The state of the art of medical imaging technology: from creation to archive and back.

Medical imaging has learnt itself well into modern medicine and revolutionized medical industry in the last 30 years. Stemming from the discovery of X-ray by Nobel laureate Wilhelm Roentgen, radiology was born, leading to the creation of large quantities of digital images as opposed to film-based medium. While this rich supply of images provides immeasurable information that would otherwise not be possible to obtain, medical images pose great challenges in archiving them safe from corrupted, lost and misuse, retrievable from databases of huge sizes with varying forms of metadata, and reusable when new tools for data mining and new media for data storing become available. This paper provides a summative account on the creation of medical imaging tomography, the development of image archiving systems and the innovation from the existing acquired image data pools. The focus of this paper is on content-based image retrieval (CBIR), in particular, for 3D images, which is exemplified by our developed online e-learning system, MIRAGE, home to a repository of medical images with variety of domains and different dimensions. In terms of novelties, the facilities of CBIR for 3D images coupled with image annotation in a fully automatic fashion have been developed and implemented in the system, resonating with future versatile, flexible and sustainable medical image databases that can reap new innovations

The State of the Art of Medical Imaging Technology: from Creation to Archive and Back

Medical imaging has learnt itself well into modern medicine and revolutionized medical industry in the last 30 years. Stemming from the discovery of X-ray by Nobel laureate Wilhelm Roentgen, radiology was born, leading to the creation of large quantities of digital images as opposed to film-based medium. While this rich supply of images provides immeasurable information that would otherwise not be possible to obtain, medical images pose great challenges in archiving them safe from corrupted, lost and misuse, retrievable from databases of huge sizes with varying forms of metadata, and reusable when new tools for data mining and new media for data storing become available. This paper provides a summative account on the creation of medical imaging tomography, the development of image archiving systems and the innovation from the existing acquired image data pools. The focus of this paper is on content-based image retrieval (CBIR), in particular, for 3D images, which is exemplified by our developed online e-learning system, MIRAGE, home to a repository of medical images with variety of domains and different dimensions. In terms of novelties, the facilities of CBIR for 3D images coupled with image annotation in a fully automatic fashion have been developed and implemented in the system, resonating with future versatile, flexible and sustainable medical image databases that can reap new innovations

Three-dimensional visualization software assists learning in students with diverse spatial intelligence in medical education

This study evaluated effect of mental rotation (MR) training on learning outcomes and explored effectiveness of teaching via three-dimensional (3D) software among medical students with diverse spatial intelligence. Data from n = 67 student volunteers were included. A preliminary test was conducted to obtain baseline level of MR competency and was utilized to assign participants to two experimental conditions, i.e., trained group (n = 25) and untrained group (n = 42). Data on the effectiveness of training were collected to measure participants\u27 speed and accuracy in performing various MR activities. Six weeks later, a large class format (LCF) session was conducted for all students using 3D software. The usefulness of technology-assisted learning at the LCF was evaluated via a pre- and post-test. Students\u27 feedback regarding MR training and use of 3D software was acquired through questionnaires. MR scores of the trainees improved from 25.9±4.6 points to 28.1±4.4 (P = 0.011) while time taken to complete the tasks reduced from 20.9±3.9 to 12.2±4.4 minutes. Males scored higher than females in all components (P = 0.016). Further, higher pre- and post-test scores were observed in trained (9.0±1.9 and 12.3±1.6) versus untrained group (7.8±1.8; 10.8±1.8). Although mixed-design analysis of variance suggested significant difference in their test scores (P \u3c 0.001), both groups reported similar trend in improvement by means of 3D software (P = 0.54). Ninety-seven percent of students reported technology-assisted learning as an effective means of instruction and found use of 3D software superior to plastic models. Software based on 3D technologies could be adopted as an effective teaching pedagogy to support learning across students with diverse levels of mental rotation abilities

Visual character N-grams for classification and retrieval of radiological images

Diagnostic radiology struggles to maintain high interpretation accuracy. Retrieval of past similar cases would help the inexperienced radiologist in the interpretation process. Character n-gram model has been effective in text retrieval context in languages such as Chinese where there are no clear word boundaries. We propose the use of visual character n-gram model for representation of image for classification and retrieval purposes. Regions of interests in mammographic images are represented with the character n-gram features. These features are then used as input to back-propagation neural network for classification of regions into normal and abnormal categories. Experiments on miniMIAS database show that character n-gram features are useful in classifying the regions into normal and abnormal categories. Promising classification accuracies are observed (83.33%) for fatty background tissue warranting further investigation. We argue that Classifying regions of interests would reduce the number of comparisons necessary for finding similar images from the database and hence would reduce the time required for retrieval of past similar cases

Deep learning in medical imaging and radiation therapy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd

Investigating the User Experience with a 3D Virtual Anatomy Application

Decreasing hours dedicated to teaching anatomy courses and declining use of human cadavers have spurred the need for innovative solutions in teaching anatomy in medical schools. Advancements in virtual reality (VR), 3D visualizations, computer graphics, and medical graphic images have enabled the development of highly interactive 3D virtual applications. Over recent years, variations of interactive systems on computer-mediated environments have been used as supplementary resource for learners. However, despite the growing sophistication of these resources for learning anatomy, studies show that students predominantly prefer traditional methods of learning and hands-on cadaver-based learning over computer-mediated platforms. There is limited research on evaluating user experience in the use of interactive 3D anatomy systems, even though Human-Computer Interaction (HCI) studies show that usability (ease of use) and user engagement are essential to technology adoption and satisfaction. The addressable problem of the research was to investigate how ease of use and flow affected aspects of the students’ engagement experience with the use of a 3D virtual anatomy application. The aim of the study was to evaluate the use of a 3D virtual application in performing dissection learning tasks and to understand aspects of user engagement as assessed by ease of use and flow experience. The flow experience was quantified using the Short Flow State Scale (S FSS-2) and the System Usability Scale (SUS) to measure perceptions about ease of use and user satisfaction. The research questions included: (1) What consequences of flow do students experience? (2) What aspects of the 3D virtual platform are distracting to performing the learning tasks? (3) How do students’ perception of ease of use affect the flow experience based on the SUS and S FSS-2 scores? (4) How do students rate their level of engagement as measured by flow based on their S FSS-2 scores? (5) How does flow help explain student satisfaction and motivation? (6) How do students perceive use of the application to learn anatomy compared with cadaver-based dissection? The study consisted of medical student participants who were asked to complete virtual dissection activities associated with learning objectives in the Structure of the Human Body course to perform using a 3D virtual anatomy application. A subset of participants who completed the learning task and the surveys had a follow-up Cognitive Walkthrough with Think-Aloud Protocol observation activity with an interview segment to gain deeper insights into their user experience with the application. The data from the convergent mixed method analysis indicated that ease of use had some impact on the flow experience and that perceived user satisfaction and motivation were attributed to the interactive 3D visualization design. Seven super-ordinate themes were identified: Ease of Use, Learnability, Interface-Technical, User Satisfaction, Visuospatial, Focus/In the Zone, and CA vs Cadaver. The results have implications for educators (particularly anatomists), educational technologists, and HCI and UX practitioners. Additional research should be conducted using the long version of the Flow State Scale to provide a better understanding of each flow dimension. Further study is recommended with students who have hands-on experience with human cadaver dissection that are also able to compare their experience with the use of a 3D virtual anatomy platform for a direct side-by-side assessment. It would also be helpful to conduct the study as part of the entire duration of the anatomy course and assess how the flow experience impacts student learning performance

Deep learning-enabled technologies for bioimage analysis.

Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases

Cloud-Based Benchmarking of Medical Image Analysis

Medical imagin

Designing ubiquitous computing for reflection and learning in diabetes management

This dissertation proposes principles for the design of ubiquitous health monitoring applications that support reflection and learning in context of diabetes management. Due to the high individual differences between diabetes cases, each affected individual must find the optimal combination of lifestyle alterations and medication through reflective analysis of personal diseases history. This dissertation advocates using technology to enable individuals' proactive engagement in monitoring of their health. In particular, it proposes promoting individuals' engagement in reflection by exploiting breakdowns in individuals' routines or understanding; supporting continuity in thinking that leads to a systematic refinement of ideas; and supporting articulation of thoughts and understanding that helps to transform insights into knowledge. The empirical evidence for these principles was gathered thought the deployment studies of three ubiquitous computing applications that help individuals with diabetes in management of their diseases. These deployment studies demonstrated that technology for reflection helps individuals achieve their personal disease management goals, such as diet goals. In addition, they showed that using technology helps individuals embrace a proactive attitude towards their health indicated by their adoption of the internal locus of control.Ph.D.Committee Chair: Elizabeth D. Mynatt; Committee Member: Abowd, Gregory; Committee Member: Bruckman, Amy; Committee Member: Dourish, Paul; Committee Member: Nersessian, Nanc