Combining Radiomics and Autoencoders to Distinguish Benign and Malignant Breast Tumors on US Images.

Background US is clinically established for breast imaging, but its diagnostic performance depends on operator experience. Computer-assisted (real-time) image analysis may help in overcoming this limitation. Purpose To develop precise real-time-capable US-based breast tumor categorization by combining classic radiomics and autoencoder-based features from automatically localized lesions.

Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification.

Automation of medical data analysis is an important topic in modern cancer diagnostics, aiming at robust and reproducible workflows. Therefore, we used a dataset of breast US images (252 malignant and 253 benign cases) to realize and compare different strategies for CAD support in lesion detection and classification. Eight different datasets (including pre-processed and spatially augmented images) were prepared, and machine learning algorithms (i.

Microstructural volumetric analysis of the jaw following dental implantation under systemic bisphosphonate delivery: An in vivo and ex vivo rat study.

Background: Because of bisphosphonate medication, dental implantation with a subsequent infection poses a relevant risk factor to suffer from medication-related osteonecrosis of the jaw. This rat study evaluated different implant materials under systemic bisphosphonate delivery using micro-computed tomography (μCT) images.

Methods: Fifty-four rats were randomly allocated into a control group 1, test group 2 with intravenous drug application of zoledronic acid and test group 3 with a subcutaneous application of alendronic acid.

A preclinical micro-computed tomography database including 3D whole body organ segmentations.

The gold-standard of preclinical micro-computed tomography (μCT) data processing is still manual delineation of complete organs or regions by specialists. However, this method is time-consuming, error-prone, has limited reproducibility, and therefore is not suitable for large-scale data analysis. Unfortunately, robust and accurate automated whole body segmentation algorithms are still missing.