Image analysis in medical imaging: recent advances in selected examples

This book places an emphasis on the basic principles and recent advances in medical imaging technologies. After an introduction, the rest of the text can be categorized into three main parts. Part 1 provides a comprehensive tutorial on the fundamentals of medical imaging modalities and image processing methodologies. Part 2 highlights the cutting-edge imaging modalities for radiography and the recent developments of information technology in biomedicine. Part 3 provides some case studies and applications in radiation therapy and computer-aided diagnosis along with a perspective on future trends in medical and molecular imaging. The book is well organized and well presented. A concise summary before each chapter highlights the most important points of the text. This book can be used as a textbook for graduate courses and as an excellent reference for radiologists, neurologists, biomedical engineers, and researchers who are interested in imaging processing and modeling.

A.P. Dhawan, H.K. Huang, D.-S. Kim, eds. World Scientific Publishing; 2008, 868 pages, $228.00. At first glance at the cover and size of Principles and Advanced Methods in Medical Imaging and Image Analysis , a passerby might think that it is the latest Harry Potter novel; however, the gadgets and

Brain development and atrophy accompany people's life. Brain development diseases, such as autism and Alzheimer's disease, affect a large part of the population. Analyzing brain development is very important in public healthcare, and image registration is essential in medical brain image analysis. Many previous studies investigate registration accuracy by the “ground truth” dataset, marker-based similarity calculation, and expert check to find the best registration algorithms. But the evaluation of image registration technology only at the accuracy level is not comprehensive. Here, we compare the performance of three publicly available registration techniques in brain magnetic resonance imaging (MRI) analysis based on some key features widely used in previous MRI studies for classification and detection tasks. According to the analysis results, SPM12 has a stable speed and success rate, and it always works as a guiding tool for newcomers to medical image analysis. It can preserve maximum contrast information, which will facilitate studies such as tumor diagnosis. FSL is a mature and widely applicable toolkit for users, with a relatively stable success rate and good performance. It has complete functions and its function-based integrated toolbox can meet the requirements of different researchers. AFNI is a flexible and complex tool that is more suitable for professional researchers. It retains most details in medical image analysis, which makes it useful in fine-grained analysis such as volume estimation. Our study provides a new idea for comparing registration tools, where tool selection strategy mainly depends on the research task in which the selected tool can leverage its unique advantages.

Medical image processing and analysis techniques play a significant role in diagnosing diseases. Thus, during the last decade, several noteworthy improvements in medical diagnostics have been made based on medical image processing techniques. In this article, we reviewed articles published in the most important journals and conferences that used or proposed medical image analysis techniques to diagnose diseases. Starting from four scientific databases, we applied the PRISMA technique to efficiently process and refine articles until we obtained forty research articles published in the last five years (2017–2021) aimed at answering our research questions. The medical image processing and analysis approaches were identified, examined, and discussed, including preprocessing, segmentation, feature extraction, classification, evaluation metrics, and diagnosis techniques. This article also sheds light on machine learning and deep learning approaches. We also focused on the most important medical image processing techniques used in these articles to establish the best methodologies for future approaches, discussing the most efficient ones and proposing in this way a comprehensive reference source of methods of medical image processing and analysis that can be very useful in future medical diagnosis systems.

Medical image computing is of growing importance in medical diagnostics and image-guided therapy. Nowadays, image analysis systems integrating advanced image computing methods are used in practice e.g. to extract quantitative image parameters or to support the surgeon during a navigated intervention. However, the grade of automation, accuracy, reproducibility and robustness of medical image computing methods has to be increased to meet the requirements in clinical routine. In the focus theme, recent developments and advances in the field of modeling and model-based image analysis are described. The introduction of models in the image analysis process enables improvements of image analysis algorithms in terms of automation, accuracy, reproducibility and robustness. Furthermore, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients. Selected contributions are assembled to present latest advances in the field. The authors were invited to present their recent work and results based on their outstanding contributions to the Conference on Medical Image Computing BVM 2011 held at the University of Lübeck, Germany. All manuscripts had to pass a comprehensive peer review. Modeling approaches and model-based image analysis methods showing new trends and perspectives in model-based medical image computing are described. Complex models are used in different medical applications and medical images like radiographic images, dual-energy CT images, MR images, diffusion tensor images as well as microscopic images are analyzed. The applications emphasize the high potential and the wide application range of these methods. The use of model-based image analysis methods can improve segmentation quality as well as the accuracy and reproducibility of quantitative image analysis. Furthermore, image-based models enable new insights and can lead to a deeper understanding of complex dynamic mechanisms in the human body. Hence, model-based image computing methods are important tools to improve medical diagnostics and patient treatment in future.

Dynamic physiological information recovery : a sampled-data filtering framework by Shan Tong thesis 2008 xiii, 114 leaves : ill. ; 30 cm In medical imaging and image analysis, the goals of a variety of problems, including cardiac motion estimation and…Read more ›

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a lesser number of training cases than did CNNs. "Deep learning", or ML with image input, in medical imaging is an explosively growing, promising field. It is expected that ML with image input will be the mainstream area in the field of medical imaging in the next few decades.

The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area. One of the reasons for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.

Medical image analysis plays a pivotal role in modern healthcare, aiding clinicians in accurate diagnosis and treatment planning. However, the complexity and diversity of medical images pose significant challenges for traditional image processing methods. Existing methods often struggle to precisely delineate structures in medical images, leading to suboptimal diagnostic accuracy. The demand for automated and accurate segmentation tools in medical imaging has grown, highlighting the necessity for robust and efficient algorithms capable of handling diverse anatomical variations and pathologies. While CNNs have shown promise in image analysis, their application to medical images requires customization to accommodate unique challenges. The literature lacks comprehensive studies that bridge the gap between general-purpose CNNs and the specific demands of medical image segmentation, especially concerning the diverse and intricate structures present in medical imagery. This study addresses the need for advanced techniques by leveraging Convolutional Neural Networks (CNNs) for semantic segmentation in medical image analysis. Our approach involves the design and implementation of a specialized CNN architecture tailored to the nuances of medical image data. We employ state-of-the-art techniques for data preprocessing, model training, and validation. The model is trained on a diverse dataset encompassing various medical imaging modalities, ensuring its adaptability and generalizability. The proposed CNN-based semantic segmentation model demonstrates superior performance in accurately delineating anatomical structures compared to traditional methods. Evaluation metrics, including Dice coefficient and sensitivity, indicate the model efficacy in achieving precise segmentation. The results underscore the potential of CNNs in advancing medical image analysis for improved clinical outcomes.

<p>Patterns in biological and medical data exhibit highly structured geometries. Leveraging geometrical comparisons through quantitative descriptors or via contractive difference measures can facilitate the development of interpretable, unsupervised, and efficient models for clinical diagnosis and biological discovery.</p> <p>In medical image analysis, abnormality-related patterns often exhibit as quantifiable geometrical deformations. Guided by this principle, the following clinical challenges are addressed: a three-dimensional quantitative modeling of bone erosion and proliferation, and the development of robust detection algorithms for real-world applications. First, building upon radiological consensus, a bone erosion is modeled as significant concave regions on the cortical layer and a model-based 3D automatic erosion detection framework is proposed. This approach involves: (a) the cortical surface reconstruction and (b) the curvature-driven erosion identification, implemented via computational differential geometry and level set optimization. Second, the bone proliferation is mathematically formalized by integrating pathological insights with statistical shape analysis. The resulting Deformation-Controllable Elastic Shape (DCES) model provides an unsupervised solution for 3D proliferation quantification.</p> <p>Beyond medical images, the rapid growth of protein structure data demands advanced geometrical comparison methods. While existing approaches primarily rely on sequential geometrical comparisons, non-sequential comparison methods remain underdeveloped. To bridge this gap, the non-sequential comparison is framed as the Entropy-regularized Partial Linear Sum Assignment Problem (epLSAP) and solved by epLSAP-Align, a solver based on Sinkhorn algorithms that enables efficient topology-agnostic comparisons with tunable fidelity-coverage tradeoffs. In summary, these contributions help analyze biomedical structures from geometrical perspectives, enabling practical clinical/biology applications with geometrical insights.</p>

Medical image processing and analysis operations, particularly segmentation, can benefit a great deal from prior information encoded to capture variations over a population in form, shape, anatomic layout, and image appearance of objects. Model/atlas-based methods are extant in medical image segmentation. Although multi-atlas/ multi-model methods have shown improved accuracy for image segmentation, if the atlases/models do not cover representatively the distinct groups, then the methods may not be generalizable to new populations. In a previous study, we have given an answer to address the following problem at image level: How many models/ atlases are needed for optimally encoding prior information to address the differing body habitus factor in a population? However, the number of models for different objects may be different, and at the image level, it may not be possible to infer the number of models needed for each object. So, the modified question to which we are now seeking an answer to in this paper is: How many models/ atlases are needed for optimally encoding prior information to address the differing body habitus factor for each object in a body region? To answer this question, we modified our method in the previous study for seeking the optimum grouping for a given population of images but focusing on the individual objects. We present our results on head and neck computed tomography (CT) scans of 298 patients.

In this article, we describe a framework for distributed statistical analysis of medical images that operates under minimal bandwidth requirements. This framework implements a distributed dynamic recursive partitioning algorithm for medical image analysis that integrates medical image data repositories that contain multiple studies and are potentially located at spatially distributed clinical sites. The goal is to detect associations among regions of interest (ROIs) in images and clinical properties such as the progression of a disease. Statistical descriptors are computed from the ROIs in order to assist in medical decision making by facilitating automatic characterization, classification, and similarity searches of ROIs (e.g., lesions, tumors, and regions of morphological variability). The system consists of a central information fusion site that coordinates the analysis by communicating with remotely located processing sites. The described system has the advantage of keeping bandwidth requirements to a minimum by reducing the amount of data that need to be transferred, while medical decision making is not affected by the data reduction. This benefit makes the proposed distributed medical image analysis framework very suitable for deploying effective telemedical applications.

This work presents general signal detection schemes based on the static fusion of posterior probabilities. Starting with the assumption that for every pixel in an image there is a posterior probability-indicating the probability of the presence or the absence of the signal to be detected, some well-known probability fusion schemes and generalizations thereof are proposed to come to an overall decision regarding the presence or absence of the signal. In addition to these well-known static fusion schemes-i.e., voting, averaging, maximum rule, etcetera, a quantile-based combination rule is presented as well. The performance of the several rules is evaluated on two real-world, medical image analysis task. Both tasks consider the computer-aided diagnosis (CAD) of standard posteroanterior chest radiographs. More specifically, in the first task the general detection of interstitial diseases is studied, while in the second task the focus is on the detection of tuberculosis.

The incorporation of physical information in machine learning frameworks is transforming medical image analysis (MIA). Integrating fundamental knowledge and governing physical laws not only improves analysis performance but also enhances the model’s robustness and interpretability. This work presents a systematic review of over 100 articles on the utility of PINNs dedicated to MIA (PIMIA) tasks. We propose a unified taxonomy to investigate what physics knowledge and processes are modeled, how they are represented, and the strategies to incorporate them into MIA models. We delve deep into a wide range of image analysis tasks, from imaging, generation, prediction, inverse imaging (super-resolution and reconstruction), registration, and image analysis (segmentation and classification). For each task, we thoroughly examine and present the central physics-guided operation, the region of interest (with respect to human anatomy), the corresponding imaging modality, the datasets used for model training, the deep network architectures employed, and the primary physical processes, equations, or principles utilized. Additionally, we also introduce a novel metric to compare the performance of PIMIA methods across different tasks and datasets. Based on this review, we summarize and distill our perspectives on the challenges, and highlight open research questions and directions for future research.

Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience