47 research outputs found
Automatic segmentation of the sphenoid sinus in CT-scans volume with DeepMedics 3D CNN architecture
Today, researchers are increasingly using manual, semi-automatic, and automatic segmentation techniques to delimit or extract organs from medical images. Deep learning algorithms are increasingly being used in the area of medical imaging analysis. In comparison to traditional methods, these algorithms are more efficient to obtain compact information, which considerably enhances the quality of medical image analysis system. In this paper, we present a new method to fully automatic segmentation of the sphenoid sinus using a 3D (convolutional neural network). The scarcity of medical data initially forced us through this study to use a 3D CNN model learned on a small data set. To make our method fully automatic, preprocessing and post processing are automated with extraction techniques and mathematical morphologies. The proposed tool is compared to a semi-automatic method and manual deductions performed by a specialist. Preliminary results from CT volumes appear very promising
Deep learning-based fully automatic segmentation of the maxillary sinus on cone-beam computed tomographic images
The detection of maxillary sinus wall is important in dental fields such as implant surgery, tooth extraction, and odontogenic disease diagnosis. The accurate segmentation of the maxillary sinus is required as a cornerstone for diagnosis and treatment planning. This study proposes a deep learning-based method for fully automatic segmentation of the maxillary sinus, including clear or hazy states, on cone-beam computed tomographic (CBCT) images. A model for segmentation of the maxillary sinuses was developed using U-Net, a convolutional neural network, and a total of 19,350 CBCT images were used from 90 maxillary sinuses (34 clear sinuses, 56 hazy sinuses). Post-processing to eliminate prediction errors of the U-Net segmentation results increased the accuracy. The average prediction results of U-Net were a dice similarity coefficient (DSC) of 0.9090 ± 0.1921 and a Hausdorff distance (HD) of 2.7013 ± 4.6154. After post-processing, the average results improved to a DSC of 0.9099 ± 0.1914 and an HD of 2.1470 ± 2.2790. The proposed deep learning model with post-processing showed good performance for clear and hazy maxillary sinus segmentation. This model has the potential to help dental clinicians with maxillary sinus segmentation, yielding equivalent accuracy in a variety of cases.ope
Development of FOTOM 2008 Plus Module for Automatic Segmentation of Nasal Cavities in CT Imaging for Subsequent Evaluation
DiplomovĂĄ prĂĄce se zabĂœvĂĄ vĂœvojem modulu pro systĂ©m FOTOM 2008 Plus. SystĂ©m FOTOM byl vytvoĆen jako fotogrammetrickĂœ systĂ©m pro analĂœzu a hodnocenĂ objektĆŻ na snĂmcĂch. Ten slouĆŸĂ k nĂĄslednĂ©mu hodnocenĂ dutin. DiplomovĂĄ prĂĄce je rozdÄlena do nÄkolika ÄĂĄstĂ. V prvnĂ ÄĂĄsti se zabĂœvĂĄm problematikou segmentace paranazĂĄlnĂch dutin a jejich anatomickou stavbou. NĂĄsledujĂcĂ ÄĂĄst je zamÄĆena na vĂœbÄr vhodnĂ©ho typu filtru CT snĂmkĆŻ. DĂĄle byl vytvoĆen modul vyuĆŸĂvajĂcĂ aktivnĂ kontury pro detekci objektĆŻ na CT snĂmcĂch a vytvoĆenĂ vĂœstupnĂch textovĂœch souborĆŻ obsahujĂcĂ informace o objektech na snĂmcĂch. V poslednĂ ÄĂĄsti je uveden nĂĄvrh statistickĂ©ho ĆeĆĄenĂ pro budoucĂ vĂœzkum na klinice otorinolaryngologie a chirurgie hlavy a krku ve FakultnĂ nemocnici Ostrava. SystĂ©m FOTOM 2008 Plus s tĂmto modulem je zde vyuĆŸĂvĂĄn jako nĂĄstroj pro mÄĆenĂ objemu paranazĂĄlnĂch dutin.This diploma thesis deals with development module of the FOTOM 2008 Plus. The FOTOM was created as a photogrammetric system for analyzing and evaluating objects in images. It is used for subsequent evaluation of cavities. The thesis is divided into several parts. In the first part I deal with the problems of the segmentation paranasal sinuses and their anatomical structure. The following section focuses on selecting the appropriate filter in CT images. In addition, module was created using active contours to detect objects in CT images and create an output text file containing information about objects in the images. In the last part there is a proposal of a statistical solution for future research at the department of otorhinolaryngology and head and neck surgery at the University hospital Ostrava. Here the FOTOM 2008 Plus with module is used as a tool for measuring the volume of paranasal sinuses.450 - Katedra kybernetiky a biomedicĂnskĂ©ho inĆŸenĂœrstvĂvelmi dobĆ
Automated Segmentation of Temporal Bone Structures
Mastoidectomy is a challenging surgical procedure that is difficult to perform and practice. As supplementation to current training techniques, surgical simulators have been developed with the ability to visualize and operate on temporal bone anatomy. Medical image segmentation is done to create three-dimensional models of anatomical structures for simulation. Manual segmentation is an accurate but time-consuming process that requires an expert to label each structure on images. An automatic method for segmentation would allow for more practical model creation. The objective of this work was to create an automated segmentation algorithm for structures of the temporal bone relevant to mastoidectomy. The first method explored was multi-atlas based segmentation of the sigmoid sinus which produced accurate and consistent results. In order to segment other structures and improve robustness and accuracy, two convolutional neural networks were compared. The convolutional neural network implementation produced results that were more accurate than previously published work
Role of Imaging and AI in the Evaluation of COVID-19 Infection: A Comprehensive Survey
Coronavirus disease 2019 (COVID-19) is a respiratory illness that started and rapidly became the pandemic of the century, as the number of people infected with it globally exceeded 253.4 million. Since the beginning of the pandemic of COVID-19, over two years have passed. During this hard period, several defies have been coped by the scientific society to know this novel disease, evaluate it, and treat affected patients. All these efforts are done to push back the spread of the virus. This article provides a comprehensive review to learn about the COVID-19 virus and its entry mechanism, its main repercussions on many organs and tissues of the body, identify its symptoms in the short and long terms, in addition to recognize the role of diagnosis imaging in COVID-19. Principally, the quick evolution of active vaccines act an exceptional accomplishment where leaded to decrease rate of death worldwide. However, some hurdels still have to be overcome. Many proof referrers that infection with CoV-19 causes neurological dis function in a substantial ratio of influenced patients, where these symptoms appear severely during the infection and still less is known about the potential long term consequences for the brain, where Loss of smell is a neurological sign and rudimentary symptom of COVID-19. Hence, we review the causes of olfactory bulb dysfunction and Anosmia associated with COVID-19, the latest appropriate therapeutic strategies for the COVID-19 treatment (e.g., the ACE2 strategy and the Ang II receptor), and the tests through the follow-up phases. Additionally, we discuss the long-term complications of the virus and thus the possibility of improving therapeutic strategies. Moreover, the main steps of artificial intelligence that have been used to foretell and early diagnose COVID-19 are presented, where Artificial intelligence, especially machine learning is emerging as an effective approach for diagnostic image analysis with performance in the discriminate diagnosis of injuries of COVID-19 on multiple organs, comparable to that of human practitioners. The followed methodology to prepare the current survey is to search the related work concerning the mentioned topic from different journals, such as Springer, Wiley, and Elsevier. Additionally, different studies have been compared, the results are collected and then reported as shown. The articles are selected based on the year (i.e., the last three years). Also, different keywords were checked (e.g., COVID-19, COVID-19 Treatment, COVID-19 Symptoms, and COVID-19 and Anosmia)
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 todayâs 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
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SCOL: Supervised Contrastive Ordinal Loss for Abdominal Aortic Calcification Scoring on Vertebral Fracture Assessment Scans
Abdominal Aortic Calcification (AAC) is a known marker of asymptomatic
Atherosclerotic Cardiovascular Diseases (ASCVDs). AAC can be observed on
Vertebral Fracture Assessment (VFA) scans acquired using Dual-Energy X-ray
Absorptiometry (DXA) machines. Thus, the automatic quantification of AAC on VFA
DXA scans may be used to screen for CVD risks, allowing early interventions. In
this research, we formulate the quantification of AAC as an ordinal regression
problem. We propose a novel Supervised Contrastive Ordinal Loss (SCOL) by
incorporating a label-dependent distance metric with existing supervised
contrastive loss to leverage the ordinal information inherent in discrete AAC
regression labels. We develop a Dual-encoder Contrastive Ordinal Learning
(DCOL) framework that learns the contrastive ordinal representation at global
and local levels to improve the feature separability and class diversity in
latent space among the AAC-24 genera. We evaluate the performance of the
proposed framework using two clinical VFA DXA scan datasets and compare our
work with state-of-the-art methods. Furthermore, for predicted AAC scores, we
provide a clinical analysis to predict the future risk of a Major Acute
Cardiovascular Event (MACE). Our results demonstrate that this learning
enhances inter-class separability and strengthens intra-class consistency,
which results in predicting the high-risk AAC classes with high sensitivity and
high accuracy.Comment: Accepted in conference MICCAI 202