Integrative personalized medicine care for adjustment disorder of a post-COVID-19 patient: A CARE-compliant case report.

Rationale: Depression is a common symptom in post-coronavirus disease 2019 (COVID-19) patients, which can be diagnosed with post-COVID-19 depression or adjustment disorder (AD) of post-COVID-19 syndrome. Recently, there have been reports of treating post-COVID-19 syndrome with herbal interventions. However, there are no studies of AD of post-COVID-19 syndrome treated with an integrative approach.

A deep learning framework for supporting the classification of breast lesions in ultrasound images.

In this research, we exploited the deep learning framework to differentiate the distinctive types of lesions and nodules in breast acquired with ultrasound imaging. A biopsy-proven benchmarking dataset was built from 5151 patients cases containing a total of 7408 ultrasound breast images, representative of semi-automatically segmented lesions associated with masses. The dataset comprised 4254 benign and 3154 malignant lesions.

Comparison Between CT and MR Images as More Favorable Reference Data Sets for Fusion Imaging-Guided Radiofrequency Ablation or Biopsy of Hepatic Lesions: A Prospective Study with Focus on Patient's Respiration.

Purpose: To identify the more accurate reference data sets for fusion imaging-guided radiofrequency ablation or biopsy of hepatic lesions between computed tomography (CT) and magnetic resonance (MR) images.

Materials And Methods: This study was approved by the institutional review board, and written informed consent was received from all patients. Twelve consecutive patients who were referred to assess the feasibility of radiofrequency ablation or biopsy were enrolled.

Automatic image fusion of real-time ultrasound with computed tomography images: a prospective comparison between two auto-registration methods.

Background A major drawback of conventional manual image fusion is that the process may be complex, especially for less-experienced operators. Recently, two automatic image fusion techniques called Positioning and Sweeping auto-registration have been developed. Purpose To compare the accuracy and required time for image fusion of real-time ultrasonography (US) and computed tomography (CT) images between Positioning and Sweeping auto-registration.

A prospective comparison between auto-registration and manual registration of real-time ultrasound with MR images for percutaneous ablation or biopsy of hepatic lesions.

Purpose: To compare the accuracy and required time for image fusion of real-time ultrasound (US) with pre-procedural magnetic resonance (MR) images between positioning auto-registration and manual registration for percutaneous radiofrequency ablation or biopsy of hepatic lesions.

Methods: This prospective study was approved by the institutional review board, and all patients gave written informed consent. Twenty-two patients (male/female, n = 18/n = 4; age, 61.

Automatic Registration between Real-Time Ultrasonography and Pre-Procedural Magnetic Resonance Images: A Prospective Comparison between Two Registration Methods by Liver Surface and Vessel and by Liver Surface Only.

The aim of this study was to compare the accuracy of and the time required for image fusion between real-time ultrasonography (US) and pre-procedural magnetic resonance (MR) images using automatic registration by a liver surface only method and automatic registration by a liver surface and vessel method. This study consisted of 20 patients referred for planning US to assess the feasibility of percutaneous radiofrequency ablation or biopsy for focal hepatic lesions. The first 10 consecutive patients were evaluated by an experienced radiologist using the automatic registration by liver surface and vessel method, whereas the remaining 10 patients were evaluated using the automatic registration by liver surface only method.

Multi-object analysis of volume, pose, and shape using statistical discrimination.

One goal of statistical shape analysis is the discrimination between two populations of objects. Whereas traditional shape analysis was mostly concerned with single objects, analysis of multi-object complexes presents new challenges related to alignment and pose. In this paper, we present a methodology for discriminant analysis of multiple objects represented by sampled medial manifolds.

Multi-figure anatomical objects for shape statistics.

Multi-figure m-reps allow us to represent and analyze a complex anatomical object by its parts, by relations among its parts, and by the object itself as a whole entity. This representation also enables us to gather either global or hierarchical statistics from a population of such objects. We propose a framework to train the statistics of multi-figure anatomical objects from real patient data.