An Automatic Framework for Nasal Esthetic Assessment by ResNet Convolutional Neural Network.

Nasal base aesthetics is an interesting and challenging issue that attracts the attention of researchers in recent years. With that insight, in this study, we propose a novel automatic framework (AF) for evaluating the nasal base which can be useful to improve the symmetry in rhinoplasty and reconstruction. The introduced AF includes a hybrid model for nasal base landmarks recognition and a combined model for predicting nasal base symmetry.

A Knowledge-Based Modality-Independent Technique for Concurrent Thigh Muscle Segmentation: Applicable to CT and MR Images.

The mass of the lower extremity muscles is a clinically significant metric. Manual segmentation of these muscles is a time-consuming task. Most of the segmentation methods for the thigh muscles are based on statistical models and atlases which need manually segmented datasets.

Preoperative Computer Simulation in Rhinoplasty Using Previous Postoperative Images.

Providing realistic and useful preoperative counseling based on a surgeon's knowledge and experience. Using previous preoperative and postoperative patients' images to predict the postoperative result of a new query patient. After preprocessing for image standardization, facial landmarking was done using 68 points on the frontal view and 19 points on the profile view.

Fully automatic estimation of pelvic sagittal inclination from anterior-posterior radiography image using deep learning framework.

Background And Objective: Malposition of the acetabular component causes dislocation and prosthetic impingement after Total Hip Arthroplasty (THA), which significantly affects the postoperative quality of life and implant longevity. The position of the acetabular component is determined by the Pelvic Sagittal Inclination (PSI), which not only varies among different people but also changes in different positions. It is important to recognize individual dynamic changes of the PSI for patient-specific planning of the THA.

A novel image-based retrieval system for characterization of maxillofacial lesions in cone beam CT images.

Purpose: The objective of medical content-based image retrieval (CBIR) is to assist clinicians in decision making by retrieving the most similar cases to a given query image from a large database. Herein, a new method for content-based image retrieval of cone beam CT (CBCT) scans is presented.

Methods: The introduced framework consists of two main phases: training database construction and querying.