Algorithm Variability in the Estimation of Lung Nodule Volume From Phantom CT Scans: Results of the QIBA 3A Public Challenge.

Rationale And Objectives: Quantifying changes in lung tumor volume is important for diagnosis, therapy planning, and evaluation of response to therapy. The aim of this study was to assess the performance of multiple algorithms on a reference data set. The study was organized by the Quantitative Imaging Biomarker Alliance (QIBA).

Inter-Method Performance Study of Tumor Volumetry Assessment on Computed Tomography Test-Retest Data.

Rationale And Objectives: Tumor volume change has potential as a biomarker for diagnosis, therapy planning, and treatment response. Precision was evaluated and compared among semiautomated lung tumor volume measurement algorithms from clinical thoracic computed tomography data sets. The results inform approaches and testing requirements for establishing conformance with the Quantitative Imaging Biomarker Alliance (QIBA) Computed Tomography Volumetry Profile.

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.

Precise segmentation of multiple organs in CT volumes using learning-based approach and information theory.

In this paper, we present a novel method by incorporating information theory into the learning-based approach for automatic and accurate pelvic organ segmentation (including the prostate, bladder and rectum). We target 3D CT volumes that are generated using different scanning protocols (e.g.

Automatic contrast phase estimation in CT volumes.

We propose an automatic algorithm for phase labeling that relies on the intensity changes in anatomical regions due to the contrast agent propagation. The regions (specified by aorta, vena cava, liver, and kidneys) are first detected by a robust learning-based discriminative algorithm. The intensities inside each region are then used in multi-class LogitBoost classifiers to independently estimate the contrast phase.

Venous tree separation in the liver: graph partitioning using a non-ising model.

Entangled tree-like vascular systems are commonly found in the body (e.g., in the peripheries and lungs).

GPU-based smart visibility techniques for tumor surgery planning.

Purpose: The rating of distances and infiltrations to vital structures is important for the planning of tumor surgery or interventional procedures. To support such an assessment, the target structures should be clearly emphasized in a 3D visualization by ensuring their visibility.

Methods: Smart Visibility techniques such as Ghosting Views and Breakaway Views are employed.

The medical exploration toolkit: an efficient support for visual computing in surgical planning and training.

Application development is often guided by the usage of software libraries and toolkits. For medical applications, the toolkits currently available focus on image analysis and volume rendering. Advance interactive visualizations and user interface issues are not adequately supported.