A Review of Three-Dimensional Medical Image Visualization

Video frame interpolation neural network for 3D tomography across different length scales

Three-dimensional (3D) tomography is a powerful investigative tool for many scientific domains, going from materials science, to engineering, to medicine. Many factors may limit the 3D resolution, often spatially anisotropic, compromising the precision of the information retrievable. A neural network, designed for video-frame interpolation, is employed to enhance tomographic images, achieving cubic-voxel resolution. The method is applied to distinct domains: the investigation of the morphology of printed graphene nanosheets networks, obtained via focused ion beam-scanning electron microscope (FIB-SEM), magnetic resonance imaging of the human brain, and X-ray computed tomography scans of the abdomen. The accuracy of the 3D tomographic maps can be quantified through computer-vision metrics, but most importantly with the precision on the physical quantities retrievable from the reconstructions, in the case of FIB-SEM the porosity, tortuosity, and effective diffusivity. This work showcases a versatile image-augmentation strategy for optimizing 3D tomography acquisition conditions, while preserving the information content.

Brain image data processing using collaborative data workflows on Texera

In the realm of neuroscience, mapping the three-dimensional (3D) neural circuitry and architecture of the brain is important for advancing our understanding of neural circuit organization and function. This study presents a novel pipeline that transforms mouse brain samples into detailed 3D brain models using a collaborative data analytics platform called "Texera." The user-friendly Texera platform allows for effective interdisciplinary collaboration between team members in neuroscience, computer vision, and data processing. Our pipeline utilizes the tile images from a serial two-photon tomography/TissueCyte system, then stitches tile images into brain section images, and constructs 3D whole-brain image datasets. The resulting 3D data supports downstream analyses, including 3D whole-brain registration, atlas-based segmentation, cell counting, and high-resolution volumetric visualization. Using this platform, we implemented specialized optimization methods and obtained significant performance enhancement in workflow operations. We expect the neuroscience community can adopt our approach for large-scale image-based data processing and analysis.

Research on Artificial-Intelligence-Assisted Medicine: A Survey on Medical Artificial Intelligence

With the improvement of economic conditions and the increase in living standards, people's attention in regard to health is also continuously increasing. They are beginning to place their hopes on machines, expecting artificial intelligence (AI) to provide a more humanized medical environment and personalized services, thus greatly expanding the supply and bridging the gap between resource supply and demand. With the development of IoT technology, the arrival of the 5G and 6G communication era, and the enhancement of computing capabilities in particular, the development and application of AI-assisted healthcare have been further promoted. Currently, research on and the application of artificial intelligence in the field of medical assistance are continuously deepening and expanding. AI holds immense economic value and has many potential applications in regard to medical institutions, patients, and healthcare professionals. It has the ability to enhance medical efficiency, reduce healthcare costs, improve the quality of healthcare services, and provide a more intelligent and humanized service experience for healthcare professionals and patients. This study elaborates on AI development history and development timelines in the medical field, types of AI technologies in healthcare informatics, the application of AI in the medical field, and opportunities and challenges of AI in the field of medicine. The combination of healthcare and artificial intelligence has a profound impact on human life, improving human health levels and quality of life and changing human lifestyles.

Aorta Segmentation in 3D CT Images by Combining Image Processing and Machine Learning Techniques

PURPOSE

Aorta segmentation is extremely useful in clinical practice, allowing the diagnosis of numerous pathologies, such as dissections, aneurysms and occlusive disease. In such cases, image segmentation is prerequisite for applying diagnostic algorithms, which in turn allow the prediction of possible complications and enable risk assessment, which is crucial in saving lives. The aim of this paper is to present a novel fully automatic 3D segmentation method, which combines basic image processing techniques and more advanced machine learning algorithms, for detecting and modelling the aorta in 3D CT imaging data.

METHODS

An initial intensity threshold-based segmentation procedure is followed by a classification-based segmentation approach, based on a Markov Random Field network. The result of the proposed two-stage segmentation process is modelled and visualized.

RESULTS

The proposed methodology was applied to 16 3D CT data sets and the extracted aortic segments were reconstructed as 3D models. The performance of segmentation was evaluated both qualitatively and quantitatively against other commonly used segmentation techniques, in terms of the accuracy achieved, compared to the actual aorta, which was defined manually by experts.

CONCLUSION

The proposed methodology achieved superior segmentation performance, compared to all compared segmentation techniques, in terms of the accuracy of the extracted 3D aortic model. Therefore, the proposed segmentation scheme could be used in clinical practice, such as in treatment planning and assessment, as it can speed up the evaluation of the medical imaging data, which is commonly a lengthy and tedious process.

A look at radiation detectors and their applications in medical imaging

The effectiveness and precision of disease diagnosis and treatment have increased, thanks to developments in clinical imaging over the past few decades. Science is developing and progressing steadily in imaging modalities, and effective outcomes are starting to show up as a result of the shorter scanning periods needed as well as the higher-resolution images generated. The choice of one clinical device over another is influenced by technical disparities among the equipment, such as detection medium, shorter scan time, patient comfort, cost-effectiveness, accessibility, greater sensitivity and specificity, and spatial resolution. Lately, computational algorithms, artificial intelligence (AI), in particular, have been incorporated with diagnostic and treatment techniques, including imaging systems. AI is a discipline comprised of multiple computational and mathematical models. Its applications aided in manipulating sophisticated data in imaging processes and increased imaging tests' accuracy and precision during diagnosis. Computed tomography (CT), positron emission tomography (PET), and Single Photon Emission Computed Tomography (SPECT) along with their corresponding radiation detectors have been reviewed in this study. This review will provide an in-depth explanation of the above-mentioned imaging modalities as well as the radiation detectors that are their essential components. From the early development of these medical instruments till now, various modifications and improvements have been done and more is yet to be established for better performance which calls for a necessity to capture the available information and record the gaps to be filled for better future advances.

Seeing Your Stories: Visualization for Narrative Medicine

Importance: Narrative medicine (NM), in which patient stories play a crucial role in their diagnosis and treatment, can potentially support a more holistic approach to patient care than traditional scientific ones. However, there are some challenges in the implementation of narrative medicine, for example, differences in understanding illnesses between physicians and patients and physicians' increased workloads and overloaded schedules. This paper first presents a review to explore previous visualization research for narrative medicine to bridge the gap between visualization researchers and narrative medicine experts and explore further visualization opportunities. Highlights: The review is conducted from 2 perspectives: (a) the contexts and domains in which visualization has been explored for narrative medicine and (b) the forms and solutions applied in these studies. Four applied domains are defined, including understanding patients from narrative records, medical communication, medical conversation training in education, and psychotherapy and emotional wellness enhancement. Conclusions: A future work framework illustrates some opportunities for future research, including groups of specific directions and future points for the 4 domains and 3 technological exploration opportunities (combination of narrative and medical data visualization, task-audience-based visual storytelling, and user-centered interactive visualization). Specifically, 3 directions of future work in medical communication (asynchronous online physician-patient communication, synchronous face-to-face medical conversation, and medical knowledge dissemination) were concluded.

Modern Image-Guided Surgery: A Narrative Review of Medical Image Processing and Visualization

Medical image analysis forms the basis of image-guided surgery (IGS) and many of its fundamental tasks. Driven by the growing number of medical imaging modalities, the research community of medical imaging has developed methods and achieved functionality breakthroughs. However, with the overwhelming pool of information in the literature, it has become increasingly challenging for researchers to extract context-relevant information for specific applications, especially when many widely used methods exist in a variety of versions optimized for their respective application domains. By being further equipped with sophisticated three-dimensional (3D) medical image visualization and digital reality technology, medical experts could enhance their performance capabilities in IGS by multiple folds. The goal of this narrative review is to organize the key components of IGS in the aspects of medical image processing and visualization with a new perspective and insights. The literature search was conducted using mainstream academic search engines with a combination of keywords relevant to the field up until mid-2022. This survey systemically summarizes the basic, mainstream, and state-of-the-art medical image processing methods as well as how visualization technology like augmented/mixed/virtual reality (AR/MR/VR) are enhancing performance in IGS. Further, we hope that this survey will shed some light on the future of IGS in the face of challenges and opportunities for the research directions of medical image processing and visualization.

Seeing the unseen: Comparison study of representation approaches for biochemical processes in education

The representations of biochemical processes must balance visual portrayals with descriptive content to be an effective learning tool. To determine what type of representation is the most suitable for education, we designed five different representations of adenosine triphosphate (ATP) synthesis and examined how they are perceived. Our representations consisted of an overview of the process in a detailed and abstract illustrative format, continuous video formats with and without narration, and a combined illustrative overview with dynamic components. The five representations were evaluated by non-experts who were randomly assigned one of them and experts who viewed and compared all five representations. Subsequently, we conducted a focus group on the outcomes of these evaluations, which gave insight into possible explanations of our results, where the non-experts preferred the detailed static representation and found the narrated video least helpful, in contradiction to the experts who favored the narrated video the most.

Insights and trends review: the role of three-dimensional technology in upper extremity surgery

The use of three-dimensional (3-D) technology in upper extremity surgery has the potential to revolutionize the way that hand and upper limb procedures are planned and performed. 3-D technology can assist in the diagnosis and treatment of conditions, allowing virtual preoperative planning and surgical templating. 3-D printing can allow the production of patient-specific jigs, instruments and implants, allowing surgeons to plan and perform complex procedures with greater precision and accuracy. Previously, cost has been a barrier to the use of 3-D technology, which is now falling rapidly. This review article will discuss the current status of 3-D technology and printing, including its applications, ethics and challenges in hand and upper limb surgery. We have provided case examples to outline how clinicians can incorporate 3-D technology in their clinical practice for congenital deformities, management of acute fracture and malunion and arthroplasty.