Author: Jon Middleton

Authors: Haleh Akrami (Signal and Image Processing Institute at University of Southern California)*; Anand Joshi (University of Southern California); Sergul Aydore (Amazon Web Services); Richard Leahy (Signal and Image Processing Institute at University of Southern California)

Poster

Abstract: The Variational AutoEncoder (VAE) has become one of the most popular models for anomaly detection in applications such as lesion detection in medical images. The VAE is a generative graphical model that is used to learn the data distribution from samples and then generate new samples from this distribution. By training on normal samples, the VAE can be used to detect inputs that deviate from this learned distribution. The VAE models the output as a conditionally independent Gaussian characterized by means and variances for each output dimension. VAEs can therefore use reconstruction probability instead of reconstruction error for anomaly detection. Unfortunately, joint optimization of both mean and variance in the VAE leads to the well-known problem of shrinkage or underestimation of variance. We describe an alternative VAE model, Quantile-Regression VAE (QR-VAE), that avoids this variance shrinkage problem by estimating conditional quantiles for the given input image.
Using the estimated quantiles, we compute the conditional mean and variance for input images under the Gaussian model. We then compute reconstruction probability using this model as a principled approach to outlier or anomaly detection.
We also show how our approach can be used for heterogeneous thresholding of images for detecting lesions in brain images.

Authors: Felix Ambellan (Zuse Institute Berlin)*; Stefan Zachow (Zuse Institute Berlin); Christoph von Tycowicz (Zuse Institute Berlin)

Poster

Abstract: Three-dimensional medical imaging enables detailed understanding of osteoarthritis structural status. However, there remains a vast need for automatic, thus, reader-independent measures that provide reliable assessment of subject-specific clinical outcomes. To this end, we derive a consistent generalization of the recently proposed B-score to Riemannian shape spaces. We further present an algorithmic treatment yielding simple, yet efficient computations allowing for analysis of large shape populations with several thousand samples. Our intrinsic formulation exhibits improved discrimination ability over its Euclidean counterpart, which we demonstrate for predictive validity on assessing risks of total knee replacement. This result highlights the potential of the geodesic B-score to enable improved personalized assessment and stratification for interventions.

Authors: Lianrui Zuo (Johns Hopkins University)*; Blake E Dewey (Johns Hopkins University); Aaron Carass (Johns Hopkins University, USA); Yihao Liu (Johns Hopkins University); Yufan He (Johns Hopkins University); Peter Calabresi (johns hopkins university); Jerry L Prince (Johns Hopkins University)

Poster

Abstract: Accuracy and consistency are two key factors in computer-assisted magnetic resonance (MR) image analysis. However, contrast variation from site to site caused by lack of standardization in MR acquisition impedes consistent measurements. In recent years, image harmonization approaches have been proposed to compensate for contrast variation in MR images. Current harmonization approaches either require cross-site traveling subjects for supervised training or heavily rely on site-specific harmonization models to encourage harmonization accuracy. These requirements potentially limit the application of current harmonization methods in large-scale multi-site studies. In this work, we propose an unsupervised MR harmonization framework, CALAMITI (Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration), based on information bottleneck theory. CALAMITI learns a disentangled latent space using a unified structure for multi-site harmonization without the need for traveling subjects. Our model is also able to adapt itself to harmonize MR images from a new site with fine tuning solely on images from the new site. Both qualitative and quantitative results show that the proposed method achieves superior performance compared with other unsupervised harmonization approaches.

Authors: Naresh Nandakumar (JHU)*; Komal Manzoor (JHU); Shruti Agarwal (JHU); Sachin Gujar (Johns Hopkins University); Jay Pillai (Johns Hopkins RAIL); Haris Sair (Johns Hopkins RAIL); Archana Venkataraman (Johns Hopkins University)

Poster

Abstract: We present a deep neural network architecture that combines multi-scale spatial attention with temporal attention to simultaneously localize the language and motor areas of the eloquent cortex from dynamic functional connectivity data. Our multi-scale spatial attention operates on graph-based features extracted from the connectivity matrices, thus honing in on the inter-regional interactions that collectively define the eloquent cortex. At the same time, our temporal attention model selects the intervals during which these interactions are most pronounced.
The final stage of our model employs multi-task learning to differentiate between the eloquent subsystems. Our training strategy enables us to handle missing eloquent class labels by freezing the weights in those branches while updating the rest of the network weights. We evaluate our method on resting-state fMRI data from one synthetic dataset and one in-house brain tumor dataset while using task fMRI activations as ground-truth labels for the eloquent cortex. Our model achieves higher localization accuracies than conventional deep learning approaches. It also produces interpretable spatial and temporal attention features which can provide further insights for presurgical planning. Thus, our model shows translational promise for improving the safety of brain tumor resections.

Authors: Peter A Wijeratne (University College London)*; Daniel Alexander (University College London)

Poster

Abstract: Progressive diseases worsen over time and can be characterised by sequences of events that correspond to changes in observable features of disease progression. Here we connect ideas from two formerly separate methodologies — event-based and hidden Markov modelling — to derive a new generative model of disease progression: the Temporal Event-Based Model (TEBM). TEBM can uniquely infer the most likely group-level sequence and timing of events (natural history) from mixed data types. Moreover, it can infer and predict individual-level trajectories (prognosis) even when data are missing, giving it high clinical utility. Here we derive TEBM and provide an inference scheme based on the expectation maximisation algorithm. We use imaging, clinical and biofluid data from the Alzheimer’s Disease Neuroimaging Initiative to demonstrate the validity and utility of our model. First, we train TEBM to uncover a new sequence and timing of events in Alzheimer’s disease, which are inferred to occur over a period of ~17.6 years. Next, we demonstrate the utility of TEBM in predicting clinical progression, and that TEBM provides improved utility over a comparative disease progression model. Finally, we demonstrate that TEBM maintains predictive accuracy with up to 50% missing data. These results support the clinical validity of TEBM and its broader utility in real-world medical applications.

Authors: Xianfeng GU (Stony Brook University)*; Yalin Wang (Arizona State University); Dongsheng An (Stony Brook University); Min Zhang (Brigham and Women’s Hospital, Harvard Medical School); Na Lei (Dalian University of Technology); Tong Zhao (inria); Jianfeng Wu (Arizona State University); Xiaoyin Xu (Brigham and Women’s Hospital, Harvard Medical School)

Poster

Abstract: Biomarkers play an important role in preclinical/early detection and intervention in Alzheimer’s disease (AD). However, obtaining effective biomarkers for AD is still a big challenge. In this work, we propose to use the worst transportation cost as the biomarker for the A beta brain surfaces.
The worst transportation (WT) aims to find the least economical way to transport one measure to the other, which contrasts to the optimal transportation (OT) that find the most economical way between measures. In contrast, maps find the The transportation cost of an OT map is the Wasserstein distance between the measures, which has been broadly applied for shape analysis.
To compute the WT cost, we generalize the Brenier theorem for the OT map to the WT map, and show that the WT map is the gradient of a concave function satisfying the Monge-Ampere equation. We also develop an algorithm to compute the WT map based on computational geometry. Finally, we successfully use the WT cost to study the group difference between the A beta positive AD patients and A beta negative cognitively unimpaired subjects.

Authors: Jiazhou Chen (South China University of Technology)*; Defu Yang (University of North Carolina at Chapel Hill); Hongmin Cai (South China University of Technology); Martin Styner (University of North Carolina); Guorong Wu (UNC-CH)

Poster

Abstract: A plethora of neuroscience studies show that neurodegenerative diseases such as Alzheimer’s disease (AD) manifest network dysfunction much earlier before the onset of clinical symptoms, where neuropathological burdens often propagate across brain networks in a prion-like manner. In this context, characterizing the in-vivo spreading pathway of neuropathological events provides a new window to understand the pathophysiological mechanism of AD progression. However, little attention has been paid to the intrinsic geometry associated with the spreading pathway of neuropathological events, which indeed requires a network-specific algebra to quantify the associated propagation profile across individuals. To address this challenge, we propose a novel manifold harmonic approach to construct a set of region-adaptive harmonic wavelets, which allow us to capture diverse local network geometry in the aging population using the “swiss army knife” of the network topology. The learned common harmonic wavelets constitute the most representative spreading pathways that can be used to characterize the local propagation patterns of neuropathology on an individual basis. We have evaluated the power of our novel harmonic wavelets in identifying AD-related spreading pathways that lead to cognitive decline. Compared with other popular empirical biomarkers, the harmonic-based propagation patterns show much high sensitivity and specificity in stratifying cognitive normal (CN), early-stage mild cognitive impairment (MCI), and late-stage MCI, which indicates the great potential of being a putative biomarker in predicting the risk of developing AD at the pre-clinical stage.