Superfluid helium nanoscope insert with millimeter working range.

A superfluid helium insert was developed for cryogenic microscopy of millimeter-sized specimens. An optical-interferometric position sensor, cryogenic objective mirror, and piezo-driven cryogenic stage were fixed to an insert holder that was immersed in superfluid helium. The single-component design stabilized the three-dimensional position of the sample, with root-mean-square deviations of (x, lateral) 0.33 nm, (y, lateral) 0.29 nm, and (z, axial) 0.25 nm. Because of the millimeter working range of the optical sensor, the working range of the sample under the active stabilization was (x, y) 5 mm and (z) 3 mm in superfluid helium at 1.8 K. The insert was used to obtain the millimeter-sized fluorescence image of cell nuclei at 1.8 K without a sample exchange.

Visualizing the Flow Patterns in an Expanding and Contracting Pulmonary Alveolated Duct Based on Microcomputed Tomography Images.

We visualized the flow patterns in an alveolated duct model with breathing-like expanding and contracting wall motions using particle image velocimetry, and then, we investigated the effect of acinar deformation on the flow patterns. We reconstructed a compliant, scaled-up model of an alveolated duct from synchrotron microcomputed tomography images of a mammalian lung. The alveolated duct did not include any bifurcation, and its entire surface was covered with alveoli. We embedded the alveolated duct in a sealed container that was filled with fluid. We oscillated the fluid in the duct and container simultaneously and independently to control the flow and duct volume. We examined the flow patterns in alveoli, with the Reynolds number (Re) at 0.03 or 0.22 and the acinar volume change at 0%, 20%, or 80%. At the same Re, the heterogeneous deformation induced different inspiration and expiration flow patterns, and the recirculating regions in alveoli changed during respiratory cycle. During a larger acinar deformation at Re = 0.03, the flow patterns tended to change from recirculating flow to radial flow during inspiration and vice versa during expiration. Additionally, the alveolar geometric characteristics, particularly the angle between the alveolar duct and mouth, affected these differences in flow patterns. At Re = 0.22, recirculating flow patterns tended to form during inspiration and expiration, regardless of the magnitude of the acinar deformation. Our in vitro experiments suggest that the alveolated flows with nonself-similar and heterogeneous wall motions may promote particle mixing and deposition.

[Semantic Segmentation of Eight Regions of Upper and Lower Limb Bones Using 3D U-Net in Whole-body CT Images].

PURPOSE: Automated analysis of skeletal muscle in whole-body computed tomography (CT) images uses bone information, but bone segmentation including the epiphysis is not achieved. The purpose of this research was the semantic segmentation of eight regions of upper and lower limb bones including the epiphysis in whole-body CT images. Our targets were left and right upper arms, forearms, thighs, and lower legs. METHOD: We connected two 3D U-Nets in cascade for segmentation of eight upper and lower limb bones in whole-body CT images. The first 3D U-Net was used for skeleton segmentation in whole-body CT images, and the second 3D U-Net was used for eight upper and lower limb bones' segmentation in skeleton segmentation results. Thirty cases of whole-body CT images were used in the experiment, and the segmentation results were evaluated using Dice coefficient with 3-fold cross-validation. RESULT: The mean Dice coefficient was 93% in the left and right upper arms, 89% in the left and right forearms, 95% in the left and right thighs, and 94% in the left and right lower legs. CONCLUSION: Although the accuracy of the segmentation results of relatively small bones remains a challenge, the semantic segmentation of eight regions of upper and lower limb bones including the epiphysis in whole-body CT images has been achieved.

Deep Learning Technique for Musculoskeletal Analysis.

Advancements in musculoskeletal analysis have been achieved by adopting deep learning technology in image recognition and analysis. Unlike musculoskeletal modeling based on computational anatomy, deep learning-based methods can obtain muscle information automatically. Through analysis of image features, both approaches can obtain muscle characteristics such as shape, volume, and area, and derive additional information by analyzing other image textures. In this chapter, we first discuss the necessity of musculoskeletal analysis and the required image processing technology. Then, the limitations of skeletal muscle recognition based on conventional handcrafted features are discussed, and developments in skeletal muscle recognition using machine learning and deep learning technology are described. Next, a technique for analyzing musculoskeletal systems using whole-body computed tomography (CT) images is shown. This study aims to achieve automatic recognition of skeletal muscles throughout the body and automatic classification of atrophic muscular disease using only image features, to demonstrate an application of whole-body musculoskeletal analysis driven by deep learning. Finally, we discuss future development of musculoskeletal analysis that effectively combines deep learning with handcrafted feature-based modeling techniques.

Symbiotic Growth of a Thermophilic Sulfide-Oxidizing Photoautotroph and an Elemental Sulfur-Disproportionating Chemolithoautotroph and Cooperative Dissimilatory Oxidation of Sulfide to Sulfate.

A thermophilic filamentous anoxygenic photosynthetic bacterium, Chloroflexus aggregans, is widely distributed in neutral to slightly alkaline hot springs. Sulfide has been suggested as an electron donor for autotrophic growth in microbial mats dominated with C. aggregans, but remarkable photoautotrophic growth of isolated C. aggregans has not been observed with sulfide as the sole electron source. From the idea that sulfide is oxidized to elemental sulfur by C. aggregans and the accumulation of elemental sulfur may have an inhibitory effect for the growth, the effects of an elemental sulfur-disproportionating bacterium that consumes elemental sulfur was examined on the autotrophic growth of C. aggregans, strain NA9-6, isolated from Nakabusa hot spring. A sulfur-disproportionating bacterium, Caldimicrobium thiodismutans strain TF1, also isolated from Nakabusa hot spring was co-cultured with C. aggregans. C. aggregans and C. thiodismutans were successfully co-cultured in a medium containing thiosulfate as the sole electron source and bicarbonate as the sole carbon source. Quantitative conversion of thiosulfate to sulfate and a small transient accumulation of sulfide was observed in the co-culture. Then the electron source of the established co-culture was changed from thiosulfate to sulfide, and the growth of C. aggregans and C. thiodismutans was successfully observed with sulfide as the sole electron donor for the autotrophic growth of the co-culture. During the cultivation in the light, simultaneous consumption and accumulation of sulfide and sulfate, respectively, were observed, accompanied with the increase of cellular DNAs of both species. C. thiodismutans likely works as an elemental sulfur scavenger for C. aggregans, and C. aggregans seems to work as a sulfide scavenger for C. thiodismutans. These results suggest that C. aggregans grows autotrophically with sulfide as the electron donor in the co-culture with C. thiodismutans, and the consumption of elemental sulfur by C. thiodismutans enabled the continuous growth of the C. aggregans in the symbiotic system. This study shows a novel symbiotic relationship between a sulfide-oxidizing photoautotroph and an elemental sulfur-disproportionating chemolithoautotroph via cooperative dissimilatory sulfide oxidation to sulfate.

Muscle Segmentation for Orthopedic Interventions.

Skeletal muscle segmentation techniques can help orthopedic interventions in various scenes. In this chapter, we describe two methods of skeletal muscle segmentation on 3D CT images. The first method is based on a computational anatomical model, and the second method is a deep learning-based method. The computational anatomy-based methods are modeling the muscle shape with its running and use it for segmentation. In the deep learning-based methods, the muscle regions are directly acquired automatically. Both approaches can obtain muscle regions including shape, area, volume, and some other image texture features. And it is desirable that the method be selected by the required orthopedic intervention. Here, we show each design philosophy and features of a representative method. We discuss the various examples of site-specific segmentation of skeletal muscle in non-contrast images using torso CT and whole-body CT including in cervical, thoracoabdominal, surface and deep muscles. And we also mention the possibility of application to orthopedic intervention.

Fully automatic segmentation of paraspinal muscles from 3D torso CT images via multi-scale iterative random forest classifications.

PURPOSE: To develop and validate a fully automatic method for segmentation of paraspinal muscles from 3D torso CT images. METHODS: We propose a novel learning-based method to address this challenging problem. Multi-scale iterative random forest classifications with multi-source information are employed in this study to speed up the segmentation and to improve the accuracy. Here, multi-source images include the original torso CT images and later also the iteratively estimated and refined probability maps of the paraspinal muscles. We validated our method on 20 torso CT data with associated manual segmentation. We randomly partitioned the 20 CT data into two evenly distributed groups and took one group as the training data and the other group as the test data. RESULTS: The proposed method achieved a mean Dice coefficient of 93.0%. It took on average 46.5 s to segment a 3D torso CT image with the size ranging from [Formula: see text] voxels to [Formula: see text] voxels. CONCLUSIONS: Our fully automatic, learning-based method can accurately segment paraspinal muscles from 3D torso CT images. It generates segmentation results that are better than those achieved by the state-of-the-art methods.

Current status of HTLV-1 carrier in Japanese pregnant women.

We examined the current status of human T-cell leukemia virus type 1 (HTLV-1) carrier in Japanese pregnant women, according to the results of HTLV-1 screening and confirmation tests of women who gave birth in Japan in 2011. We requested 2642 obstetrical facilities to provide information of HTLV-1 tests and 71.3% of them responded. Considering the response rate and the rate of implementation of confirmation tests, the number of HTLV-1 carrier in Japanese pregnant women was estimated to be 1620 (0.16%) per year.

[Development of a tissue-equivalent magnetic resonance imaging phantom using indigestible dextrin and soluble calcium].

In magnetic resonance imaging (MRI), the ideal phantom should have similar T1 and T2 values to those of organs of interest for measuring the change in signal intensity, contrast ratio and contrast noise ratio. There have been several reports to develop such a phantom using materials with limited availability or complex methods. In this study, we have developed a simple phantom using indigestible dextrin and soluble calcium at 1.5-tesla MRI. The T1 and T2 values have been reduced by dissolving indigestible dextrin and soluble calcium in distilled water. The similar T1 and T2 values to those of organs (i.e., kidney cortex, kidney medulla, liver, spleen, pancreas, bone marrow, uterus myometrium, uterus endometrium, uterus cervix, prostate, brain white matter, and brain gray matter) have been obtained by varying the concentration of indigestible dextrin and soluble calcium. This phantom is easy to develop and has a potential to increase the accuracy of MRI phantom experiments.

A survey analysis of acoustic trauma related to MR scans.

PURPOSE: The maximum limit of MR scanner noise and necessity of ear protection is defined in the IEC standard (IEC60601-2-33) of MR safety. With improvements in MR scanner performance, pulse sequences generating higher scanning noise have been used clinically. In this study, we investigated the factors significantly related to potential acoustic trauma cases (PATC) after MR examinations. To consider the future direction for MR safety and prevention of acoustic trauma, issues related to noise generation by MR scanners and acoustic trauma were systematically reviewed. METHODS: A statistical analysis was performed using the data set from a survey (n=974) conducted in 2010 by the JSMRM safety committee. Hierarchical clustering analysis was used to extract the characteristics of the responders. With this classification as a reference, tests of independence and a residual analysis were employed to evaluate the factors related to PATC. RESULTS: No significant relationship was observed between the ear protection policy and the incidence or the reported outcome of PATC. While the two main clusters out of the six clusters extracted were associated with who reported the PATC and the confirmation process of the acoustic noise level of MR scanners, no cluster was associated with the frequency of PATC. An absence of PATC was significantly less reported (p=0.03) and more PATC was reported (p=0.04) by facilities with 3T MR systems. DISCUSSION: Although the total frequency was 4 cases, it should be noted that persistent hearing disturbances are a possible consequence of MR examinations. Neither the condition of the subjects nor the ear protection method was significantly related to the probability of PATC, suggesting the difficulty of predicting the potential risk of acoustic trauma. It is recommended to more systematically follow up PATC cases and clarify the risk factors.

Automated segmentation of psoas major muscle in X-ray CT images by use of a shape model: preliminary study.

Our motivation was to provide an automatic tool for radiologists and orthopedic surgeons for improving the quality of life of an aging population. We propose a method for generating a shape model and a fully automated segmenting scheme for the psoas major muscle in X-ray CT images by using the shape model. Our approach consists of two steps: (1) The generation of a shape model and its application to muscle segmentation. The shape model describes the muscle's outer shape and has two parameters, an outer shape parameter and a fitting parameter. The former was determined by approximating of the outer shape of the muscle region in training cases. The latter was determined for each test case in the recognition process. (2) Finally, the psoas major muscle was segmented by use of the shape model. To evaluate the performance of the method, we applied it to CT images for constructing the shape models by using 20 cases as training samples; 80 cases were used for testing. The accuracy of this method was measured by comparison of the extracted muscle regions with regions that were identified manually by an expert radiologist. The experimental results of the segmentation of the psoas major muscle gave a mean Jaccard similarity coefficient of 72.3%. The mean true segmentation coefficient was 76.2%. The proposed method can be used for the analysis of cross-sectional area and muscular thickness in a transverse section, offering radiologists an alternative to manual measurement for saving their time and improving the reproducibility of segmentation.

[Redefining the concept of elderly--for "successful aging" society].

For a long time, the term "elderly" has been defined as a chronological age of 65 years or more. However, in modern Japan where the average life span is 80 years or more, it has become already outdated to group the elderly into a chronological age of 65 years or more. We have attempted to review the definition of the elderly by analyzing data from statistics on population dynamics, trends of Japanese public opinion, long-term longitudinal epidemiological studies and clinical and pathological studies. We propose rebuilding a more flexible definition of the elderly based on comprehensive evidence in all aspects of social, cultural and medical sciences. In addition, our proposal might be a starting point in elaborating a strategy for a successful aging society.

[Combined irinotecan (CPT-11) and nedaplatin (254-S) therapy for advanced and/or recurrent cervical cancer].

We performed combination therapy with irinotecan (CPT-11) plus nedaplatin (254-S) for patients with cervical cancer. A total of 9 patients with cervical cancer (5 patients treated in neoadjuvant setting and 4 patients for recurrent disease) were administered 80 mg/m(2) of 254-Sintravenously on day 1 and 50 mg/m(2) of CPT-11 intravenously on day 1, 8 and 15. Treatment was repeated every 4 weeks. The average number of courses administered was 3.8 (range, 2-6). Grade 3 or 4 adverse events were leukopenia in 4 patients, thrombocytopenia in 1 patient and vomiting in 1 patient. The response rate was 40% in the neoadjuvant setting (2 PRs) and 75% in recurrent disease (3 PRs).