A Framework for the Objective Assessment of Registration Accuracy

Validation and accuracy assessment are themain bottlenecks preventing the adoption of image processing algorithms in the clinical practice. In the classical approach, a posteriori analysis is performed through objective metrics. In this work, a different approach based on Petri nets is proposed.The basic idea consists in predicting the accuracy of a given pipeline based on the identification and characterization of the sources of inaccuracy. The concept is demonstrated on a case study: the intrasubject rigid and affine registration of magnetic resonance images. A choice of possible sources of inaccuracies that can affect the registration process is accounted for, and an estimation of the overall inaccuracy is provided through Petri nets. Both synthetic and real data are considered. While synthetic data allow the benchmarking of the performance with respect to the ground truth, real data enable to assess the robustness of the methodology in real contexts as well as to determine the suitability of the use of synthetic data in the training phase. Results revealed a higher correlation and a lower dispersion among the metrics for simulated data, while the opposite trend was observed for pathologic ones. Results show that the proposedmodel not only provides a good prediction performance but also leads to the optimization of the end-to-end chain in terms of accuracy and robustness, setting the ground for its generalization to different and more complex scenarios

DCE-MRI and DWI Integration for Breast Lesions Assessment and Heterogeneity Quantification

In order to better predict and follow treatment responses in cancer patients, there is growing interest in noninvasively characterizing tumor heterogeneity based on MR images possessing different contrast and quantitative information. This requires mechanisms for integrating such data and reducing the data dimensionality to levels amenable to interpretation by human readers. Here we propose a two-step pipeline for integrating diffusion and perfusion MRI that we demonstrate in the quantification of breast lesion heterogeneity. First, the images acquired with the two modalities are aligned using an intermodal registration. Dissimilarity-based clustering is then performed exploiting the information coming from both modalities. To this end an ad hoc distance metric is developed and tested for tuning the weighting for the two modalities. The distributions of the diffusion parameter values in subregions identified by the algorithm are extracted and compared through nonparametric testing for posterior evaluation of the tissue heterogeneity. Results show that the joint exploitation of the information brought by DCE and DWI leads to consistent results accounting for both perfusion and microstructural information yielding a greater refinement of the segmentation than the separate processing of the two modalities, consistent with that drawn manually by a radiologist with access to the same data

Low-frequency oscillations employ a general coding of the spatio-temporal similarity of dynamic faces

Brain networks use neural oscillations as information transfer mechanisms. Although the face perception network in occipitotemporal cortex is well-studied, contributions of oscillations to face representation remain an open question. We tested for links between oscillatory responses that encode facial dimensions and the theoretical proposal that faces are encoded in similarity-based “face spaces”. We quantified similarity-based encoding of dynamic faces in magnetoencephalographic sensor-level oscillatory power for identity, expression, physical and perceptual similarity of facial form and motion. Our data show that evoked responses manifest physical and perceptual form similarity that distinguishes facial identities. Low-frequency induced oscillations (< 20 Hz) manifested more general similarity structure, which was not limited to identity, and spanned physical and perceived form and motion. A supplementary fMRI-constrained source reconstruction implicated fusiform gyrus and V5 in this similarity-based representation. These findings introduce a potential link between “face space” encoding and oscillatory network communication, which generates new hypotheses about the potential oscillation-mediated mechanisms that might encode facial dimensions

Medical Image Processing Validation and Accuracy Prediction:from Clinical Exploitability to Brain Connectivity

da aggiungere con l versione definitiva della tesiValidation and accuracy assessment are the main bottlenecks preventing the adoption of many medical image processing algorithms in the clinical practice. In the classical approach, a-posteriori analysis is performed based on some predefined objective metrics. In this thesis, a different approach is proposed. The basic idea consists in predicting the accuracy that will result from a given processing on a given type of data based on the identification and characterization of the sources of inaccuracy intervening along the whole chain. To this scope we have developed a complete validation framework, that exploits not only the classical validation and accuracy assessment metrics and approaches, but also methods arising from risk analysis and graph theory. It is proposed a proof of concept in both single-task and complex clinical scenarios. For what concerns simple clinical scenarios, we have focused our work on the validation of Magnetic Resonance (MR) images registration, highlighting not only the registration framework related aspects, but also considering the possible sources of inaccuracies that can rise from the quality of the images themselves. In particular MR images are normally corrupted by random noise from the measurement process complicating the automatic feature extraction and analysis of clinical data. The accurate registration of images observed in additive noise is a challenging task. The noise can increase the number of misregistered regions, and decrease the accuracy of subpixel registration. Besides, considering complex scenarios, we have investigated the problems related to a Diffusion MR images analysis pipeline, in order to detect the critical aspect that affect the accuracy of the results the most, especially in terms of robustness and reproducibility. In this context we have taken into account the whole processing system that comprises segmentation, registration and data reconstruction steps by stressing both each single module and the framework itself on the whole. Results show that the proposed model not only provides a good prediction performance, but also suggests the optimal processing approach in terms of accuracy, time load and robustness

Validation through Accuracy Prediction in Neuroimage Registration

Validation and accuracy assessment are the main bottlenecks preventing the adoption of many medical image processing algorithms in the clinical practice. In the classical approach, a-posteriori analysis is performed based on some predefined objective metrics. The main limitation of this methodology is in the fact that it does not provide a mean to estimate what the performance would be a-priori, and thus to shape the processing workflow in the most suitable way. In this paper, we propose a different approach based on Petri Nets. The basic idea consists in predicting the accuracy that will result from a given processing on a given type of data based on the identification and characterization of the sources of inaccuracy intervening along the whole chain. Here we propose a proof of concept in the specific case of image registration. A Petri Net is constructed after the detection of the possible sources of inaccuracy and the evaluation of their respective impact on the estimation of the deformation field. A training set of five different synthetic volumes is used. Afterward, validation is performed on a different set of five synthetic volumes by comparing the estimated inaccuracy with the posterior measurements according to a set of predefined metrics. Two real cases are also considered. Results show that the proposed model provides a good prediction performance. An extended set of clinical data will allow the complete characterization of the system for the considered task

Registration accuracy assessment on noisy neuroimages

Validation and accuracy assessment are the main bottlenecks preventing the adoption of many medical image processing algorithms in the clinical practice. In the classical approach, a-posteriori analysis is performed based on some predefined objective metrics. In this paper, a different approach based on Petri Nets is proposed. The basic idea consists in predicting the accuracy that will result from a given processing on a given type of data based on the identification and characterization of the sources of inaccuracy intervening along the whole chain. Here it is proposed a proof of concept in the specific case of noisy Magnetic Resonance image registration. Magnetic Resonance images are normally corrupted by random noise from the measurement process complicating the automatic feature extraction and analysis of clinical data. The accurate registration of images observed in additive noise is a challenging task. The noise can increase the number of misregistered regions, and decrease the accuracy of subpixel registration. A Petri Net is built after the detection of the possible sources of inaccuracy, ranging from the images noise to the registration parameters adopted, and the evaluation of their respective impact on the estimation of the deformation field. A training set of five different synthetic volumes is used. Afterward, validation is performed on a different set of five synthetic volumes by comparing the estimated inaccuracy with the posterior measurements according to a set of predefined metrics. Results show that the proposed model provides a good prediction performance. An extended set of clinical data will allow the complete characterization of the system for the considered task

A New Paradigm for Geometric AccuracyPrediction in Medical Image Segmentation

Safety and accuracy are two important keywords when deal-ing with life-critical systems. In particular, in medical image processingthese two aspects have to be taken into careful attention since it represents the first step in the process that starts with the image acquisition and proceeds to the diagnosis step and therapy definition. Therefore it is important to analyze the possible inaccuracy sources that can be found in this step, since they will affect the accuracy of the whole system. In literature there are several techniques for the safety analysis and accuracy evaluation of complex systems, however most of the proposed approaches in the field of medical image processing only face the problem of defining different metrics that can accurately assess the accuracy of imaging processing techniques from a purely geometrical and quantitative point of view.In this paper we introduce a different approach to the problem of segmentation accuracy assessment based on the analysis of the critical aspects in the segmentation workflow that can affect the accuracy of the overall system, according to the specific clinical problem under investigation.We present a proof of feasibility of our approach by combining the use of Petri Nets for the modeling of the workflow of segmentation procedures in two different clinical scenarios: accuracy evaluation of manual segmentations performed by non-experts (skin tumors) and of a semi-automatic system (liver lesions).Results show that it is feasible to correlate the qualitative analysis withthe quantitative measures: in this way it is possible to predict the inaccuracy of the segmentation results and to optimize the different steps of the system before even acquiring and processing the data

Automated identification of the human thalamic nuclei using local white-matter properties

We introduce a method for automatic segmentation of the thalamic nuclei. The method uses diffusion-weighted MR images to subdivide the thalamus in a predefined set of nuclei. The method relies on two basic assumptions. First, it assumes that white-matter fascicles’ orientation is homogeneous within each thalamic nucleus (Wiegell et al., 1999). Second, it assumes that nuclei are spatially homogeneous (Wiegell et al., 2003). Based on these assumptions thalamic nuclei are segmented by a weighted combination of local tensor information (Basser et al., 1994) and spatial coordinates. The method avoids manual intervention by computing in each subject the dissimilarity (Pekalska and Duin, 2005) among voxels in the thalamic region. Dissimilarity represents the difference of each voxel from the rest of the voxels along each dimension; spatial position and diffusion tensor. We use multidimensional scaling (Bronstein et al., 2005) to produce a compact encoding of the dissimilarity information. This reduces processing time for the clustering algorithm. Finally, we use k-means clustering (Hartigan and Wong, 1979) to automatically segment the thalamic nuclei

Multimodal MRI-based tissue classification in breast ductal carcinoma

In this paper we propose a pipeline to integrate breast diffusion and perfusion MRI for diagnosis, surgical planning and follow-up. Dynamic contrast enhanced (DCE) and diffusion weighted (DWI) MRI provide complementary information on the tissue structure and properties: while DCE-MRI allows the characterization of the lesion angiogenesis, DWI techniques can probe the apparent diffusion coefficient (ADC) and therefore assess the nature and cellularity of the lesions. Here we propose a two-step process for the integration of these modalities. First, dissimilarity-based clustering is performed on DCE-MRI to identify the different tumoral subregions. These are then mapped onto the DWI images following inter-modal registration. The probability density functions (PDFs) of the so-identified subregions in the ADC map are extracted and compared through non-parametric testing. Results show that subregions corresponding to different clusters hold statistically different PDFs, indicating a degree of consistency in the information obtained from the two modalities while providing a posterior validation of the registration method. This enables the efficient integration of the information brought by DCE and DWI, respectively, while taking advantage of their complementarity