Soichiro Miki

Development of automatic visceral fat volume calculation software for CT volume data.

Objective. To develop automatic visceral fat volume calculation software for computed tomography (CT) volume data and to evaluate its feasibility. Methods. A total of 24 sets of whole-body CT volume data and anthropometric measurements were obtained, with three sets for each of four BMI categories (under 20, 20 to 25, 25 to 30, and over 30) in both sexes. True visceral fat volumes were defined on the basis of manual segmentation of the whole-body CT volume data by an experienced radiologist. Software to automatically calculate visceral fat volumes was developed using a region segmentation technique based on morphological analysis with CT value threshold. Automatically calculated visceral fat volumes were evaluated in terms of the correlation coefficient with the true volumes and the error relative to the true volume. Results. Automatic visceral fat volume calculation results of all 24 data sets were obtained successfully and the average calculation time was 252.7 seconds/case. The correlation coefficients between the true visceral fat volume and the automatically calculated visceral fat volume were over 0.999. Conclusions. The newly developed software is feasible for calculating visceral fat volumes in a reasonable time and was proved to have high accuracy.

Automatic detection of over 100 anatomical landmarks in medical CT images: A framework with independent detectors and combinatorial optimization

&NA; An automatic detection method for 197 anatomically defined landmarks in computed tomography (CT) volumes is presented. The proposed method can handle missed landmarks caused by detection failure, a limited imaging range and other problems using a novel combinatorial optimization framework with a two‐stage sampling algorithm. After a list of candidates is generated by each landmark detector, the best combination of candidates is searched for by a combinatorial optimization algorithm using a landmark point distribution model (L‐PDM) to provide prior knowledge. Optimization is performed by simulated annealing and iterative Gibbs sampling. Prior to each cycle of Gibbs sampling, another sampling algorithm is processed to estimate the spatial distribution of each target landmark, so that landmark positions without any correct detector‐derived candidates can be estimated. The proposed method was evaluated using 104 CT volumes with various imaging ranges. The overall average detection distance error was 6.6 mm, and 83.8, 93.2 and 96.5% of landmarks were detected within 10, 15 and 20 mm from the ground truth, respectively. The proposed method worked even when most of the landmarks were outside of the imaging range. The identification accuracy of the vertebral centroid was also evaluated using public datasets and the proposed method could identify 70% of vertebrae including severely diseased ones. From these results, the feasibility of our framework in detecting multiple landmarks in various CT datasets was validated. HighlightsNovel landmark detection framework for over 100 anatomical landmarks.Handling missing landmarks caused by detection failure or a limited imaging range.Estimation of the positions of missing landmarks.A stochastic model of a general landmark detector.Validation with various CT datasets, e.g. different imaging ranges. Graphical abstract Figure. Image, graphical abstract

Hemosuccus Pancreaticus in a Patient with Iodine Allergy: Successful Diagnosis with Magnetic Resonance Imaging and Treatment with Transarterial Embolization Using Carbon Dioxide as the Contrast Medium

Hemosuccus pancreaticus (HP) is defined as gastrointestinal bleeding via the pancreatic duct and duodenal papilla. Since the bleeding is usually intermittent, it often remains undetected by endoscopy. Most cases are diagnosed by contrast-enhanced computed tomography (CT) or angiography, and the first-line treatment is transarterial embolization (TAE). However, in general, these modalities require a large amount of iodinated contrast medium. Here, we report the case of a 50-year-old female with HP due to chronic pancreatitis. Contrast-enhanced CT and ordinary angiography were contraindicated for her, as she was allergic to iodine. She was diagnosed with HP following gadolinium-enhanced magnetic resonance imaging and successfully treated by TAE of the splenic artery with metallic coils using carbon dioxide as the contrast medium.

The effect of expert knowledge on medical search: medical experts have specialized abilities for detecting serious lesions

How does domain-specific knowledge influence the experts’ performance in their domain of expertise? Specifically, can visual search experts find, with uniform efficiency, any type of target in their domain of expertise? We examined whether acquired knowledge of target importance influences an expert’s visual search performance. In some professional searches (e.g., medical screenings), certain targets are rare; one aim of this study was to examine the extent to which experts miss such targets in their searches. In one experiment, radiologists (medical experts) engaged in a medical lesion search task in which both the importance (i.e., seriousness/gravity) and the prevalence of targets varied. Results showed decreased target detection rates in the low prevalence conditions (i.e., the prevalence effect). Also, experts were better at detecting important (versus unimportant) lesions. Results of an experiment using novices ruled out the possibility that decreased performance with unimportant targets was due to low target noticeability/visibility. Overall, the findings suggest that radiologists do not have a generalized ability to detect any type of lesion; instead, they have acquired a specialized ability to detect only those important lesions relevant for effective medical practices.

Deep neural network-based computer-assisted detection of cerebral aneurysms in MR angiography

The usefulness of computer‐assisted detection (CAD) for detecting cerebral aneurysms has been reported; therefore, the improved performance of CAD will help to detect cerebral aneurysms.

Feasibility Study of a Generalized Framework for Developing Computer-Aided Detection Systems—a New Paradigm

We propose a generalized framework for developing computer-aided detection (CADe) systems whose characteristics depend only on those of the training dataset. The purpose of this study is to show the feasibility of the framework. Two different CADe systems were experimentally developed by a prototype of the framework, but with different training datasets. The CADe systems include four components; preprocessing, candidate area extraction, candidate detection, and candidate classification. Four pretrained algorithms with dedicated optimization/setting methods corresponding to the respective components were prepared in advance. The pretrained algorithms were sequentially trained in the order of processing of the components. In this study, two different datasets, brain MRA with cerebral aneurysms and chest CT with lung nodules, were collected to develop two different types of CADe systems in the framework. The performances of the developed CADe systems were evaluated by threefold cross-validation. The CADe systems for detecting cerebral aneurysms in brain MRAs and for detecting lung nodules in chest CTs were successfully developed using the respective datasets. The framework was shown to be feasible by the successful development of the two different types of CADe systems. The feasibility of this framework shows promise for a new paradigm in the development of CADe systems: development of CADe systems without any lesion specific algorithm designing.

Effects of Iterative Reconstruction Algorithms on Computer-assisted Detection (CAD) Software for Lung Nodules in Ultra-low-dose CT for Lung Cancer Screening

RATIONALE AND OBJECTIVES This study aimed to evaluate the effects of iterative reconstruction (IR) algorithms on computer-assisted detection (CAD) software for lung nodules in ultra-low-dose computed tomography (ULD-CT) for lung cancer screening. MATERIALS AND METHODS We selected 85 subjects who underwent both a low-dose CT (LD-CT) scan and an additional ULD-CT scan in our lung cancer screening program for high-risk populations. The LD-CT scans were reconstructed with filtered back projection (FBP; LD-FBP). The ULD-CT scans were reconstructed with FBP (ULD-FBP), adaptive iterative dose reduction 3D (AIDR 3D; ULD-AIDR 3D), and forward projected model-based IR solution (FIRST; ULD-FIRST). CAD software for lung nodules was applied to each image dataset, and the performance of the CAD software was compared among the different IR algorithms. RESULTS The mean volume CT dose indexes were 3.02 mGy (LD-CT) and 0.30 mGy (ULD-CT). For overall nodules, the sensitivities of CAD software at 3.0 false positives per case were 78.7% (LD-FBP), 9.3% (ULD-FBP), 69.4% (ULD-AIDR 3D), and 77.8% (ULD-FIRST). Statistical analysis showed that the sensitivities of ULD-AIDR 3D and ULD-FIRST were significantly higher than that of ULD-FBP (P < .001). CONCLUSIONS The performance of CAD software in ULD-CT was improved by using IR algorithms. In particular, the performance of CAD in ULD-FIRST was almost equivalent to that in LD-FBP.

Training Strategy for Performance Improvement in Computer-Assisted Detection of Lesions: Based on Multi-institutional Study in Teleradiology Environment

The performance of computer-assisted detection (CAD) software depends on quality and quantity of the dataset used for supervised learning. If the data characteristics in development and practical use are different, the performance of CAD software will be degraded. Therefore, it is necessary to continuously collect data for supervised learning in practical use, and to improve CAD software by retraining with the collected data. For this purpose, we developed a web-based CAD software processing and evaluation platform (CIRCUS CS), which provides on-line processing of CAD software and interfaces to evaluate the results obtained from CAD software. For a multi-institutional study, we implemented CIRCUS CS into a teleradiology environment, which has been in practical use since September 2011. In this study, we investigated the performance improvement of CAD software for each institution based on retraining through a simulation-based study. According to the results, the performance of CAD software for each institution was improved by retraining.

A Multiple Anatomical Landmark Detection System for Body CT Images

Automatic detection of anatomical landmarks has wide range of application in medical image analysis. In this short paper, we present a two-stage method to detect 181 landmarks simultaneously. In the first stage, each landmark is independently searched by a dedicated detector which outputs a list of candidate positions for the target landmark. Each detector is composed of an appearance-based initial detector and a classifier ensemble to estimate the probabilities of detected candidates and to eliminate false positives. Here, the appearance shape used in each detector is optimized by a cross-validation-based variable selection algorithm in advance. Then, in the following second stage, a single combination of all landmark positions is determined from all the candidate lists. The determination is performed by maximum a posteriori (MAP) estimation in which the posterior probability is calculated from both the likelihoods of detected candidates (estimated by the classifier ensemble) and a statistical spatial distribution model of the all landmarks. This MAP estimation process can also determine whether each landmark is within the given CT volume or out of the imaging range. The proposed system was trained for 181 landmarks with 60 human torso CT datasets and evaluated with another 60 datasets. The datasets include both plain CT and contrast enhanced CT volumes with various imaging ranges. In the result, 69.0% and 87.9% of the landmarks were successfully detected within 1 and 2 cm from the ground truth point, respectively. The average detection error was 9.58 mm. From these results, applicability of the proposed system to various CT datasets was verified.

Landmark-guided diffeomorphic demons algorithm and its application to automatic segmentation of the whole spine and pelvis in CT images

PurposeA fully automatic multiatlas-based method for segmentation of the spine and pelvis in a torso CT volume is proposed. A novel landmark-guided diffeomorphic demons algorithm is used to register a given CT image to multiple atlas volumes. This algorithm can utilize both grayscale image information and given landmark coordinate information optimally.MethodsThe segmentation has four steps. Firstly, 170 bony landmarks are detected in the given volume. Using these landmark positions, an atlas selection procedure is performed to reduce the computational cost of the following registration. Then the chosen atlas volumes are registered to the given CT image. Finally, voxelwise label voting is performed to determine the final segmentation result.ResultsThe proposed method was evaluated using 50 torso CT datasets as well as the public SpineWeb dataset. As a result, a mean distance error of