X-ray microtomography measurements of bioactive glass scaffolds in rabbit femur samples at multiple stages of bone regeneration : reduction of image artefacts and a preliminary segmentation

A series of x-ray microtomography (micro-CT) measurements was performed on a set of rabbit femur bone samples containing artificial scaffolds of bioactive glass BAG-S53P4, implanted into an intentionally induced defect, i.e. a gap, in the femur. The scaffolds, some additionally enveloped in PLGA, were supportive structures composed of small granules of bioactive glass, intended to enhance, stimulate and guide the healing and regeneration of bone. The 34 samples were harvested from the rabbits at three different stages of healing and bone regeneration: 2 weeks, 4 weeks and 8 weeks. In addition to 27 samples that contained scaffolds of BAG-S53P4 or BAG-S53P4-PLGA, which had been implanted into the femur of a rabbit, 3 scaffolds of BAG-S53P4(-PLGA) that were not implanted and 7 control samples containing inert PMMA-implants were also included in the measurements for comparison. During the healing process the bioactive glass granules are gradually dissolved into the surrounding bodily fluids and a thin reaction layer composed of silica gel forms onto the surfaces of the granules. Subsequently an additional surface layer composed of HCA, a material that closely resembles natural hydroxyapatite, is formed onto the granules. As the healing process to regenerate the bone in the gap progresses, a complex three-dimensional network of newly formed trabecular bone grows in between the granules, attaching onto the surface layers and eventually enveloping the gradually dissolving granules entirely. Ultimately, the scaffold is intended to degrade completely, and a structure of regenerated, remodeled cortical bone is expected to be formed into the volume of the initial defect. As the thicknesses of both the surface layers of the granules and the individual trabeculae of the newly formed bone are in the micrometre range, x-ray microtomography was employed to evaluate and assess the complex three-dimensional structure, consisting of trabecular bone intertwined with granules at varying stages of dissolution. By evaluating the rate of formation of these structures at three different stages, i.e. time points, of regeneration, valuable information on the effectiveness of the bioactive glass BAG-S53P4(-PLGA) for the regeneration of defected bone can be obtained. The measurements were performed at University of Helsinki’s Laboratory of Microtomography using its Nanotom-apparatus with 80kV voltage, 150microA current and a voxel size of 15micrometres. 1000 projection images per sample were used in 37 reconstructions utilizing the FBP-algorithm. Subsequent image processing to analyze and compare the samples was conducted using ImageJ. A procedure to reduce image artefacts – due to metal parts in the samples – was developed, utilizing Gaussian filtering, as well as a preliminary image segmentation scheme, utilizing Morphological filtering, to automatically separate the bone from the granules and their surface layers

Deep learning-based tool for radiomics analysis of cancer 3D multicellular spheroids

Cancer 3D multicellular spheroids are a fundamental in vitro tool for studying in vivo tumors. Volume is the main feature used for evaluating the drug and treatment effects, but several other features can be estimated even from a simple 2D image. For high-content screening analysis, the bottleneck is the segmentation stage, which is essential for detecting the spheroids in the images and then proceeding to the feature extraction stage for performing radiomic analysis. Thanks to new deep learning models, it is possible to optimize the process for adapting the analysis to big datasets. One of the most promising approaches is the use of convolutional neural networks (CNNs), which have shown remarkable results in various medical imaging applications. By training a CNN on a large dataset of annotated images, it can learn to recognize patterns and features that are relevant for segmenting spheroids in new images. This approach has several advantages, such as manual or semi-automatic segmentation, which are time-consuming and prone to inter-observer variability. Moreover, CNNs can be fine-tuned for specific tasks and can handle different types of data, such as multi-modal or multi-dimensional images. Starting from the first version of Analysis of SPheroids (AnaSP), an open-source software for estimating morphological features of spheroids, we implemented a new module for automatically segmenting brightfield images by exploiting CNNs. In this work, several deep learning segmentation models have been trained and compared using ground truth masks. Then, a module based on an 18-layer deep residual network (ResNet18) was integrated into AnaSP, releasing AnaSP 2.0, a version of the tool optimized for high-content screening analysis

Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review

Instance segmentation of nuclei and glands in the histology images is an important step in computational pathology workflow for cancer diagnosis, treatment planning and survival analysis. With the advent of modern hardware, the recent availability of large-scale quality public datasets and the community organized grand challenges have seen a surge in automated methods focusing on domain specific challenges, which is pivotal for technology advancements and clinical translation. In this survey, 126 papers illustrating the AI based methods for nuclei and glands instance segmentation published in the last five years (2017-2022) are deeply analyzed, the limitations of current approaches and the open challenges are discussed. Moreover, the potential future research direction is presented and the contribution of state-of-the-art methods is summarized. Further, a generalized summary of publicly available datasets and a detailed insights on the grand challenges illustrating the top performing methods specific to each challenge is also provided. Besides, we intended to give the reader current state of existing research and pointers to the future directions in developing methods that can be used in clinical practice enabling improved diagnosis, grading, prognosis, and treatment planning of cancer. To the best of our knowledge, no previous work has reviewed the instance segmentation in histology images focusing towards this direction.Comment: 60 pages, 14 figure

Inclusion of calcium phosphate does not further improve in vitro and in vivo osteogenesis in a novel, highly biocompatible, mechanically stable and 3D printable polymer

At a time of unpredictable challenges for health, one trend is certain: there is an exceedingly high demand for functional implants, particularly bone grafts. This has encouraged the emergence of bone tissue engineering substitutes as an alternative method to conventional bone grafts. However, the current approaches in the field face several limitations that have prevented the ultimate translation into clinical settings. As a result, many attempts have been made to fabricate synthetic bone implants that can offer suitable biological and mechanical properties.Light curable methacrylate-based polymers have ideal properties for bone repair. These materials are also suitable for 3D printing which can be applicable for restoration of both function and aesthetics. The main objective of this research was to investigate the role of calcium phosphate (CaP) incorporation in a mechanically stable, biologically functional and 3D printable polymer for the reconstruction of complex craniofacial defects. The experimental work initially involved the synthesis of (((((((((((3R,3aR,6S,6aR)- hexahydrofuro[3,2-b]furan-3,6-diyl)bis(oxy))bis(ethane-2,1- 48 diyl))bis(oxy))bis(carbonyl))bis(azanediyl))bis(3,3,5-trimethylcyclohexane-5,1- 49 diyl))bis(azanediyl))bis(carbonyl))bis(oxy))bis(ethane-2,1-diyl) bis(2-methylacrylate) referred to as CSMA and fabrication of composite discs via a Digital Light Printing (DLP) method. The flow behaviour of the polymer as a function of CaP addition, surface remineralisation potential, in vitro cell culture, using MC3T3 and Adipose-Derived Mesenchymal Stem Cells (ADSCs) and ex ovo angiogenic response was assessed. Finally, in vivo studies were carried out to investigate neo-bone formation at 4- and 8-weeks post-implantation. Quantitative micro-CT and histological evaluation did not show a higher rate of bone formation in CaP filled CSMA composites compared to CSMA itself. Therefore, such polymeric systems hold promising features by allowing more flexibility in designing a 3D printed scaffold targeted at the reconstruction of maxillofacial defects

Three-dimensional quantification and visualization of vascular networks in engineered tissues

Three-dimensional textural and volumetric image analysis holds great potential in understanding the image data produced by multi-photon microscopy. In this thesis, a tool that provides quantitative textural and morphometric analyzes of vasculature in engineered tissues, alongside with a fast three-dimensional volume rendering is proposed. The investigated 3D artificial tissues consist of Human Umbilical Vein Endothelial Cells (HUVEC) embedded in collagen exposed to two regimes of ultrasound standing wave fields under different pressure conditions. Textural features were evaluated over the extracted connected region in our samples using the normalized Gray Level Co-occurrence Matrix (GLCM) combined with Gray-Level Run Length Matrix (GLRLM) analysis. To minimize the error resulting from any possible volumetric rotation and to provide a comprehensive textural analysis, an averaged version of nine GLCM and GLRLM orientations is used. To evaluate volumetric features, parameters such as volume run length and percentage volume were utilized. The z-projection versions of the samples were used to estimate the tortuosity of the vessels, as well as, to measure the length and the angle of the branches. We utilized a three-dimensional volume rendering technique named MATVTK (derived from MATLAB and VTK) and runs under MATLAB that shows a great improvement on the processing time to reconstruct our volumes compared to MATLAB built-in functions. Results show that our analysis is able to differentiate among the exposed samples, due to morphological changes induced by the ultrasound standing wave fields. Furthermore, we demonstrate that providing more textural parameters than what is currently being reported in the literature, enhances the quantitative understanding of the heterogeneity of artificial tissues