23 research outputs found
Automatic segmentation of the human thigh muscles in magnetic resonance imaging
Advances in magnetic resonance imaging (MRI) and analysis techniques have improved
diagnosis and patient treatment pathways. Typically, image analysis requires substantial
technical and medical expertise and MR images can su↵er from artefacts, echo and
intensity inhomogeneity due to gradient pulse eddy currents and inherent e↵ects of pulse
radiation on MRI radio frequency (RF) coils that complicates the analysis. Processing
and analysing serial sections of MRI scans to measure tissue volume is an additional
challenge as the shapes and the borders between neighbouring tissues change significantly
by anatomical location. Medical imaging solutions are needed to avoid laborious manual
segmentation of specified regions of interest (ROI) and operator errors.
The work set out in this thesis has addressed this challenge with a specific focus on
skeletal muscle segmentation of the thigh. The aim was to develop an MRI segmentation
framework for the quadriceps muscles, femur and bone marrow. Four contributions of
this research include: (1) the development of a semi-automatic segmentation framework
for a single transverse-plane image; (2) automatic segmentation of a single transverseplane
image; (3) the automatic segmentation of multiple contiguous transverse-plane
images from a full MRI thigh scan; and (4) the use of deep learning for MRI thigh
quadriceps segmentation.
Novel image processing, statistical analysis and machine learning algorithms were developed
for all solutions and they were compared against current gold-standard manual
segmentation. Frameworks (1) and (3) require minimal input from the user to delineate
the muscle border. Overall, the frameworks in (1), (2) and (3) o↵er very good
output performance, with respective framework’s mean segmentation accuracy by JSI
and processing time of: (1) 0.95 and 17 sec; (2) 0.85 and 22 sec; and (3) 0.93 and 3 sec.
For the framework in (4), the ImageNet trained model was customized by replacing the
fully-connected layers in its architecture to convolutional layers (hence the name of Fully
Convolutional Network (FCN)) and the pre-trained model was transferred for the ROI
segmentation task. With the implementation of post-processing for image filtering and
morphology to the segmented ROI, we have successfully accomplished a new benchmark
for thigh MRI analysis. The mean accuracy and processing time with this framework
are 0.9502 (by JSI ) and 0.117 sec per image, respectively
Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review
The medical image analysis field has traditionally been focused on the
development of organ-, and disease-specific methods. Recently, the interest in
the development of more 20 comprehensive computational anatomical models has
grown, leading to the creation of multi-organ models. Multi-organ approaches,
unlike traditional organ-specific strategies, incorporate inter-organ relations
into the model, thus leading to a more accurate representation of the complex
human anatomy. Inter-organ relations are not only spatial, but also functional
and physiological. Over the years, the strategies 25 proposed to efficiently
model multi-organ structures have evolved from the simple global modeling, to
more sophisticated approaches such as sequential, hierarchical, or machine
learning-based models. In this paper, we present a review of the state of the
art on multi-organ analysis and associated computation anatomy methodology. The
manuscript follows a methodology-based classification of the different
techniques 30 available for the analysis of multi-organs and multi-anatomical
structures, from techniques using point distribution models to the most recent
deep learning-based approaches. With more than 300 papers included in this
review, we reflect on the trends and challenges of the field of computational
anatomy, the particularities of each anatomical region, and the potential of
multi-organ analysis to increase the impact of 35 medical imaging applications
on the future of healthcare.Comment: Paper under revie
Advanced Sensing and Image Processing Techniques for Healthcare Applications
This Special Issue aims to attract the latest research and findings in the design, development and experimentation of healthcare-related technologies. This includes, but is not limited to, using novel sensing, imaging, data processing, machine learning, and artificially intelligent devices and algorithms to assist/monitor the elderly, patients, and the disabled population
ERP implementation methodologies and frameworks: a literature review
Enterprise Resource Planning (ERP) implementation is a complex and vibrant process, one that involves a combination of technological and organizational interactions. Often an ERP implementation project is the single largest IT project that an organization has ever launched and requires a mutual fit of system and organization. Also the concept of an ERP implementation supporting business processes across many different departments is not a generic, rigid and uniform concept and depends on variety of factors. As a result, the issues addressing the ERP implementation process have been one of the major concerns in industry. Therefore ERP implementation receives attention from practitioners and scholars and both, business as well as academic literature is abundant and not always very conclusive or coherent. However, research on ERP systems so far has been mainly focused on diffusion, use and impact issues. Less attention has been given to the methods used during the configuration and the implementation of ERP systems, even though they are commonly used in practice, they still remain largely unexplored and undocumented in Information Systems research. So, the academic relevance of this research is the contribution to the existing body of scientific knowledge. An annotated brief literature review is done in order to evaluate the current state of the existing academic literature. The purpose is to present a systematic overview of relevant ERP implementation methodologies and frameworks as a desire for achieving a better taxonomy of ERP implementation methodologies. This paper is useful to researchers who are interested in ERP implementation methodologies and frameworks. Results will serve as an input for a classification of the existing ERP implementation methodologies and frameworks. Also, this paper aims also at the professional ERP community involved in the process of ERP implementation by promoting a better understanding of ERP implementation methodologies and frameworks, its variety and history