Tracking and Mapping in Medical Computer Vision: A Review

As computer vision algorithms are becoming more capable, their applications in clinical systems will become more pervasive. These applications include diagnostics such as colonoscopy and bronchoscopy, guiding biopsies and minimally invasive interventions and surgery, automating instrument motion and providing image guidance using pre-operative scans. Many of these applications depend on the specific visual nature of medical scenes and require designing and applying algorithms to perform in this environment. In this review, we provide an update to the field of camera-based tracking and scene mapping in surgery and diagnostics in medical computer vision. We begin with describing our review process, which results in a final list of 515 papers that we cover. We then give a high-level summary of the state of the art and provide relevant background for those who need tracking and mapping for their clinical applications. We then review datasets provided in the field and the clinical needs therein. Then, we delve in depth into the algorithmic side, and summarize recent developments, which should be especially useful for algorithm designers and to those looking to understand the capability of off-the-shelf methods. We focus on algorithms for deformable environments while also reviewing the essential building blocks in rigid tracking and mapping since there is a large amount of crossover in methods. Finally, we discuss the current state of the tracking and mapping methods along with needs for future algorithms, needs for quantification, and the viability of clinical applications in the field. We conclude that new methods need to be designed or combined to support clinical applications in deformable environments, and more focus needs to be put into collecting datasets for training and evaluation.Comment: 31 pages, 17 figure

Program: Graduate Research Achievement Day 2017

Full program for 2017 Graduate Research Achievement Day.https://digitalcommons.odu.edu/graduateschool_achievementday2017-18_programs/1001/thumbnail.jp

ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model

The ChatGPT, a lite and conversational variant of Generative Pretrained Transformer 4 (GPT-4) developed by OpenAI, is one of the milestone Large Language Models (LLMs) with billions of parameters. LLMs have stirred up much interest among researchers and practitioners in their impressive skills in natural language processing tasks, which profoundly impact various fields. This paper mainly discusses the future applications of LLMs in dentistry. We introduce two primary LLM deployment methods in dentistry, including automated dental diagnosis and cross-modal dental diagnosis, and examine their potential applications. Especially, equipped with a cross-modal encoder, a single LLM can manage multi-source data and conduct advanced natural language reasoning to perform complex clinical operations. We also present cases to demonstrate the potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical application. While LLMs offer significant potential benefits, the challenges, such as data privacy, data quality, and model bias, need further study. Overall, LLMs have the potential to revolutionize dental diagnosis and treatment, which indicates a promising avenue for clinical application and research in dentistry

CT Scanning

Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society

The development of a soft tissue mimicking hydrogel: Mechanical characterisation and 3D printing

Accurate tissue phantoms are difficult to design due to the complex hyperelastic, viscoelastic and biphasic properties of real soft tissues. The aim of this work is to demonstrate the tissue mimicking ability of a composite hydrogel (CH), constituting of poly(vinyl alcohol) (PVA) and phytagel (PHY), as a soft tissue phantom over a range mechanical properties, for a variety of biomedical and tissue engineering applications. Its compressive stress-strain behaviour, relaxation response, tensile impact stresses and surgical needle-tissue interactions were mapped and characterised with respect to its constituent hydrogel formulation. The mechanical characterisation of biological tissues was also investigated and the results were used as the ground truth for mimicking. The best mimicking hydrogel compositions were determined by combining the most relevant mechanical properties for each desired application. This thesis demonstrates the use of the tissue mimicking composite hydrogel formulations as tissue phantoms for various surgical procedures, including convection enhanced drug delivery, and traumatic brain injury studies. To expand the applications of the CH, a preliminary biological evaluation of the hydrogel was performed using human dermal fibroblasts. Cell seeded on the collagen-coated composite hydrogel showed good attachment and viability. Finally, a novel fabrication method with the aim of creating samples that replicate the anisotropic properties of biological tissues was developed. A cryogenic 3D printing method utilising the liquid to solid phase change of the composite hydrogel ink was achieved by rapidly cooling the ink solution below its freezing point. The setup was able to successfully create complex 3D brain mimicking material. The method was validated by showing that the mechanical and microstructural properties of the 3D printed material was well matched to its cast-moulded equivalent. This greatly widens the applications of the CH as a mechanically accurate tool for in-vitro testing and also demonstrates promise for future mechanobiology and tissue engineering studies.Open Acces

AI in Medical Imaging Informatics: Current Challenges and Future Directions

This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine