Acantharia (Acantharea) are wide-spread marine protozoa, presenting one of the rare examples of stron-tium sulfate mineralization in the biosphere. Their endoskeletons consist of 20 spicules arranged accord-ing to a u...
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Acantharia (Acantharea) are wide-spread marine protozoa, presenting one of the rare examples of stron-tium sulfate mineralization in the biosphere. Their endoskeletons consist of 20 spicules arranged accord-ing to a unique geometric pattern named Muller's principle. Given the diverse mineral architecture of the Acantharia class, we set out to examine the complex three-dimensional skeletal morphology at the nanometer scale using synchrotron X-ray nanotomography, followed by imagesegmentation based on deeplearning methods. The present study focuses on how the spicules emanate from the robust central junction in the orders Symphyacanthida and Arthracanthida, the geometry of lateral spicule wings as well as pockets of interspicular space, which may be involved in cell compartmentalization. Through these morphometric studies, we observed subtle deviations from the previously described spatial arrangement of the spicules. According to our data, spicule shapes are adjusted in opposite spicules as to accommodate the overall spicule arrangement. In all types examined, previously unknown interspicular interstices were found in areas where radial spicules meet, which could have implications for the crystal growth mecha-nism and overall endoskeletal integrity. A deeper understanding of the spiculogenesis in Acantharia can provide biomimetic routes towards complex inorganic shapes. Statement of significance Morphogenesis, the origin and control of shape, provides an avenue towards tailored inorganic materials. In this work, we explored the intricate skeletal organization of planktonic Acantharia, which are amongst the few strontium sulfate biomineralizing organisms in nature. By using nanoscale X-ray imaging and deep learning image segmentation, we found deviations from previously described geometric patterns and undiscovered skeletal features. The bio-inspired synthesis of inorganic materials with complex shape has important ramifications for solid-state chemistry and nanotec
This work investigated the porosity evolution of POCO ZXF-5Q graphite that has been irradiated by 340 kW, 120 GeV protons inside NT02 target system in Fermilab's NuMI beamline. This POCO graphite has undergone dir...
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This work investigated the porosity evolution of POCO ZXF-5Q graphite that has been irradiated by 340 kW, 120 GeV protons inside NT02 target system in Fermilab's NuMI beamline. This POCO graphite has undergone direct bulk dimensional swelling at low dose irradiation and its local microstructural change is still not well-understood during this process. In this work, the (sub-) micrometre scale porosity from six locations across proton beam fluence and temperature gradients have been studied using focused ion beam-scanning electron microscopy (FIB-SEM) tomography. A deeplearning-based tomographic imagesegmentation technique has been established and implemented for porosity segmentation and quantification. It has been found that there is a decrease in the total volumetric percentage of the porosity at proton beam centre (-8 - 8.4 vol.%), by comparing to un-irradiated POCO (-12 - 13 vol.%) and to beam 2c5 and 5c5 radii (-12 vol.%). This decrease in porosity volume per-centage was found to be caused by the reduction in pores with volumes > 0.1 mu m3 induced by material bulk dimensional swelling at proton beam centre area. The porosity reduction in relation to dimensional change and irradiation creep was discussed among with other contributing factors, and further investigations through well-controlled irradiation experiment are still needed.
State of the art deeplearning (DL) manifested in image processing as an accurate segmentation method. Nevertheless, its black-box nature hardly allows user interference. In this paper, we present a generic Graph cut ...
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ISBN:
(纸本)9789897584886
State of the art deeplearning (DL) manifested in image processing as an accurate segmentation method. Nevertheless, its black-box nature hardly allows user interference. In this paper, we present a generic Graph cut (GC) and Graph segmentation (GS) approach for user-guided interactive post-processing of segmentations resulting from DL. The GC fitness function incorporates both, the original image characteristics and DL segmentation results, combining them with weights optimized by evolution strategy optimization. To allow for accurate user-guided processing, the fore- and background seeds of the Graph cut are automatically selected from the DL segmentations, but implementing effective features for expert input for adaptions of position and topology. The seamless integration of DL with GC/GS leads to marginal trade-off in quality, namely Jaccard (JI) 1.3% for automated GC and JI 0.46% for GS only. Yet, in specific areas where a well-trained DL model may potentially fail, precise adaptions at a low demand for user-interaction become feasible and thus even outperforming the original DL results. The potential of GC/GS is shown running on ground-truth seeds thereby outperforming DL by 0.44% JI for the GC and even by 1.16% JI for the GS. Iterative slice-by-slice progression of the post-processed and improved results keeps the demand for user-interaction low.
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