The Predictive Power of Near-Infrared Spectroscopy in Improving Cognitive Problems in Patients Undergoing Brain Surgeries: A Systematic Review.

One of the main objectives in neurosurgical procedures is the prevention of cerebral ischemia and hypoxia leading to secondary brain injury. Different methods for early detection of intraoperative cerebral ischemia and hypoxia have been used. Near-infrared spectroscopy (NIRS) is a simple, non-invasive method for monitoring cerebral oxygenation increasingly used today. The aim of this study was to systematically review the brain monitoring with NIRS in neurosurgery. The search process resulted in the detection of 324 articles using valid keywords on the electronic databases, including Embase, PubMed, Scopus, Web of Science, and Cochrane Library. Subsequently, the full texts of 34 studies were reviewed, and finally 11 articles (seven prospective studies, three retrospective studies, and one randomized controlled trial) published from 2005 to 2020 were identified as eligible for systematic review. Meta-analysis was not possible due to high heterogeneity in neurological and neurosurgical conditions of patients, expression of different clinical outcomes, and different standard reference tests in the studies reviewed. The results showed that NIRS is a non-invasive cerebral oximetry that provides continuous and measurable cerebral oxygenation information and can be used in a variety of clinical settings.

Prediction of myocardial infarction by assessing regional cardiac wall in CMR images through active mesh modeling.

Myocardial infarction is a leading cause of morbidity and mortality. In this study, using Cine MRI images, the infarct region was precisely determined by examining the local migration path length of critical points on myocardium borders and the fractional thickening effects. First, MRI Cine images of Epi/Endocardium were processed in 3D for all slices, and then incorporated in all frames to build a dynamic model. Epi/Endocardium images were segmented using Heiberg algorithm, and then by a robust restricted block matching algorithm, the sparse points were tracked. Finally, by fitting a 3D active mesh model to the sparse point displacements, a dense motion field was obtained, and some useful local parameters of left ventricle in patients with myocardial infarction were estimated. The local parameters are path length, fractional thickening, and strain. Using this process, the cardiac wall motion was quantized to determine the region and extent of infarct lesion. The process was implemented, and the results were examined and modified against the cardiac perfusion scan. Data were acquired from 10 healthy individuals and 20 patients with the myocardial infarction. The findings also reveal that the infarct region can be determined by locating less than 20% in the wall thickening. In all the patients, the process was able to precisely determine the affected region. The cardiac wall kinesis in damaged regions was properly evaluated by normalized path length and presented in standard bull's-eye format. The above approach is promising and can be extended in prognosis of acute heart infraction by prediction of prone to the wall kinesis regions in the patients close to MI by examining the local indexes of the myocardium in the cardiac MRI images.

Barrett's Mucosa Segmentation in Endoscopic Images Using a Hybrid Method: Spatial Fuzzy c-mean and Level Set.

Barrett's mucosa is one of the most important diseases in upper gastrointestinal system that caused by gastro-esophagus reflux. If left untreated, the disease will cause distal esophagus and gastric cardia adenocarcinoma. The malignancy risk is very high in short segment Barrett's mucosa. Therefore, lesion area segmentation can improve specialist decision for treatment. In this paper, we proposed a combined fuzzy method with active models for Barrett's mucosa segmentation. In this study, we applied three methods for special area segmentation and determination. For whole disease area segmentation, we applied the hybrid fuzzy based level set method (LSM). Morphological algorithms were used for gastroesophageal junction determination, and we discriminated Barrett's mucosa from break by applying Chan-Vase method. Fuzzy c-mean and LSMs fail to segment this type of medical image due to weak boundaries. In contrast, the full automatic hybrid method with correlation approach that has used in this paper segmented the metaplasia area in the endoscopy image with desirable accuracy. The presented approach omits the manually desired cluster selection step that needed the operator manipulation. Obtained results convinced us that this approach is suitable for esophagus metaplasia segmentation.

Application of Hyperelastic-based Active Mesh Model in Cardiac Motion Recovery.

Considering the nonlinear hyperelastic or viscoelastic nature of soft tissues has an important effect on modeling results. In medical applications, accounting nonlinearity begets an ill posed problem, due to absence of external force. Myocardium can be considered as a hyperelastic material, and variational approaches are proposed to estimate stiffness matrix, which take into account the linear and nonlinear properties of myocardium. By displacement estimation of some points in the four-dimensional cardiac magnetic resonance imaging series, using a similarity criterion, the elementary deformations are estimated, then using the Moore-Penrose inverse matrix approach, all point deformations are obtained. Using this process, the cardiac wall motion is quantized to mechanically determine local parameters to investigate the cardiac wall functionality. This process was implemented and tested over 10 healthy and 20 patients with myocardial infarction. In all patients, the process was able to precisely determine the affected region. The proposed approach was also compared with linear one and the results demonstrated its superiority respect to the linear model.

Ensemble Supervised Classification Method Using the Regions of Interest and Grey Level Co-Occurrence Matrices Features for Mammograms Data.

BACKGROUND: Breast cancer is one of the most encountered cancers in women. Detection and classification of the cancer into malignant or benign is one of the challenging fields of the pathology. OBJECTIVES: Our aim was to classify the mammogram data into normal and abnormal by ensemble classification method. PATIENTS AND METHODS: In this method, we first extract texture features from cancerous and normal breasts, using the Gray-Level Co-occurrence Matrices (GLCM) method. To obtain better results, we select a region of breast with high probability of cancer occurrence before feature extraction. After features extraction, we use the maximum difference method to select the features that have predominant difference between normal and abnormal data sets. Six selected features served as the classifying tool for classification purpose by the proposed ensemble supervised algorithm. For classification, the data were first classified by three supervised classifiers, and then by simple voting policy, we finalized the classification process. RESULTS: After classification with the ensemble supervised algorithm, the performance of the proposed method was evaluated by perfect test method, which gave the sensitivity and specificity of 96.66% and 97.50%, respectively. CONCLUSIONS: In this study, we proposed a new computer aided diagnostic tool for the detection and classification of breast cancer. The obtained results showed that the proposed method is more reliable in diagnostic to assist the radiologists in the detection of abnormal data and to improve the diagnostic accuracy.