A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Development of algorithms and methods for three-dimensional image analysis and biomedical applications

2010/2011Tomographic imaging is both the science and the tool to explore the internal structure of objects. The mission is to use images to characterize the static and/or dynamic properties of the imaged object in order to further integrate these properties into principles, laws or theories. Among the recent trends in tomographic imaging, three- dimensional (3D) methods are gaining preference and there is the quest for overcoming the bare qualitative observation towards the extraction of quantitative parameters directly from the acquired images. To this aim, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), as well as the related micro-scale techniques (μ-CT and μ-MRI), are promising tools for all the fields of science in which non-destructive tests are required. In order to support the interpretation of the images produced by these techniques, there is a growing demand of reliable image analysis methods for the specific 3D domain. The aim of this thesis is to present approaches for effective and efficient three-dimensional image analysis with special emphasis on porous media analysis. State-of-the art as well as innovative tools are included in a special software and hardware solution named Pore3D, developed in a collaboration with the Italian 3rd generation synchrotron laboratory Elettra (Basovizza - Trieste, Italy). Algorithms and methods for the characterization of different kinds of porous media are described. The key steps of image segmentation and skeletonization of the segmented pore space are also discussed in depth. Three different clinical and biomedical applications of quantitative analysis of tomographic images are presented. The reported applications have in common the characterization of the micro-architecture of trabecular bone. The trabecular (or cancellous) bone is a 3D mesh- work of bony trabeculae and void spaces containing the bone marrow. It can then be thought of as a porous medium with an interconnected porous space. To be more specific, the first application aims at characterizing a structure (a tissue engineering scaffold) that has to mimic the architecture of trabecular bone. The relevant features of porosity, pore- and throat-size distributions, connectivity and structural anisotropy indexes are automatically extracted from μ-CT images. The second application is based on ex vivo experiments carried out on femurs and lumbar spines of mice affected by microgravity conditions. Wild type and transgenic mice were hosted in the International Space Station (ISS) for 3 months and the observed bone loss due to the near-zero gravity was quantified by means of synchrotron radiation μ-CT image analysis. Finally, the results of an in vivo study on the risk of fracture in osteoporotic subjects is reported. The study is based on texture analysis of high resolution clinical magnetic resonance (MR) images.XXIV Ciclo198

Biphasic calcium phosphate biomaterials: Stem Cell-derived Osteoinduction or In-vivo Osteoconduction? Novel insights in Maxillary Sinus Augmentation by advanced Imaging

none11noMaxillary sinus augmentation is often necessary prior to implantology procedure, in particular in cases of atrophic posterior maxilla. In this context, bone substitute biomaterials made of biphasic calcium phosphates, produced by three-dimensional additive manufacturing were shown to be highly biocompatible with an efficient osteoconductivity, especially when combined with cell-based tissue engineering. Thus, in the present research, osteoinduction and osteoconduction properties of biphasic calcium-phosphate constructs made by direct rapid prototyping and engineered with ovine-derived amniotic epithelial cells or amniotic fluid cells were evaluated. More in details, this preclinical study was performed using adult sheep targeted to receive scaffold alone (CTR), oAFSMC, or oAEC engineered constructs. The grafted sinuses were explanted at 90 days and a cross-linked experimental approach based on Synchrotron Radiation microCT and histology analysis was performed on the complete set of samples. The study, performed taking into account the distance from native surrounding bone, demonstrated that no significant differences occurred in bone regeneration between oAEC-, oAFMSC-cultured, and Ctr samples and that there was a predominant action of the osteoconduction versus the stem cells osteo-induction. Indeed, it was proven that the newly formed bone amount and distribution decreased from the side of contact scaffold/native bone toward the bulk of the scaffold itself, with almost constant values of morphometric descriptors in volumes more than 1 mm from the border.openGiovanna Iezzi, Antonio Scarano, Luca Valbonetti, Serena Mazzoni, Michele Furlani, Carlo Mangano, Aurelio Muttini, Mario Raspanti, Barbara Barboni, Adriano Piattelli, Alessandra GiulianiIezzi, Giovanna; Scarano, Antonio; Valbonetti, Luca; Mazzoni, Serena; Furlani, Michele; Mangano, Carlo; Muttini, Aurelio; Raspanti, Mario; Barboni, Barbara; Piattelli, Adriano; Giuliani, Alessandr

Texture analysis and Its applications in biomedical imaging: a survey

Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This survey’s emphasis is in collecting and categorising over five decades of active research on texture analysis.Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this survey’s final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.Manuscript received February 3, 2021; revised June 23, 2021; accepted September 21, 2021. Date of publication September 27, 2021; date of current version January 24, 2022. This work was supported in part by the Portuguese Foundation for Science and Technology (FCT) under Grants PTDC/EMD-EMD/28039/2017, UIDB/04950/2020, PestUID/NEU/04539/2019, and CENTRO-01-0145-FEDER-000016 and by FEDER-COMPETE under Grant POCI-01-0145-FEDER-028039. (Corresponding author: Rui Bernardes.)info:eu-repo/semantics/publishedVersio

Texture Analysis of Diffraction Enhanced Synchrotron Images of Trabecular Bone at the Wrist

The purpose of this study is to determine the correlation between texture features of Di raction Enhanced Imaging (DEI) images and trabecular properties of human wrist bone in the assessment of osteoporosis. Osteoporosis is a metabolic bone disorder that is characterized by reduced bone mass and a deterioration of bone structure which results in an increased fracture risk. Since the disease is preventable, diagnostic techniques are of major importance. Bone micro-architecture and Bone mineral density (BMD) are two main factors related to osteoporotic fractures. Trabecular properties like bone volume (BV), trabecular number (Tb.N), trabecular thickness (Tb.Th), bone surface (BS), and other properties of bone, characterizes the bone architecture. Currently, however, BMD is the only measurement carried out to assess osteoporosis. Researchers suggest that bone micro-architecture and texture analysis of bone images along with BMD can provide more accuracy in the assessment. We have applied texture analysis on DEI images and extracted texture features. In our study, we used fractal analysis, gray level co-occurrence matrix (GLCM), texture feature coding method (TFCM), and local binary patterns (LBP) as texture analysis methods to extract texture features. 3D Micro-CT trabecular properties were extracted using SkyScanTM CTAN software. Then, we determined the correlation between texture features and trabecular properties. GLCM energy fea- ture of DEI images explained more than 39% of variance in bone surface by volume ratio (BS/BV), 38% of variance in percent bone volume (BV/TV), and 37% of variance in trabecular number (Tb.N). TFCM homogeneity feature of DEI images explained more than 42% of variance in bone surface (BS) parameter. LBP operator - LBP 11 of DEI images explained more than 34% of vari- ance in bone surface (BS) and 30% of variance in bone surface density (BS/TV). Fractal dimension parameter of DEI images explained more than 47% of variance in bone surface (BS) and 32% of variance in bone volume (BV). This study will facilitate in the quanti cation of osteoporosis beyond conventional BMD

Multi-modal matching of 2D images with 3D medical data

Image registration is the process of aligning images of the same object taken at different time points or with different imaging modalities with the aim to compare them in one coordinate system. Image registration is particularly important in biomedical imaging, where a multitude of imaging modalities exist. For example, images can be obtained with X-ray computed tomography (CT) which is based on the object’s X-ray beam attenuation whereas magnetic resonance imaging (MRI) underlines its local proton density. The gold standard in pathology for tissue analysis is histology. Histology, however, provides only 2D information in the selected sections of the 3D tissue. To evaluate the tissue’s 3D structure, volume imaging techniques, such as CT or MRI, are preferable. The combination of functional information from histology with 3D morphological data from CT is essential for tissue analysis. Furthermore, histology can validate anatomical features identified in CT data. Therefore, the registration of these two modalities is indispensable to provide a more complete overview of the tissue. Previously proposed algorithms for the registration of histological slides into 3D volumes usually rely on manual interactions, which is time-consuming and prone to bias. The high complexity of this type of registration originates from the large number of degrees of freedom. The goal of my thesis was to develop an automatic method for histology to 3D volume registration to master these challenges. The first stage of the developed algorithm uses a scale-invariant feature detector to find common matches between the histology slide and each tomography slice in a 3D dataset. A plane of the most likely position is then fitted into the feature point cloud using a robust model fitting algorithm. The second stage builds upon the first one and introduces fine-tuning of the slice position using normalized Mutual Information (NMI). Additionally, using previously developed 2D-2D registration techniques we find the rotation and translation of the histological slide within the plane. Moreover, the framework takes into account any potential nonlinear deformations of the histological slides that might occur during tissue preparation. The application of the algorithm to MRI data is investigated in our third work. The developed extension of the multi-modal feature detector showed promising results, however, the registration of a histological slide to the direct MRI volume remains a challenging task