CardSegNet: An adaptive hybrid CNN-vision transformer model for heart region segmentation in cardiac MRI.

Cardiovascular MRI (CMRI) is a non-invasive imaging technique adopted for assessing the blood circulatory system's structure and function. Precise image segmentation is required to measure cardiac parameters and diagnose abnormalities through CMRI data. Because of anatomical heterogeneity and image variations, cardiac image segmentation is a challenging task.

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.

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.

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.

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.

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.