Modeling 4D Changes in Pathological Anatomy Using Domain Adaptation: Analysis of TBI Imaging Using a Tumor Database

Abstract

Analysis of 4D medical images presenting pathology (i.e., lesions) is significantly challenging due to the presence of complex changes over time. Image analysis methods for 4D images with lesions need to account for changes in brain structures due to deformation, as well as the formation and deletion of new structures (e.g., edema, bleeding) due to the physiological processes associated with damage, intervention, and recovery. We propose a novel framework that models 4D changes in pathological anatomy across time, and provides explicit mapping from a healthy template to subjects with pathology. Moreover, our framework uses transfer learning to leverage rich information from a known source domain, where we have a collection of completely segmented images, to yield effective appearance models for the input target domain. The automatic 4D segmentation method uses a novel domain adaptation technique for generative kernel density models to transfer information between different domains, resulting in a fully automatic method that requires no user interaction. We demonstrate the effectiveness of our novel approach with the analysis of 4D images of traumatic brain injury (TBI), using a synthetic tumor database as the source domain.

4D active cut: An interactive tool for pathological anatomy modeling

“…We follow the 4D pathological anatomy modeling framework of Wang et al [7], and define π k,t = A k o φ t + Q k,t , where A is the tissue class probability that is initially associated with the healthy template, φ t is the diffeomorphic deformation from time t to the atlas, and Q t is the non-diffeomorphic probabilistic change. We use alternating gradient descent to estimate A, φ t and Q t by optimizing $\mathcal{F}(A,{\varphi }_t,Q_t)=-{\sum }_{t=1}^T{𝔼}_{p(\mathbf{z}\mid \mathbf{x})}\left[\log p\right(\mathbf{z},\mathbf{x}\mid \theta ,{\pi }_t\left)\right]$…”

Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

“…Task Domain Transfer type Brain Zhang and Shen (2012) MCI conversion prediction different same feature, multi-task Wang et al (2013) tissue, lesion segmentation same different instance, weight van Opbroek et al (2015a) tissue, lesion segmentation same different instance, weight Guerrero et al (2014) AD classification same different instance, align van Opbroek et al (2015b) tissue, lesion segmentation same different instance, weight Cheng et al (2015) MCI conversion prediction different same feature, multi-task Goetz et al (2016) tumor segmentation same different instance, weight Wachinger and Reuter (2016) AD classification same different instance, weight Cheplygina et al (2016a) tissue segmentation same different instance, weight Ghafoorian et al (2017) lesion segmentation same different feature, pretraining Kamnitsas et al (2017) segmentation of abnormalities same different feature, pretraining Alex et al (2017) lesion segmentation different same feature, pretraining Hofer et al (2017) AD classification same different instance, align Hon and Khan (2017) AD classification different different feature, pretraining Kouw et al (2017) tissue segmentation same, different instance, align Breast Huynh and Giger (2016) tumor detection different different feature, pretraining Samala et al (2016) mass detection same different feature, pretraining Kisilev et al (2016) lesion detection, description in mammography or ultrasound different same feature, multi-task Bi et al (2008) abnormality classification different same feature, multi-task Schlegl et al (2014) lung tissue classification different same/different feature, pretraining Bar et al (2015) chest pathology detection different different feature, pretraining Ciompi et al (2015) nodule classification different different feature, pretraining Shen et al (2016) lung cancer malignancy prediction different same feature, multi-task Chen et al (2017b) attribute classification in nodules different same feature, multi-task Hussein et al (2017) attribute regression, malignancy prediction different same feature, multi-task Cheplygina et al (2017) COPD classification same different instance, weight…”

Modeling 4D pathological changes by leveraging normative models

“…The processing framework includes explicit mapping from a normative probabilistic template representing healthy anatomy to each image of the subject’s image time series and multimodal segmentation and extends early work (Wang et al, 2013b) in which we focused on a pathological anatomy modeling framework with transfer learning-based image appearance model estimation by adding user initialization and user interaction as alternative approaches to estimate the image appearance model. We also present a study of manual expert annotation comparison by having three human experts perform manual segmentation using the latest version of ITK-SNAP 3.2.0 (Yushkevich et al, 2006), which is to our knowledge the only tool available to take multimodal data as input for performing manual segmentation.…”