Development and Validation of a Deep-Learning-Based Algorithm for Detecting and Classifying Metallic Implants in Abdominal and Spinal CT Topograms.

Purpose: To develop and validate a deep-learning-based algorithm (DLA) that is designed to segment and classify metallic objects in topograms of abdominal and spinal CT.

Methods: DLA training for implant segmentation and classification was based on a U-net-like architecture with 263 annotated hip implant topograms and 2127 annotated spine implant topograms. The trained DLA was validated with internal and external datasets.

Artificial Intelligence in Diagnostic Imaging: Status Quo, Challenges, and Future Opportunities.

In this review article, the current and future impact of artificial intelligence (AI) technologies on diagnostic imaging is discussed, with a focus on cardio-thoracic applications. The processing of imaging data is described at 4 levels of increasing complexity and wider implications. At the examination level, AI aims at improving, simplifying, and standardizing image acquisition and processing.

ADR--Anatomy-Driven Reformation.

Dedicated visualization methods are among the most important tools of modern computer-aided medical applications. Reformation methods such as Multiplanar Reformation or Curved Planar Reformation have evolved as useful tools that facilitate diagnostic and therapeutic work. In this paper, we present a novel approach that can be seen as a generalization of Multiplanar Reformation to curved surfaces.

A retrieval-based computer-aided diagnosis system for the characterization of liver lesions in CT scans.

Rationale And Objectives: To evaluate a computer-aided diagnosis (CADx) system for the characterization of liver lesions in computed tomography (CT) scans. The stand-alone predictive performance of the CADx system was assessed and compared to that of three radiologists who were provided with the same amount of image information to which the CADx system had access.

Materials And Methods: The CADx system operates as an image search engine exploiting texture analysis of liver lesion image data for the lesion in question and lesions from a database.

Fast and robust 3D vertebra segmentation using statistical shape models.

We propose a top-down fully automatic 3D vertebra segmentation algorithm using global shape-related as well as local appearance-related prior information. The former is brought into the system by a global statistical shape model built from annotated training data, i.e.