Automatic Segmentation of Lumen Intima Layer in Longitudinal Mode Ultrasound Images.

We propose an automated method for the segmentation of lumen intima layer of the common carotid artery in longitudinal mode ultrasound images. The method is hybrid, in the sense that a coarse segmentation is first achieved by optimizing a locally defined contrast function of an active oblong considering its five degrees-of-freedom, and subsequently the fine segmentation and delineation of the carotid artery are achieved by post-processing the portion of the ultrasound image spanned by the annulus region of the optimally fitted active oblong. The post-processing includes median filtering and Canny edge detection to retain the lumen intima representative points followed by a smooth curve fitting technique to delineate the lumen intima boundary.

Deep learning, computer-aided radiography reading for tuberculosis: a diagnostic accuracy study from a tertiary hospital in India.

In general, chest radiographs (CXR) have high sensitivity and moderate specificity for active pulmonary tuberculosis (PTB) screening when interpreted by human readers. However, they are challenging to scale due to hardware costs and the dearth of professionals available to interpret CXR in low-resource, high PTB burden settings. Recently, several computer-aided detection (CAD) programs have been developed to facilitate automated CXR interpretation.

Diagnostic reliability of the Thyroid Imaging Reporting and Data System (TI-RADS) in routine practice.

Purpose: To evaluate the diagnostic reliability of Thyroid Imaging Reporting and Data System (TI-RADS) classifications described by American College of Radiology (ACR) and Kwak . by calculating the risk of malignancy, to assess the role of TI-RADS in reducing fine-needle aspiration cytology (FNAC) of benign lesions.

Material And Methods: This was a prospective study during the period from December 2017 to August 2018.