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). MATERIALS AND METHODS: The study was organized as a public challenge. Computed tomography scans of synthetic lung tumors in an anthropomorphic phantom were acquired by the Food and Drug Administration. Tumors varied in size, shape, and radiodensity. Participants applied their own semi-automated volume estimation algorithms that either did not allow or allowed post-segmentation correction (type 1 or 2, respectively). Statistical analysis of accuracy (percent bias) and precision (repeatability and reproducibility) was conducted across algorithms, as well as across nodule characteristics, slice thickness, and algorithm type. RESULTS: Eighty-four percent of volume measurements of QIBA-compliant tumors were within 15% of the true volume, ranging from 66% to 93% across algorithms, compared to 61% of volume measurements for all tumors (ranging from 37% to 84%). Algorithm type did not affect bias substantially; however, it was an important factor in measurement precision. Algorithm precision was notably better as tumor size increased, worse for irregularly shaped tumors, and on the average better for type 1 algorithms. Over all nodules meeting the QIBA Profile, precision, as measured by the repeatability coefficient, was 9.0% compared to 18.4% overall. CONCLUSION: The results achieved in this study, using a heterogeneous set of measurement algorithms, support QIBA quantitative performance claims in terms of volume measurement repeatability for nodules meeting the QIBA Profile criteria.

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. MATERIALS AND METHODS: Industry and academic groups participated in a challenge study. Intra-algorithm repeatability and inter-algorithm reproducibility were estimated. Relative magnitudes of various sources of variability were estimated using a linear mixed effects model. Segmentation boundaries were compared to provide a basis on which to optimize algorithm performance for developers. RESULTS: Intra-algorithm repeatability ranged from 13% (best performing) to 100% (least performing), with most algorithms demonstrating improved repeatability as the tumor size increased. Inter-algorithm reproducibility was determined in three partitions and was found to be 58% for the four best performing groups, 70% for the set of groups meeting repeatability requirements, and 84% when all groups but the least performer were included. The best performing partition performed markedly better on tumors with equivalent diameters greater than 40 mm. Larger tumors benefitted by human editing but smaller tumors did not. One-fifth to one-half of the total variability came from sources independent of the algorithms. Segmentation boundaries differed substantially, not ony in overall volume but also in detail. CONCLUSIONS: Nine of the 12 participating algorithms pass precision requirements similar to what is indicated in the QIBA Profile, with the caveat that the present study was not designed to explicitly evaluate algorithm profile conformance. Change in tumor volume can be measured with confidence to within ±14% using any of these nine algorithms on tumor sizes greater than 10 mm. No partition of the algorithms was able to meet the QIBA requirements for interchangeability down to 10 mm, although the partition comprising best performing algorithms did meet this requirement for a tumor size of greater than approximately 40 mm.

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. The main concept is to generate reformatted medical volumes driven by the individual anatomical geometry of a specific patient. This process generates flat views of anatomical structures that facilitate many tasks such as diagnosis, navigation and annotation. Our reformation framework is based on a non-linear as-rigid-as-possible volumetric deformation scheme that uses generic triangular surface meshes as input. To manage inevitable distortions during reformation, we introduce importance maps which allow controlling the error distribution and improving the overall visual quality in areas of elevated interest. Our method seamlessly integrates with well-established concepts such as the slice-based inspection of medical datasets and we believe it can improve the overall efficiency of many medical workflows. To demonstrate this, we additionally present an integrated visualization system and discuss several use cases that substantiate its benefits.

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., contrast and non-contrast, with and without implant in the prostate, various resolution and position), and the volumes come from largely diverse sources (e.g., diseased in different organs). Three key ingredients are combined to solve this challenging segmentation problem. First, marginal space learning (MSL) is applied to efficiently and effectively localize the multiple organs in the largely diverse CT volumes. Second, learning techniques, steerable features, are applied for robust boundary detection. This enables handling of highly heterogeneous texture pattern. Third, a novel information theoretic scheme is incorporated into the boundary inference process. The incorporation of the Jensen-Shannon divergence further drives the mesh to the best fit of the image, thus improves the segmentation performance. The proposed approach is tested on a challenging dataset containing 188 volumes from diverse sources. Our approach not only produces excellent segmentation accuracy, but also runs about eighty times faster than previous state-of-the-art solutions. The proposed method can be applied to CT images to provide visual guidance to physicians during the computer-aided diagnosis, treatment planning and image-guided radiotherapy to treat cancers in pelvic region.

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. Each classifier forms a node in a decision tree which is used to obtain the final phase label. Combining independent classification from multiple regions in a tree has the advantage when one of the region detectors fail or when the phase training example database is imbalanced. We show on a dataset of 1016 volumes that the system correctly classifies native phase in 96.2% of the cases, hepatic dominant phase (92.2%), hepatic venous phase (96.7%), and equilibrium phase (86.4%) in 7 seconds on average.

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). Separation of these systems in medical images may be formulated as a graph partitioning problem given an imperfect segmentation and specification of the tree roots. In this work, we show that the ubiquitous Ising-model approaches (e.g., Graph Cuts, Random Walker) are not appropriate for tackling this problem and propose a novel method based on recursive minimal paths for doing so. To motivate our method, we focus on the intertwined portal and hepatic venous systems in the liver. Separation of these systems is critical for liver intervention planning, in particular when resection is involved. We apply our method to 34 clinical datasets, each containing well over a hundred vessel branches, demonstrating its effectiveness.

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. Ghosting Views highlight focus structures by fading out occluding structures and are often used in anatomical illustrations. Breakaway Views reveal the structure by cutting into surrounding structures. As a result, an intersection surface is created that allows relating the focus structure with its surroundings. In this contribution, a specialized GPU-based implementation of these techniques is presented for polygonal models derived from a segmentation of the anatomical structures. RESULTS: We present different rendering styles of the techniques and apply them to highlight enlarged lymph nodes in the neck, as well as tumors inside the liver. Compared to other methods, we focus on polygonal models and optimizations. Thus, very high frame rates could be achieved on consumer graphics hardware. Furthermore, we employed markers that support the estimation of distances within the scene and possible infiltrations around the focus structures. CONCLUSION: The parameters for the techniques are defined automatically to aid the employment in clinical routine. Such an application is also supported by the combination and refinement of established rendering techniques.

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. Hence, we present a toolkit for application development in the field of medical intervention planning, training, and presentation--the MEDICALEXPLORATIONTOOLKIT (METK). The METK is based on the rapid prototyping platform MeVisLab and offers a large variety of facilities for an easy and efficient application development process. We present dedicated techniques for advanced medical visualizations, exploration, standardized documentation, adn interface widgets for common tasks. These include, e.g., advanced animation facilities, viewpoint selection, several illustrative rendering techniques, and new techniques for object selection in 3D surface models. No extended programming skills are needed for application building, since a graphical programming approach can be used. the toolkit is freely available and well documented to facilitate the use and extension of the toolkit.