We propose a method for automatic segmentation of individual muscles from a clinical CT. The method uses Bayesian convolutional neural networks with the U-Net architecture, using Monte Carlo dropout that infers an uncertainty metric in addition to the segmentation label. We evaluated the performance of the proposed method using two data sets: 20 fully annotated CTs of the hip and thigh regions and 18 partially annotated CTs that are publicly available from The Cancer Imaging Archive (TCIA) database. The experiments showed a Dice coefficient (DC) of 0.891±0.016 (mean±std) and an average symmetric surface distance (ASD) of 0.994±0.230 mm over 19 muscles in the set of 20 CTs. These results were statistically significant improvements compared to the state-of-the-art hierarchical multi-atlas method which resulted in 0.845 ± 0.031 DC and 1.556 ± 0.444 mm ASD. We evaluated validity of the uncertainty metric in the multi-class organ segmentation problem and demonstrated a correlation between the pixels with high uncertainty and the segmentation failure. One application of the uncertainty metric in active-learning is demonstrated, and the proposed query pixel selection method considerably reduced the manual annotation cost for expanding the training data set. The proposed method allows an accurate patient-specific analysis of individual muscle shapes in a clinical routine. This would open up various applications including personalization of biomechanical simulation and quantitative evaluation of muscle atrophy.

This paper describes a ultrasound (3-D US) system that aims to achieve augmented reality (AR) visualization during laparoscopic surgery, especially for the liver. To acquire 3-D US data of the liver, the tip of a laparoscopic ultrasound probe is tracked inside the abdominal cavity using a magnetic tracker. The accuracy of magnetic trackers, however, is greatly affected by magnetic field distortion that results from the close proximity of metal objects and electronic equipment, which is usually unavoidable in the operating room. In this paper, we describe a calibration method for intraoperative magnetic distortion that can be applied to laparoscopic 3-D US data acquisition; we evaluate the accuracy and feasibility of the method by in vitro and in vivo experiments. Although calibration data can be acquired freehand using a magneto-optic hybrid tracker, there are two problems associated with this method--error caused by the time delay between measurements of the optical and magnetic trackers, and instability of the calibration accuracy that results from the uniformity and density of calibration data. A temporal calibration procedure is developed to estimate the time delay, which is then integrated into the calibration, and a distortion model is formulated by zeroth-degree to fourth-degree polynomial fitting to the calibration data. In the in vivo experiment using a pig, the positional error caused by magnetic distortion was reduced from 44.1 to 2.9 mm. The standard deviation of corrected target positions was less than 1.0 mm. Freehand acquisition of calibration data was performed smoothly using a magneto-optic hybrid sampling tool through a trocar under guidance by realtime 3-D monitoring of the tool trajectory; data acquisition time was less than 2 min. The present study suggests that our proposed method could correct for magnetic field distortion inside the patient's abdomen during a laparoscopic procedure within a clinically permissible period of time, as well as enabling an accurate 3-D US reconstruction to be obtained that can be superimposed onto live endoscopic images.

The present paper examines the effect of safe failure fraction (SFF) constraints on hazardous-event rates, and discusses the validity of the SFF constraints in IEC 61508. First, the safe states are categorized into three types of states, and overall systems involving safety-related systems are classified into six types of systems based on the safe-state categorization, and the completeness of trips. Next, state-transition models for the systems where the effect of SFF is the greatest are presented, and the hazardous-event rates are analysed for the systems. Then, it is found that, when the effect of the SFF constraints is positive, it is negligible; and when it is negative, it is not negligible for safety. Thus, we recommend that the application of the SFF constraints to the standard should be put on hold.

In medical diagnostic imaging, an X-ray CT scanner or a MRI system have been widely used to examine 3D shapes or internal structures of living organisms or bones. However, these apparatuses are generally very expensive and of large size. A prior arrangement is also required before an examination, and thus, it is not suitable for an urgent fracture diagnosis in emergency treatment. This paper proposes a method to estimate a patient-specific 3D shape of a femur from only two fluoroscopic images using a parametric femoral model. Firstly, we develop a parametric femoral model by statistical analysis of a number of 3D femoral shapes created from CT images of 51 patients. Then, the pose and shape parameters of the parametric model are estimated from two 2D fluoroscopic images using a distance map constructed by the level set method. Experiments using synthesized images and fluoroscopic images of a phantom femur are successfully carried out and the usefulness of the proposed method is verified.

This paper presents a framework for a cluster system that is suited for high-resolution image processing over the Internet during surgery. The system realizes high-performance computing (HPC) assisted surgery, which allows surgeons to utilize HPC resources remote from the operating room. One application available in the system is an intraoperative estimator for the range of motion (ROM) adjustment in total hip replacement (THR) surgery. In order to perform this computation-intensive estimation during surgery, we parallelize the ROM estimator on a cluster of 64 PCs, each with two CPUs. Acceleration techniques such as dynamic load balancing and data compression methods are incorporated into the system. The system also provides a remote-access service over the Internet with a secure execution environment. We applied the system to an actual THR surgery performed at Osaka University Hospital and confirmed that it realizes intraoperative ROM estimation without degrading the resolution of images and limiting the area for estimations.

This paper describes several new methods and software for automatic segmentation of the pelvis and the femur, based on clinically obtained multislice computed tomography (CT) data. The hip joint is composed of the acetabulum, cavity of the pelvic bone, and the femoral head. In vivo CT data sets of 60 actual patients were used in the study. The 120 (60 x 2) hip joints in the data sets were divided into four groups according to several key features for segmentation. Conventional techniques for classification of bony tissues were first employed to distinguish the pelvis and the femur from other CT tissue images in the hip joint. Automatic techniques were developed to extract the boundary between the acetabulum and the femoral head. An automatic method was built up to manage the segmentation task according to image intensity of bone tissues, size, center, shape of the femoral heads, and other characters. The processing scheme consisted of the following five steps: 1) preprocessing, including resampling 3-D CT data by a modified Sinc interpolation to create isotropic volume and to avoid Gibbs ringing, and smoothing the resulting images by a 3-D Gaussian filter; 2) detecting bone tissues from CT images by conventional techniques including histogram-based thresholding and binary morphological operations; 3) estimating initial boundary of the femoral head and the joint space between the acetabulum and the femoral head by a new approach utilizing the constraints of the greater trochanter and the shapes of the femoral head; 4) enhancing the joint space by a Hessian filter; and 5) refining the rough boundary obtained in step 3) by a moving disk technique and the filtered images obtained in step 4). The above method was implemented in a Microsoft Windows software package and the resulting software is freely available on the Internet. The feasibility of this method was tested on the data sets of 60 clinical cases (5000 CT images).

Measuring the thickness of sheet-like thin anatomical structures, such as articular cartilage and brain cortex, in three-dimensional (3-D) magnetic resonance (MR) images is an important diagnostic procedure. This paper investigates the fundamental limits on the accuracy of thickness determination in MR images. We defined thickness here as the distance between the two sides of boundaries measured at the subvoxel resolution, which are the zero-crossings of the second directional derivatives combined with Gaussian blurring along the normal directions of the sheet surface. Based on MR imaging and computer postprocessing parameters, characteristics for the accuracy of thickness determination were derived by a theoretical simulation. We especially focused on the effects of voxel anisotropy in MR imaging with variable orientation of sheet-like structure. Improved and stable accuracy features were observed when the standard deviation of Gaussian blurring combined with thickness determination processes was around square root of 2/2 times as large as the pixel size. The relation between voxel anisotropy in MR imaging and the range of sheet normal orientation within which acceptable accuracy is attainable was also clarified, based on the dependences of voxel anisotropy and the sheet normal orientation obtained by numerical simulations. Finally, in vitro experiments were conducted using an acrylic plate phantom and a resected femoral head to validate the results of theoretical simulation. The simulated thickness was demonstrated to be well-correlated with the actual in vitro thickness.

This paper reports on the clinical application of a system for recovering the time-varying three-dimensional (3-D) left-ventricular (LV) shape from multiview X-ray cineangiocardiograms. Considering that X-ray cineangiocardiography is still commonly employed in clinical cardiology and computational costs for 3-D recovery and visualization are rapidly decreasing, it is meaningful to develop a clinically applicable system for 3-D LV shape recovery from X-ray cineangiocardiograms. The system is based on a previously reported closed-surface method of shape recovery from two-dimensional occluding contours with multiple views. To apply the method to "real" LV cineangiocardiograms, user-interactive systems were implemented for preprocessing, including detection of LV contours, calibration of the imaging geometry, and setting of the LV model coordinate system. The results for three real LV angiographic image sequences are presented, two with fixed multiple views (using supplementary angiography) and one with rotating views. 3-D reconstructions utilizing different numbers of views were compared and evaluated in terms of contours manually traced by an experienced radiologist. The performance of the preprocesses was also evaluated, and the effects of variations in user-specified parameters on the final 3-D reconstruction results were shown to be sufficiently small. These experimental results demonstrate the potential usefulness of combining multiple views for 3-D recovery from "real" LV cineangiocardiograms.

The goal of this paper is to present appropriate line segmentation for intersections (X-junctions) and branches (T-junctions). In the local regions of intersections and branches, multiple orientations occur. A novel representation called "orientation space" is proposed, which is derived by adding the orientation axis to the abscissa and the ordinate of the image. The orientation space representation is constructed by treating the orientation parameter, to which Gabor filters can be tuned, as a continuous variable. The problem of multiple orientation line segmentation is dealt with by thresholding 3D images of the orientation space and then detecting the connected components therein. In this way, X-junctions and T-junctions are able to be separated effectively. Experimental results are presented using synthesized and real biomedical images. In particular, overlapping vessels in an x-ray coronary angiogram were well segmented by orientation space filtering.

A method of evaluating brain function using the metacomputer concept of the Globus system combined with a message-passing interface is described. The proposed method has the ability to exploit various geographically distributed resources and parallel computing linked to a high-technology medical instrumentation system, magnetoencephalography, to analyze the functional state of the brain. It is envisaged that the method will lead to the realization of an efficient telemedicine system for health care.

This paper describes a novel approach to tissue classification using three-dimensional (3D) derivative features in the volume rendering pipeline. In conventional tissue classification for a scalar volume, tissues of interest are characterized by an opacity transfer function defined as a one-dimensional (1D) function of the original volume intensity. To overcome the limitations inherent in conventional 1D opacity functions, we propose a tissue classification method that employs a multidimensional opacity function, which is a function of the 3D derivative features calculated from a scalar volume as well as the volume intensity. Tissues of interest are characterized by explicitly defined classification rules based on 3D filter responses highlighting local structures, such as edge, sheet, line, and blob, which typically correspond to tissue boundaries, cortices, vessels, and nodules, respectively, in medical volume data. The 3D local structure filters are formulated using the gradient vector and Hessian matrix of the volume intensity function combined with isotropic Gaussian blurring. These filter responses and the original intensity define a multidimensional feature space in which multichannel tissue classification strategies are designed. The usefulness of the proposed method is demonstrated by comparisons with conventional single-channel classification using both synthesized data and clinical data acquired with CT (computed tomography) and MRI (magnetic resonance imaging) scanners. The improvement in image quality obtained using multichannel classification is confirmed by evaluating the contrast and contrast-to-noise ratio in the resultant volume-rendered images with variable opacity values.

This paper describes the usefulness of computer assistance in the acquisition of "good" images for stenosis diagnosis and quantification in coronary angiography. The system recommends the optimal viewpoints from which stenotic lesions can be observed clearly based on images obtained from initial viewpoints. First, the viewpoint dependency of the apparent severity of a stenotic lesion is experimentally analyzed using software phantoms in order to show the seriousness of the problem. The implementation of the viewpoint determination system is then described. The system provides good user-interactive tools for the semiautomated estimation of the orientation and diameter of stenotic segments and the three-dimensional (3-D) reconstruction of vessel structures. Using these tools, viewpoints that will not give rise to foreshortening and vessel overlap can be efficiently determined. Experiments using real coronary angiograms show the system to be capable of the reliable diagnosis and quantification of stenosis.

This paper describes augmented reality visualization for the guidance of breast-conservative cancer surgery using ultrasonic images acquired in the operating room just before surgical resection. By combining an optical three-dimensional (3-D) position sensor, the position and orientation of each ultrasonic cross section are precisely measured to reconstruct geometrically accurate 3-D tumor models from the acquired ultrasonic images. Similarly, the 3-D position and orientation of a video camera are obtained to integrate video and ultrasonic images in a geometrically accurate manner. Superimposing the 3-D tumor models onto live video images of the patient's breast enables the surgeon to perceive the exact 3-D position of the tumor, including irregular cancer invasions which cannot be perceived by touch, as if it were visible through the breast skin. Using the resultant visualization, the surgeon can determine the region for surgical resection in a more objective and accurate manner, thereby minimizing the risk of a relapse and maximizing breast conservation. The system was shown to be effective in experiments using phantom and clinical data.

We propose a new model and new algorithm for acquiring symbolic description from a flow field. Our model is derived from physical data, and our algorithm is based on holomorphic complex function theory. Application of this method enables both qualitative and quantitative information to be obtained from a flow field. We also demonstrate the robustness of our method using a simulated flow field and a real image sequence.

This paper describes a method for the accurate recovery of time-varying 3D shapes with known cycle from images with different viewpoints as well as times; aiming at the recovery of the left ventricular shapes. Our recovery method is based on the integration of apparent contours from different viewpoints. We perform direct fitting to a 4D closed surface model based on B-splines so as to deal with fragmented contours such as extracted from X-ray cineangiocardiograms. The method is quantitatively evaluated using synthesized and real image sequences.

A post-processing technique has been developed to suppress the magnetic resonance imaging (MRI) artifact arising from object planar rigid motion. In two-dimensional Fourier transform (2-DFT) MRI, rotational and translational motions of the target during magnetic resonance magnetic resonance (MR) scan respectively impose nonuniform sampling and a phase error an the collected MRI signal. The artifact correction method introduced considers the following three conditions: (1) for planar rigid motion with known parameters, a reconstruction algorithm based on bilinear interpolation and the super-position method is employed to remove the MRI artifact, (2) for planar rigid motion with known rotation angle and unknown translational motion (including an unknown rotation center), first, a super-position bilinear interpolation algorithm is used to eliminate artifact due to rotation about the center of the imaging plane, following which a phase correction algorithm is applied to reduce the remaining phase error of the MRI signal, and (3) to estimate unknown parameters of a rigid motion, a minimum energy method is proposed which utilizes the fact that planar rigid motion increases the measured energy of an ideal MR image outside the boundary of the imaging object; by using this property all unknown parameters of a typical rigid motion are accurately estimated in the presence of noise. To confirm the feasibility of employing the proposed method in a clinical setting, the technique was used to reduce unknown rigid motion artifact arising from the head movements of two volunteers.

A computer postprocessing technique is developed to remove MRI artifact arising from unknown translational motion in the imaging plane. Based on previous artifact correction methods, the improved technique uses two successive steps to reduce read out and phase-encoding direction artifacts: First, the spectrum shift method is applied to remove read-out axis translational motion. Then, the phase retrieval method is employed to eliminate the remaining subpixel motion of the read-out axis and the entire motion of the phase-encoding axis. In the presence of noise, to protect edge detection (in the spectrum shift method), two high-density gray-level markers are added, one to each side of the imaging object. Experimental results with an actual MR scan confirmed the ability of the method to correct the artifact of an MR image caused by unknown translational motion in the imaging plane.

We make a comparision of classification ability between BPN (BackPropagation Neural Network) and k-NN (k-Nearest Neighbor) classification methods. Voice data and patellar subluxation images are used. The result was that the average recognition rate of BPN was 9.2 percent higher than that of the k-NN classification method. Although k-NN classification is simple in theory, classification time was fairly long. Therefore, it seems that real time recognition is difficult. On the other hand, the BPN method has a long learning time but a very short recognition time. Especially if the number of dimensions of the samples is large, it can be said that BPN is better than k-NN in classification ability.

The recovery of viewpoint-independent descriptions of 3D shapes from two-and-one-half dimensional images. A novel 3D shape representation called the smoothed local generalized cones (SLGCs) is proposed. This representation is suitable for recovery of the axis, because the local constraint that characterizes a data set corresponding to the same axis point, namely, the local generalized cone (LGC), is explicitly defined. The extracted axis can be used as a basis for determining a natural parameterization of the object surface. Using this parameterization, the deformable surface fitting problem results in a linear least-squares problem, so stable volumetric recovery is possible. Recovery experiments involving real 3D range images are reported.>

A formulation of the part decomposition problem motived by the minimum-description-length (MDL) criteria is presented. Unlike previous MDL approaches which use analytic functions, a general geometric constraint, convexity, is used as a part constraint. Therefore, the method is suitable for complex natural shapes such as human faces. The recovery process consists of a bottom-up grouping process and a subsequent optimization process based on the MDL criteria. The definite causal relations of part structure between different sensitivity levels are used to recover the hierarchy of part structure. Part decomposition experiments involving real 3-D range images are reported.>