Browsing by Subject "Feature extraction"

Purpose: To develop a novel voxel-based radiomics extraction technique, and to investigate the potential association between spatially-encoded radiomics features of the lungs and pulmonary function.

Methods: We developed a voxel-based radiomics feature extraction platform to generate radiomics filtered images. Specifically, for each voxel in the image, 62 radiomics features were calculated in a rotationally-invariant 3D neighbourhood to capture spatially-encoded information. In general, such an approach results in an image tensor object, i.e., each voxel in the original image is represented by a 62-dimensional radiomics feature vector. Two digital phantoms are then designed to validate the technique's ability to quantify regional image information. To test the technique as a potential pulmonary biomarker, we generated radiomics filtered images for 25 lung CT image and are subsequently evaluated against corresponding Galligas PET images, as the ground truth for pulmonary function, using voxel-wise Spearman correlation (r). The Canonical Correlation Analysis (CCA)-based feature fusion method is also implemented to enhance such a correlation. Finally, the Spearman distributions were compared with 37 individual CT ventilation image (CTVI) algorithms to assess the overall performance relative to conventional CT-based techniques.

Results: Several radiomics filtered images were identified to be correlated with Galligas PET lung imaging. The most robust association was found to be the Run Length Encoding feature, Run-Length Non-uniformity (0.21

Conclusions: This preliminary study indicates that spatially-encoded lung texture and lung density are potentially associated with pulmonary function as measured via Galligas PET ventilation images. Collectively, low density, heterogeneous coarse lung texture was often associated with lower Galligas radiotracer amounts.

Purpose: To characterize the effect of different discretization techniques (methods and values) on radiomic features extracted from positron emission tomography (PET) images of patients undergoing definitive radiotherapy for oropharyngeal cancer (OPC) and determine if there are optimal binning techniques associated with the computed texture and histogram measurements.Methods: 71 patients were enrolled in a prospective clinical trial to receive definitive radiotherapy (70Gy) for OPC. PET/CT images were acquired both prior to treatment and two weeks into treatment (i.e., after 20 Gy). All patients were scanned on the same PET/CT imaging system. The gross tumor volume at the primary tumor site was manually segmented on CT and transferred to PET, from which 74 quantitative radiomic features were extracted as potential imaging biomarkers. The sensitivity of feature extraction to common discretization techniques (fixed bin number vs. fixed bin size) was systematically evaluated by measuring radiomic feature values at monotonically increasing bin numbers (32, 64, 128, 256) and bin sizes (0.1, 0.5, 1.0, 5.0). Disparities in radiomics data parameterized by these different discretization settings were quantified based on t-tests of individual features and cross-correlation of matrix-level feature spaces. A discretization invariance score (DIS) was defined as an aggregation of each unique probability of rejecting the null hypothesis that any two discretization techniques produce the same feature value. To evaluate the generalization of these characteristics during treatment, DIS values were compared between pre- and intra-treatment imaging. Results: Only 50% of radiomic features were robust (DIS > 0.7) to changes in bin number, compared to 66% of features when varying bin size. Regardless of discretization technique, grey level variance (DIS=0.0) and high grey level size emphasis (DIS=0.21) were the most sensitive to binning perturbations, while skewness (DIS=1.0) and kurtosis (DIS=1.0) were nearly invariant. The cross-correlation between discretization-specific feature spaces was maximized for fixed bin number and minimized for fixed bin size. Ranked DIS measurements were comparable between pre-treatment and intra-treatment imaging, implying that feature sensitivity is invariant to changes in the absolute feature value over time. Conclusion: The impact of discretization is largely feature-dependent. Individual features demonstrated a non-linear response to systematic changes in discretization parameters, which was captured by our DIS metric. DIS values can be used to optimize downstream radiomic biomarkers, where the prognostic value of individual features may depend on feature-specific discretization.

The notion of distance plays and important role in many reinforcement learning (RL) techniques. This role may be explicit, as in some non-parametric approaches, or it may be implicit in the architecture of the feature space. The ability to learn distance functions tailored for RL tasks could, thus, benefit many different RL paradigms. While several approaches to learning distance functions from data do exist, they are frequently intended for use in clustering or classification tasks and typically do not take into account the inherent structure present in trajectories sampled from RL environments. For those that do, this structure is generally used to define a similarity between states rather than to represent the mechanics of the domain. Based on the idea that a good distance function in such a domain would reflect the number of transitions necessary to get to from one state to another, we detail an approach to learning distance functions which accounts for the nature of state transitions in a Markov decision process, including their inherent directionality. We then present the results of experiments performed in multiple RL environments in order to demonstrate the benefit of learning such distance functions.