Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Image processing for plastic surgery planning

This thesis presents some image processing tools for plastic surgery planning. In particular, it presents a novel method that combines local and global context in a probabilistic relaxation framework to identify cephalometric landmarks used in Maxillofacial plastic surgery. It also uses a method that utilises global and local symmetry to identify abnormalities in CT frontal images of the human body. The proposed methodologies are evaluated with the help of several clinical data supplied by collaborating plastic surgeons

Novel Techniques for Automated Dental Identification

Automated dental identification is one of the best candidates for postmortem identification. With the large number of victims encountered in mass disasters, automating the process of postmortem identification is receiving an increased attention. This dissertation introduces new approaches for different stages of Automated Dental Identification system: These stages include segmentations, classification, labeling, and matching:;We modified the seam carving technique to adapt the problem of segmenting dental image records into individual teeth. We propose a two-stage teeth segmentation approach for segmenting the dental images. In the first stage, the teeth images are preprocessed by a two-step thresholding technique, which starts with an iterative thresholding followed by an adaptive thresholding to binarize the teeth images. In the second stage, we adapt the seam carving technique on the binary images, using both horizontal and vertical seams, to separate each individual tooth. We have obtained an optimality rate of 54.02% for the bitewing type images, which is superior to all existing fully automated dental segmentation algorithms in the literature, and a failure rate of 1.05%. For the periapical type images, we have obtained a high optimality rate of 58.13% and a low failure rate of 0.74 which also surpasses the performance of existing techniques. An important problem in automated dental identification is automatic classification of teeth into four classes (molars, premolars, canines, and incisors). A dental chart is a key to avoiding illogical comparisons that inefficiently consume the limited computational resources, and may mislead decision-making. We tackle this composite problem using a two-stage approach. The first stage, utilizes low computational-cost, appearance-based features, using Orthogonal Locality Preserving Projections (OLPP) for assigning an initial class. The second stage applies a string matching technique, based on teeth neighborhood rules, to validate initial teeth-classes and hence to assign each tooth a number corresponding to its location in the dental chart, even in the presence of a missed tooth. The experimental results of teeth classification show that on a large dataset of bitewing and periapical films, the proposed approach achieves overall classification accuracy of 77% and teeth class validation enhances the overall teeth classification accuracy to 87% which is slightly better than the performance obtained from previous methods based on EigenTeeth the performance of which is 75% and 86%, respectively.;We present a new technique that searches the dental database to find a candidate list. We use dental records of the FBI\u27s Criminal Justice Service (CJIC) ADIS database, that contains 104 records (about 500 bitewing and periapical films) involving more than 2000 teeth, 47 Antemortem (AM) records and 57 Postmortem (PM) records with 20 matched records.;The proposed approach consists of two main stages, the first stage is to preprocess the dental records (segmentation and teeth labeling classification) in order to get a reliable, appearance-based, low computational-cost feature. In the second stage, we developed a technique based on LaplacianTeeth using OLPP algorithm to produce a candidate list. The proposed technique can correctly retrieve the dental records 65% in the 5 top ranks while the method based on EigenTeeth remains at 60%. The proposed approach takes about 0.17 seconds to make record to record comparison while the other method based on EigenTeeth takes about 0.09 seconds.;Finally, we address the teeth matching problem by presenting a new technique for dental record retrieval. The technique is based on the matching of the Scale Invariant feature Transform (SIFT) descriptors guided by the teeth contour between the subject and reference dental records. Our fundamental objective is to accomplish a relatively short match list, with a high probability of having the correct match reference. The proposed technique correctly retrieves the dental records with performance rates of 35% and 75% in the 1 and 5 top ranks respectively, and takes only an average time of 4.18 minutes to retrieve a match list. This compares favorably with the existing technique shape-based (edge direction histogram) method which has the performance rates of 29% and 46% in the 1 and 5 top ranks respectively.;In summary, the proposed ADIS system accurately retrieves the dental record with an overall rate of 80% in top 5 ranks when a candidate list of 20 is used (from potential match search) whereas a candidate size of 10 yields an overall rate of 84% in top 5 ranks and takes only a few minutes to search the database, which compares favorably against most of the existing methods in the literature, when both accuracy and computational complexity are considered

Automated Analysis of Metacarpal Cortical Thickness in Serial Hand Radiographs

To understand the roles of various genes that influence skeletal bone accumulation and loss, accurate measurement of bone mineralization is needed. However, it is a challenging task to accurately assess bone growth over a person\u27s lifetime. Traditionally, manual analysis of hand radiographs has been used to quantify bone growth, but these measurements are tedious and may be impractical for a large-scale growth study. The aim of this project was to develop a tool to automate the measurement of metacarpal cortical bone thickness in standard hand-wrist radiographs of humans aged 3 months to 70+ years that would be more accurate, precise and efficient than manual radiograph analysis. The task was divided into two parts: development of automatic analysis software and the implementation of the routines in a Graphical User Interface (GUI). The automatic analysis was to ideally execute without user intervention, but we anticipated that not all images would be successfully analyzed. The GUI, therefore, provides the interface for the user to execute the program, review results of the automated routines, make semi-automated and manual corrections, view the quantitative results and growth trend of the participant and save the results of all analyses. The project objectives were attained. Of a test set of about 350 images from participants in a large research study, automatic analysis was successful in approximately 75% of the reasonable quality images and manual intervention allowed the remaining 25% of these images to be successfully analyzed. For images of poorer quality, including many that the Lifespan Health Research Center (LHRC) clients would not expect to be analyzed successfully, the inputs provided by the user allowed approximately 80% to be analyzed, but the remaining 20% could not be analyzed with the software. The developed software tool provides results that are more accurate and precise than those from manual analyses. Measurement accuracy, as assessed by phantom measurements, was approximately 0.5% and interobserver and intraobserver agreement were 92.1% and 96.7%, respectively. Interobserver and intraobserver correlation values for automated analysis were 0.9674 and 0.9929, respectively, versus 0.7000 and 0.7820 for manual analysis. The automated analysis process is also approximately 87.5% more efficient than manual image analysis and automatically generates an output file containing over 160 variables of interest. The software is currently being used successfully to analyze over 17,000 images in a study of human bone growth

A review of arthritis diagnosis techniques in artificial intelligence era: Current trends and research challenges

Deep learning, a branch of artificial intelligence, has achieved unprecedented performance in several domains including medicine to assist with efficient diagnosis of diseases, prediction of disease progression and pre-screening step for physicians. Due to its significant breakthroughs, deep learning is now being used for the diagnosis of arthritis, which is a chronic disease affecting young to aged population. This paper provides a survey of recent and the most representative deep learning techniques (published between 2018 to 2020) for the diagnosis of osteoarthritis and rheumatoid arthritis. The paper also reviews traditional machine learning methods (published 2015 onward) and their application for the diagnosis of these diseases. The paper identifies open problems and research gaps. We believe that deep learning can assist general practitioners and consultants to predict the course of the disease, make treatment propositions and appraise their potential benefits

A Survey on Artificial Intelligence Techniques for Biomedical Image Analysis in Skeleton-Based Forensic Human Identification

This paper represents the first survey on the application of AI techniques for the analysis of biomedical images with forensic human identification purposes. Human identification is of great relevance in today’s society and, in particular, in medico-legal contexts. As consequence, all technological advances that are introduced in this field can contribute to the increasing necessity for accurate and robust tools that allow for establishing and verifying human identity. We first describe the importance and applicability of forensic anthropology in many identification scenarios. Later, we present the main trends related to the application of computer vision, machine learning and soft computing techniques to the estimation of the biological profile, the identification through comparative radiography and craniofacial superimposition, traumatism and pathology analysis, as well as facial reconstruction. The potentialities and limitations of the employed approaches are described, and we conclude with a discussion about methodological issues and future research.Spanish Ministry of Science, Innovation and UniversitiesEuropean Union (EU) PGC2018-101216-B-I00Regional Government of Andalusia under grant EXAISFI P18-FR-4262Instituto de Salud Carlos IIIEuropean Union (EU) DTS18/00136European Commission H2020-MSCA-IF-2016 through the Skeleton-ID Marie Curie Individual Fellowship 746592Spanish Ministry of Science, Innovation and Universities-CDTI, Neotec program 2019 EXP-00122609/SNEO-20191236European Union (EU)Xunta de Galicia ED431G 2019/01European Union (EU) RTI2018-095894-B-I0

A deep learning algorithm for contour detection in synthetic 2D biplanar X-ray images of the scapula: towards improved 3D reconstruction of the scapula

Three-dimensional (3D) reconstruction from X-ray images using statistical shape models (SSM) provides a cost-effective way of increasing the diagnostic utility of two-dimensional (2D) X-ray images, especially in low-resource settings. The landmark-constrained model fitting approach is one way to obtain patient-specific models from a statistical model. This approach requires an accurate selection of corresponding features, usually landmarks, from the bi-planar X-ray images. However, X-ray images are 2D representations of 3D anatomy with super-positioned structures, which confounds this approach. The literature shows that detection and use of contours to locate corresponding landmarks within biplanar X-ray images can address this limitation. The aim of this research project was to train and validate a deep learning algorithm for detection the contour of a scapula in synthetic 2D bi-planar Xray images. Synthetic bi-planar X-ray images were obtained from scapula mesh samples with annotated landmarks generated from a validated SSM obtained from the Division of Biomedical Engineering, University of Cape Town. This was followed by the training of two convolutional neural network models as the first objective of the project; the first model was trained to predict the lateral (LAT) scapula image given the anterior-posterior (AP) image. The second model was trained to predict the AP image given the LAT image. The trained models had an average Dice coefficient value of 0.926 and 0.964 for the predicted LAT and AP images, respectively. However, the trained models did not generalise to the segmented real X-ray images of the scapula. The second objective was to perform landmark-constrained model fitting using the corresponding landmarks embedded in the predicted images. To achieve this objective, the 2D landmark locations were transformed into 3D coordinates using the direct linear transformation. The 3D point localization yielded average errors of (0.35, 0.64, 0.72) mm in the X, Y and Z directions, respectively, and a combined coordinate error of 1.16 mm. The reconstructed landmarks were used to reconstruct meshes that had average surface-to-surface distances of 3.22 mm and 1.72 mm for 3 and 6 landmarks, respectively. The third objective was to reconstruct the scapula mesh using matching points on the scapula contour in the bi-planar images. The average surface-to-surface distances of the reconstructed meshes with 8 matching contour points and 6 corresponding landmarks of the same meshes were 1.40 and 1.91 mm, respectively. In summary, the deep learning models were able to learn the mapping between the bi-planar images of the scapula. Increasing the number of corresponding landmarks from the bi-planar images resulted into better 3D reconstructions. However, obtaining these corresponding landmarks was non-trivial, necessitating the use of matching points selected from the scapulae contours. The results from the latter approach signal a need to explore contour matching methods to obtain more corresponding points in order to improve the scapula 3D reconstruction using landmark-constrained model fitting