Modeling 4D pathological changes by leveraging normative models

Abstract

With the increasing use of efficient multimodal 3D imaging, clinicians are able to access longitudinal imaging to stage pathological diseases, to monitor the efficacy of therapeutic interventions, or to assess and quantify rehabilitation efforts. Analysis of such four-dimensional (4D) image data presenting pathologies, including disappearing and newly appearing lesions, represents a significant challenge due to the presence of complex spatio-temporal changes. Image analysis methods for such 4D image data have to include not only a concept for joint segmentation of 3D datasets to account for inherent correlations of subject-specific repeated scans but also a mechanism to account for large deformations and the destruction and formation of lesions (e.g., edema, bleeding) due to underlying physiological processes associated with damage, intervention, and recovery. In this paper, we propose a novel framework that provides a joint segmentation-registration framework to tackle the inherent problem of image registration in the presence of objects not present in all images of the time series. Our methodology models 4D changes in pathological anatomy across time and and also provides an explicit mapping of a healthy normative template to a subject’s image data with pathologies. Since atlas-moderated segmentation methods cannot explain appearance and locality pathological structures that are not represented in the template atlas, the new framework provides different options for initialization via a supervised learning approach, iterative semisupervised active learning, and also transfer learning, which results in a fully automatic 4D segmentation method. We demonstrate the effectiveness of our novel approach with synthetic experiments and a 4D multimodal MRI dataset of severe traumatic brain injury (TBI), including validation via comparison to expert segmentations. However, the proposed methodology is generic in regard to different clinical applications requiring quantitative analysis of 4D imaging representing spatio-temporal changes of pathologies.

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

“…We found 7 asymmetric approaches that transformed target domain features into source domain features via generative adversarial networks [ 56 , 57 ], Bregman divergence minimization [ 58 ], probabilistic models [ 59 ], median [ 60 ], and nearest neighbors [ 61 ]. Contrarily, Qin et al [ 62 ] transformed source domain features into target domain features via dictionary-based interpolation to optimize a model on the target domain.…”

Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review

“…We found 7 asymmetric approaches that transformed target domain features into source domain features via generative adversarial networks [54,55], Bregman divergence minimization [56], probabilistic models [57], median [58], and nearest neighbors [59]. Contrarily, Qin et al [60] transformed source domain features into target domain features via dictionary-based interpolation to optimize a model on the target domain.…”

Speech Map: a statistical multimodal atlas of 4D tongue motion during speech from tagged and cine MR images

“…In the present work, both cine- and tagged-MRI are in the same spatio-temporal coordinate system and therefore the nonlinear mappings learned in one modality can be used to map the other modality. In related developments, Wang et al [15] proposed a joint segmentation and registration method to model 4D changes in pathological anatomy across time by providing an explicit mapping of a healthy normative template. In that work, because a normative template cannot deal with pathological appearance for the joint segmentation and registration, they used different options for initialization via a supervised and semi-supervised learning and transfer learning approach for the application of traumatic brain injury.…”