Improving the domain generalization and robustness of neural networks for medical imaging

Deep neural networks are powerful tools to process medical images, with great potential to accelerate clinical workflows and facilitate large-scale studies. However, in order to achieve satisfactory performance at deployment, these networks generally require massive labeled data collected from various domains (e.g., hospitals, scanners), which is rarely available in practice. The main goal of this work is to improve the domain generalization and robustness of neural networks for medical imaging when labeled data is limited. First, we develop multi-task learning methods to exploit auxiliary data to enhance networks. We first present a multi-task U-net that performs image classification and MR atrial segmentation simultaneously. We then present a shape-aware multi-view autoencoder together with a multi-view U-net, which enables extracting useful shape priors from complementary long-axis views and short-axis views in order to assist the left ventricular myocardium segmentation task on the short-axis MR images. Experimental results show that the proposed networks successfully leverage complementary information from auxiliary tasks to improve model generalization on the main segmentation task. Second, we consider utilizing unlabeled data. We first present an adversarial data augmentation method with bias fields to improve semi-supervised learning for general medical image segmentation tasks. We further explore a more challenging setting where the source and the target images are from different data distributions. We demonstrate that an unsupervised image style transfer method can bridge the domain gap, successfully transferring the knowledge learned from labeled balanced Steady-State Free Precession (bSSFP) images to unlabeled Late Gadolinium Enhancement (LGE) images, achieving state-of-the-art performance on a public multi-sequence cardiac MR segmentation challenge. For scenarios with limited training data from a single domain, we first propose a general training and testing pipeline to improve cardiac image segmentation across various unseen domains. We then present a latent space data augmentation method with a cooperative training framework to further enhance model robustness against unseen domains and imaging artifacts.Open Acces

Similarity Measures and Dimensionality Reduction Techniques for Time Series Data Mining

The chapter is organized as follows. Section 2 will introduce the similarity matching problem on time series. We will note the importance of the use of efficient data structures to perform search, and the choice of an adequate distance measure. Section 3 will show some of the most used distance measure for time series data mining. Section 4 will review the above mentioned dimensionality reduction techniques

Fast myocardial T(1) mapping using cardiac motion correction

PURPOSE: To improve the efficiency of native and postcontrast high-resolution cardiac T(1) mapping by utilizing cardiac motion correction. METHODS: Common cardiac T(1) mapping techniques only acquire data in a small part of the cardiac cycle, leading to inefficient data sampling. Here, we present an approach in which 80% of each cardiac cycle is used for T(1) mapping by integration of cardiac motion correction. Golden angle radial data was acquired continuously for 8 s with in-plane resolution of 1.3 × 1.3 mm(2). Cine images were reconstructed for nonrigid cardiac motion estimation. Images at different TIs were reconstructed from the same data, and motion correction was performed prior to T(1) mapping. Native T(1) mapping was evaluated in healthy subjects. Furthermore, the technique was applied for postcontrast T(1) mapping in 5 patients with suspected fibrosis. RESULTS: Cine images with high contrast were obtained, leading to robust cardiac motion estimation. Motion-corrected T(1) maps showed myocardial T(1) times similar to cardiac-triggered T(1) maps obtained from the same data (1288 ± 49 ms and 1259 ± 55 ms, respectively) but with a 34% improved precision (spatial variation: 57.0 ± 12.5 ms and 94.8 ± 15.4 ms, respectively, P < 0.0001) due to the increased amount of data. In postcontrast T(1) maps, focal fibrosis could be confirmed with late contrast-enhancement images. CONCLUSION: The proposed approach provides high-resolution T(1) maps within 8 s. Data acquisition efficiency for T(1) mapping was improved by a factor of 5 by integration of cardiac motion correction, resulting in precise T(1) maps

A study of left ventricular (LV) segmentation on cardiac cine-MR images

Left ventricular segmentation from cardiac images has high impact to have early diagnosis of various cardiovascular disorders. However, it is really a challenging task to segment left ventricular images from magnetic resonance image (MRI). In this paper, we explore several state-of-the-art segmentation algorithms applied on left ventricular (LV) segmentation on cardiac cine-MR images. Both adaptive and global thresholding algorithms along with region-based segmentation algorithm have been explored. Edge-based segmentation is disregard due to the absence of edge information in the employed dataset. For evaluation, we explored a benchmark dataset that was used for the MICCAI 3D segmentation challenge. We found that the cardiac MRI global thresholding has proved to be much efficient and robust than the adaptive thresholding. We achieved more than 92% accuracy for global thresholding, whereas, about 78% accuracy for the adaptive thresholding approach. The use of entropy or histogram to characterize segmentation in place of the intensity value of the pixel has a vital effect on segmentation efficiency. It is evident that the intensity information is corrupted by acquisition procedure, as well as the structure of organs. Due to the lack of boundary information in cardiac cine-MRI, clustering and region-based segmentation have produced more than 93% segmentation accuracy. For the case of soft clustering, the increased accuracy is found as 96%. However, more explorations are required, specially based on deep learning approaches on very large datasets

A reversal coarse-grained analysis with application to an altered functional circuit in depression

Introduction: When studying brain function using functional magnetic resonance imaging (fMRI) data containing tens of thousands of voxels, a coarse-grained approach – dividing the whole brain into regions of interest – is applied frequently to investigate the organization of the functional network on a relatively coarse scale. However, a coarse-grained scheme may average out the fine details over small spatial scales, thus rendering it difficult to identify the exact locations of functional abnormalities. Methods: A novel and general approach to reverse the coarse-grained approach by locating the exact sources of the functional abnormalities is proposed. Results: Thirty-nine patients with major depressive disorder (MDD) and 37 matched healthy controls are studied. A circuit comprising the left superior frontal gyrus (SFGdor), right insula (INS), and right putamen (PUT) exhibit the greatest changes between the patients with MDD and controls. A reversal coarse-grained analysis is applied to this circuit to determine the exact location of functional abnormalities. Conclusions: The voxel-wise time series extracted from the reversal coarse-grained analysis (source) had several advantages over the original coarse-grained approach: (1) presence of a larger and detectable amplitude of fluctuations, which indicates that neuronal activities in the source are more synchronized; (2) identification of more significant differences between patients and controls in terms of the functional connectivity associated with the sources; and (3) marked improvement in performing discrimination tasks. A software package for pattern classification between controls and patients is available in Supporting Information

Statistical and Dynamical Modeling of Riemannian Trajectories with Application to Human Movement Analysis

abstract: The data explosion in the past decade is in part due to the widespread use of rich sensors that measure various physical phenomenon -- gyroscopes that measure orientation in phones and fitness devices, the Microsoft Kinect which measures depth information, etc. A typical application requires inferring the underlying physical phenomenon from data, which is done using machine learning. A fundamental assumption in training models is that the data is Euclidean, i.e. the metric is the standard Euclidean distance governed by the L-2 norm. However in many cases this assumption is violated, when the data lies on non Euclidean spaces such as Riemannian manifolds. While the underlying geometry accounts for the non-linearity, accurate analysis of human activity also requires temporal information to be taken into account. Human movement has a natural interpretation as a trajectory on the underlying feature manifold, as it evolves smoothly in time. A commonly occurring theme in many emerging problems is the need to \emph{represent, compare, and manipulate} such trajectories in a manner that respects the geometric constraints. This dissertation is a comprehensive treatise on modeling Riemannian trajectories to understand and exploit their statistical and dynamical properties. Such properties allow us to formulate novel representations for Riemannian trajectories. For example, the physical constraints on human movement are rarely considered, which results in an unnecessarily large space of features, making search, classification and other applications more complicated. Exploiting statistical properties can help us understand the \emph{true} space of such trajectories. In applications such as stroke rehabilitation where there is a need to differentiate between very similar kinds of movement, dynamical properties can be much more effective. In this regard, we propose a generalization to the Lyapunov exponent to Riemannian manifolds and show its effectiveness for human activity analysis. The theory developed in this thesis naturally leads to several benefits in areas such as data mining, compression, dimensionality reduction, classification, and regression.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks

Magnetic resonance fingerprinting (MRF) quantifies multiple nuclear magnetic resonance parameters in a single and fast acquisition. Standard MRF reconstructs parametric maps using dictionary matching, which lacks scalability due to computational inefficiency. We propose to perform MRF map reconstruction using a spatiotemporal convolutional neural network, which exploits the relationship between neighboring MRF signal evolutions to replace the dictionary matching. We evaluate our method on multiparametric brain scans and compare it to three recent MRF reconstruction approaches. Our method achieves state-of-the-art reconstruction accuracy and yields qualitatively more appealing maps compared to other reconstruction methods. In addition, the reconstruction time is significantly reduced compared to a dictionary-based approach.Comment: Accepted for Machine Learning for Medical Image Reconstruction (MLMIR) workshop at MICCAI 2018. The revision corrects Amaresha's last name and Section 2.1 (scanner type and flip angles