCT) has been increasing recently. If the degree of progress of osteoporosis can be estimated using these images, it may be useful as it will allow predictions of vertebral fractures without an additional radiation exposure. The aims of this study were to investigate segmental variations in bone mineral density (BMD) distributions of thoracic and lumbar vertebral bodies and to show specific differences according to age and gender. A large database including 1,031 Japanese subjects for whom MDCT was used to examine various organs and tissues was utilized in this study for trabecular BMD at thoracic and lumbar vertebrae. In relationship to vertebral level, L3 had the lowest trabecular BMD. BMD tended to gradually increase from L3 to T1 in all age categories. Also, there was a moderate correlation between vertebrae whose distance from each other was great whereas there was a high correlation between adjacent vertebrae. It may be appropriate to use an arbitrary vertebra as a first approximation for assessing vertebrae that are in the area of predilection for the fracture; however, to better understand their behavior, it may be necessary to measure BMD directly in this region. This study showed trabecular BMD distribution at healthy thoracic and lumbar vertebrae in Japanese subjects and specific differences in age and gender. Improved knowledge about vertebral BMD may help with the diagnosis of primary osteoporosis using MDCT.

Automated scheme for measuring mandibular cortical thickness on dental panoramic radiographs for osteoporosis screening

Findings of dental panoramic radiographs (DPRs) have shown that the mandibular cortical thickness (MCT) was significantly correlated with osteoporosis. Identifying asymptomatic patients with osteoporosis through dental examinations may bring a supplemental benefit for the patients. However, most of the DPRs are used for only diagnosing dental conditions by dentists in their routine clinical work. The aim of this study was to develop a computeraided diagnosis scheme that automatically measures MCT to assist dentists in screening osteoporosis. First, the inferior border of mandibular bone was detected by use of an active contour method. Second, the locations of mental foramina were estimated on the basis of the inferior border of mandibular bone. Finally, MCT was measured on the basis of the grayscale profile analysis. One hundred DPRs were used to evaluate our proposed scheme. Experimental results showed that the sensitivity and specificity for identifying osteoporotic patients were 92.6 % and 100 %, respectively. We conducted multiclinic trials, in which 223 cases have been obtained and processed in about a month. Our scheme succeeded in detecting all cases of suspected osteoporosis. Therefore, our scheme may have a potential to identify osteoporotic patients at an early stage.

Automated segmentation and recognition of the bone structure in non-contrast torso CT images using implicit anatomical knowledge

X-ray CT images have been widely used in clinical diagnosis in recent years. A modern CT scanner can generate about 1000 CT slices to show the details of all the human organs within 30 seconds. However, CT image interpretations (viewing 500-1000 slices of CT images manually in front of a screen or films for each patient) require a lot of time and energy. Therefore, computer-aided diagnosis (CAD) systems that can support CT image interpretations are strongly anticipated. Automated recognition of the anatomical structures in CT images is a basic pre-processing of the CAD system. The bone structure is a part of anatomical structures and very useful to act as the landmarks for predictions of the other different organ positions. However, the automated recognition of the bone structure is still a challenging issue. This research proposes an automated scheme for segmenting the bone regions and recognizing the bone structure in noncontrast torso CT images. The proposed scheme was applied to 48 torso CT cases and a subjective evaluation for the experimental results was carried out by an anatomical expert following the anatomical definition. The experimental results showed that the bone structure in 90% CT cases have been recognized correctly. For quantitative evaluation, automated recognition results were compared to manual inputs of bones of lower limb created by an anatomical expert on 10 randomly selected CT cases. The error (maximum distance in 3D) between the recognition results and manual inputs distributed from 3-8 mm in different parts of the bone regions.

Lung structure recognition: a further study of thoracic organ recognitions based on CT images

Abstract We are developing a computer-aided diagnosis (CAD) system for extracting and recognizing thoracic organ regions from chest CT images. In a previous study, we have shown that this system can automatically recognize nine kinds of human organ and tissue regions from multi-slice CT images and provide some useful applications for visualizing CT images three-dimensionally (3-D) or two-dimensionally (2-D) based on preliminary recognition results. In this paper, we propose some further studies for this system that focus on lung structure recognition. These studies include: (1) identification of thoracic cage region, (2) extraction of major and minor fissures and classification of lung regions, (3) classification of lung surface and identification of hilus pulmonis. The proposed methods have been applied to three chest CT images for recognizing lung structure and have demonstrated promising results.

An automatic detection method for carotid artery calcifications using top-hat filter on dental panoramic radiographs

The purpose of this study is to develop an automated carotid artery calcification (CAC) detection scheme on dental panoramic radiographs (DPRs). The CAC is one of the indices for predicting the risk of arteriosclerosis. First, regions of interest (ROIs) that include CACs were determined on the basis of inflection points of the mandibular contour. Initial CAC candidates were detected by using a grayscale top-hat filter and simple grayscale thresholding technique. Finally, a rule-based approach and support vector machine to reduce the number of false positive (FP) findings were applied using features such as area, location, and circularity. Thirty-four DPRs were used to evaluate the proposed scheme. The sensitivity for the detection of CACs was 93.6% with 4.4 FPs per image. Experimental results showed that our computer-aided detection scheme may be useful to detect CACs.

Automated contralateral subtraction of dental panoramic radiographs for detecting abnormalities in paranasal sinus

Inflammation in the paranasal sinus is often observed in seasonal allergic rhinitis or with colds, but is also an indication for odontogenic tumors, carcinoma of the maxillary sinus or a maxillary cyst. The detection of those findings in dental panoramic radiographs is not difficult for radiologists, but general dentists may miss the findings since they focus on treatments of teeth. The purpose of this work is to develop a contralateral subtraction method for detecting the odontogenic sinusitis region on dental panoramic radiographs. We developed a contralateral subtraction technique in paranasal sinus region, consisting of 1) image filtering of the smoothing and sobel operation for noise reduction and edge extraction, 2) image registration of mirrored image by using mutual information, and 3) image display method of subtracted pixel data. We employed 56 cases (24 normal and 32 abnormal). The abnormal regions and the normal cases were verified by a board-certified radiologist using CT scans. Observer studies with and without subtraction images were performed for 9 readers. The true-positive rate at a 50% confidence level in 7 out of 9 readers was improved, but there was no statistical significance in the difference of area-under-curve (AUC) in each radiologist. In conclusion, the contralateral subtraction images of dental panoramic radiographs may improve the detection rate of abnormal regions in paranasal sinus.

Sophisticated Imaging Technology in the Assessment of Osteoporosis Risk

Osteoporosis is a common disease characterized by low bone mass and microstructural deterioration of bone tissue, with an increased fracture risk. With an aging population, osteoporosis and its related fractures have become an increasingly important health and socioeconomic issue. The aim of osteoporosis screening and treatment is to prevent bone fracture. A fracture occurs when the external force applied to a bone exceeds its strength. The ability of a bone to resist fracture depends on its amount, spatial distribution, and intrinsic properties. Sophisticated bone imaging techniques, as new modalities, improve the potential for non-invasive study of bone anatomy, physiology and pathophysiology. The objective of bone imaging in osteoporosis is to minimize fracture occurrence by identifying the osteoporotic process at an early stage, differentiate distinctive patterns of bone loss, predict fracture risk accurately and monitor treatment response precisely. Non-invasive imaging techniques, such as computed tomography (CT) and magnetic resonance imaging (MRI), provide structural information, beyond bone mineral density (BMD). Non-invasive or non-destructive imaging techniques can provide important structural information about the local and systemic skeletal status and about the propensity to fracture. These advanced imaging techniques provide information about bone beyond standard bone mineral densitometry. In this chapter, we will discuss recent progress in bone imaging in a range from the macroto micro-structures in order to investigate the structural basis of the skeletal fragility underlying osteoporosis.

A new screening pathway for identifying asymptomatic patients using dental panoramic radiographs

To identify asymptomatic patients is the challenging task and the essential first step in diagnosis. Findings of dental panoramic radiographs include not only dental conditions but also radiographic signs that are suggestive of possible systemic diseases such as osteoporosis, arteriosclerosis, and maxillary sinusitis. Detection of such signs on panoramic radiographs has a potential to provide supplemental benefits for patients. However, it is not easy for general dental practitioners to pay careful attention to such signs. We addressed the development of a computer-aided detection (CAD) system that detects radiographic signs of pathology on panoramic images, and the design of the framework of new screening pathway by cooperation of dentists and our CAD system. The performance evaluation of our CAD system showed the sensitivity and specificity in the identification of osteoporotic patients were 92.6 % and 100 %, respectively, and those of the maxillary sinus abnormality were 89.6 % and 73.6 %, respectively. The detection rate of carotid artery calcifications that suggests the need for further medical evaluation was approximately 93.6 % with 4.4 false-positives per image. To validate the utility of the new screening pathway, preliminary clinical trials by using our CAD system were conducted. To date, 223 panoramic images were processed and 4 asymptomatic patients with suspected osteoporosis, 7 asymptomatic patients with suspected calcifications, and 40 asymptomatic patients with suspected maxillary sinusitis were detected in our initial trial. It was suggested that our new screening pathway could be useful to identify asymptomatic patients with systemic diseases.