MUSE--a new tool for interactive image analysis and segmentation based on multivariate statistics

Translational Radiomics: Defining the Strategy Pipeline and Considerations for Application-Part 2: From Clinical Implementation to Enterprise

Enterprise imaging has channeled various technological innovations to the field of clinical radiology, ranging from advanced imaging equipment and postacquisition iterative reconstruction tools to image analysis and computer-aided detection tools. More recently, the advancement in the field of quantitative image analysis coupled with machine learning-based data analytics, classification, and integration has ushered in the era of radiomics, a paradigm shift that holds tremendous potential in clinical decision support as well as drug discovery. However, there are important issues to consider to incorporate radiomics into a clinically applicable system and a commercially viable solution. In this two-part series, we offer insights into the development of the translational pipeline for radiomics from methodology to clinical implementation (Part 1) and from that point to enterprise development (Part 2). In Part 2 of this two-part series, we study the components of the strategy pipeline, from clinical implementation to building enterprise solutions.

A rule based method for context sensitive threshold segmentation in SPECT using simulation

Robust techniques for automatic or semi-automatic segmentation of objects in single photon emission computed tomography (SPECT) are still the subject of development. This paper describes a threshold based method which uses empirical rules derived from analysis of computer simulated images of a large number of objects. The use of simulation allowed the factors affecting the threshold which correctly segmented objects to be investigated systematically. Rules could then be derived from these data to define the threshold in any particular context. The technique operated iteratively and calculated local context sensitive thresholds along radial profiles from the centre of gravity of the object. It was evaluated in a further series of simulated objects and in human studies, and compared to the use of a global fixed threshold. The method was capable of improving accuracy of segmentation and volume assessment compared to the global threshold technique. The improvements were greater for small volumes, shapes with large surface area to volume ratio, variable surrounding activity and non-uniform distributions. The method was applied successfully to simulated objects and human studies and is considered to be a significant advance on global fixed threshold techniques.

Principal component analysis of dynamic positron emission tomography images

Multivariate image analysis can be used to analyse multivariate medical images. The purpose could be to visualize or classify structures in the image. One common multivariate image analysis technique which can be used for visualization purposes is principal component analysis (PCA). The present work concerns visualization of organs and structures with different kinetics in a dynamic sequence utilizing PCA. When applying PCA on positron emission tomography (PET) images, the result is initially not satisfactory. It is illustrated that one major explanation for the behaviour of PCA when applied to PET images is that it is a data-driven technique which cannot separate signals from high noise levels. With a better understanding of the PCA, gained with a strategy of examining the image data set, the transformations, and the results using visualization tools, a surprisingly easily understood methodology can be derived. The proposed methodology can enhance clinically interesting information in a dynamic PET imaging sequence in the first few principal component images and thus should be able to aid in the identification of structures for further analysis.