Automatic segmentation for synchrotron-based imaging of porous bread dough using deep learning approach.

In recent years, major capability improvements at synchrotron beamlines have given researchers the ability to capture more complex structures at a higher resolution within a very short time. This opens up the possibility of studying dynamic processes and observing resulting structural changes over time. However, such studies can create a huge quantity of 3D image data, which presents a major challenge for segmentation and analysis. Here tomography experiments at the Australian synchrotron source are examined, which were used to study bread dough formulations during rising and baking, resulting in over 460 individual 3D datasets. The current pipeline for segmentation and analysis involves semi-automated methods using commercial software that require a large amount of user input. This paper focuses on exploring machine learning methods to automate this process. The main challenge to be faced is in generating adequate training datasets to train the machine learning model. Creating training data by manually segmenting real images is very labour-intensive, so instead methods of automatically creating synthetic training datasets which have the same attributes of the original images have been tested. The generated synthetic images are used to train a U-Net model, which is then used to segment the original bread dough images. The trained U-Net outperformed the previously used segmentation techniques while taking less manual effort. This automated model for data segmentation would alleviate the time-consuming aspects of experimental workflow and would open the door to perform 4D characterization experiments with smaller time steps.

Characterization of infected, explanted ventricular assist device drivelines: The role of biofilms and microgaps in the driveline tunnel.

BACKGROUND: Driveline infections remain a major complication of ventricular assist device (VAD) implantation. This study aimed to characterize in vivo microbial biofilms associated with driveline infections and host tissue integration of implanted drivelines. METHODS: A total of 9 infected and 13 uninfected drivelines were obtained from patients with VAD undergoing heart transplantation in Australia between 2016 and 2018. Each driveline was sectioned into 11 pieces of 1.5 cm in length, and each section was examined by scanning electron microscopy (SEM) and viable counts for microbial biofilms. Microorganisms were cultured and identified. Host tissue integration of clinical drivelines was assessed with micro-computed tomography (CT) and SEM. An in vitro interstitial biofilm assay was used to simulate biofilm migration in the driveline tunnel, and time-lapse microscopy was performed. RESULTS: Of the 9 explanted, infected drivelines, all had organisms isolated from varying depths along the velour section of the drivelines, and all were consistent with the swab culture results of the clinically infected exit site. SEM and micro-CT suggested insufficient tissue integration throughout the driveline velour, with microgaps observed. Clinical biofilms presented as microcolonies within the driveline tunnel, with human tissue as the sub-stratum, and were resistant to anti-microbial treatment. Biofilm migration mediated by a dispersal-seeding mechanism was observed. CONCLUSIONS: This study of explanted infected drivelines showed extensive anti-microbial-resistant biofilms along the velour, associated with microgaps between the driveline and the surrounding tissue. These data support the enhancement of tissue integration into the velour as a potential preventive strategy against driveline infections by preventing biofilm migration that may use microgaps as mediators.

Toward Improving Breast Cancer Imaging: Radiological Assessment of Propagation-Based Phase-Contrast CT Technology.

RATIONALE AND OBJECTIVES: This study employs clinical/radiological evaluation in establishing the optimum imaging conditions for breast cancer imaging using the X-ray propagation-based phase-contrast tomography. MATERIALS AND METHODS: Two series of experiments were conducted and in total 161 synchrotron-based computed tomography (CT) reconstructions of one breast mastectomy specimen were produced at different imaging conditions. Imaging factors include sample-to-detector distance, X-ray energy, CT reconstruction method, phase retrieval algorithm applied to the CT projection images and maximum intensity projection. Observers including breast radiologists and medical imaging experts compared the quality of the reconstructed images with reference images approximating the conventional (absorption) CT. Various radiological image quality attributes in a visual grading analysis design were used for the radiological assessments. RESULTS: The results show that the application of the longest achievable sample-to-detector distance (9.31 m), the lowest employed X-ray energy (32 keV), the full phase retrieval, and the maximum intensity projection can significantly improve the radiological quality of the image. Several combinations of imaging variables resulted in images with very high-quality scores. CONCLUSION: The results of the present study will support future experimental and clinical attempts to further optimize this innovative approach to breast cancer imaging.

A synchrotron-based local computed tomography combined with data-constrained modelling approach for quantitative analysis of anthracite coal microstructure.

Quantifying three-dimensional spatial distributions of pores and material compositions in samples is a key materials characterization challenge, particularly in samples where compositions are distributed across a range of length scales, and where such compositions have similar X-ray absorption properties, such as in coal. Consequently, obtaining detailed information within sub-regions of a multi-length-scale sample by conventional approaches may not provide the resolution and level of detail one might desire. Herein, an approach for quantitative high-definition determination of material compositions from X-ray local computed tomography combined with a data-constrained modelling method is proposed. The approach is capable of dramatically improving the spatial resolution and enabling finer details within a region of interest of a sample larger than the field of view to be revealed than by using conventional techniques. A coal sample containing distributions of porosity and several mineral compositions is employed to demonstrate the approach. The optimal experimental parameters are pre-analyzed. The quantitative results demonstrated that the approach can reveal significantly finer details of compositional distributions in the sample region of interest. The elevated spatial resolution is crucial for coal-bed methane reservoir evaluation and understanding the transformation of the minerals during coal processing. The method is generic and can be applied for three-dimensional compositional characterization of other materials.