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. Two radiologists independently reviewed the external dataset consisting of 2178 abdomen anteroposterior (AP) topograms and 515 spine AP and lateral topograms, all collected in a consecutive manner. Sensitivity and specificity were calculated per pixel row and per patient. Pairwise intersection over union (IoU) was also calculated between the DLA and the two radiologists. RESULTS: The performance parameters of the DLA were consistently >95% in internal validation per pixel row and per patient. DLA can save 27.4% of reconstruction time on average in patients with metallic implants compared to the existing iMAR. The sensitivity and specificity of the DLA during external validation were greater than 90% for the detection of spine implants on three different topograms and for the detection of hip implants on abdominal AP and spinal AP topograms. The IoU was greater than 0.9 between the DLA and the radiologists. However, the DLA training could not be performed for hip implants on spine lateral topograms. CONCLUSIONS: A prototype DLA to detect metallic implants of the spine and hip on abdominal and spinal CT topograms improves the scan workflow with good performance for both spine and hip implants.

Modeling and visualization techniques for virtual stenting of aneurysms and stenoses.

In this work, we present modeling and visualization techniques for virtual stenting of aneurysms and stenoses. In particular, contributions to support the computer-aided treatment of artery diseases - artery enlargement (aneurysm) and artery contraction (stenosis) - are made. If an intervention takes place, there are two different treatment alternatives for this kind of artery diseases: open surgery and minimally invasive (endovascular) treatment. Computer-assisted optimization of endovascular treatments is the main focus of our work. In addition to stent simulation techniques, we also present a computer-aided simulation of endoluminal catheters to support the therapy-planning phase. The stent simulation is based on a three-dimensional Active Contour Method and is applicable to both non-bifurcated (I-stents) and bifurcated stents (Y-stents). All methods are introduced in detail and are evaluated with phantom datasets as well as with real patient data from the clinical routine. Additionally, the clinical prototype that is based upon these methods is described.