Lipeng N, Rathi Y. Dynamic Regression for Partial Correlation and Causality Analysis of Functional Brain Networks. Int Conf Med Image Comput Comput Assist Interv. 2017;20(Pt1):365–72.

We propose a general dynamic regression framework for partial correlation and causality analysis of functional brain networks. Using the optimal prediction theory, we present the solution of the dynamic regression problem by minimizing the entropy of the associated stochastic process. We also provide the relation between the solutions and the linear dependence models of Geweke and Granger and derive novel expressions for computing partial correlation and causality using an optimal prediction filter with minimum error variance. We use the proposed dynamic framework to study the intrinsic partial correlation and causal- ity between seven different brain networks using resting state functional MRI (rsfMRI) data from the Human Connectome Project (HCP) and compare our results with those obtained from standard correlation and causality measures. The results show that our optimal prediction filter explains a significant portion of the variance in the rsfMRI data at low frequencies, unlike standard partial correlation analysis.

Otake Y, Yokota F, Fukuda N, Takao M, Takagi S, Yamamura N, Donnell LO, Carl-Fredrik W, Sugano N, Sato Y. Patient-Specific Skeletal Muscle Fiber Modeling from Structure Tensor Field of Clinical CT Images. Int Conf Med Image Comput Comput Assist Interv. 2017;20(Pt1):656–63.

We propose an optimization method for estimating patient- specific muscle fiber arrangement from clinical CT. Our approach first computes the structure tensor field to estimate local orientation, then a geometric template representing fiber arrangement is fitted using a B- spline deformation by maximizing fitness of the local orientation using a smoothness penalty. The initialization is computed with a previously proposed algorithm that takes account of only the muscle’s surface shape. Evaluation was performed using a CT volume (1.0mm3/voxel) and high resolution optical images of a serial cryosection (0.1mm3/voxel). The mean fiber distance error at the initialization of 6.00 mm was decreased to 2.78mm after the proposed optimization for the gluteus maximus muscle, and from 5.28 mm to 3.09 mm for the gluteus medius muscle. The result from 20 patient CT images suggested that the proposed algorithm reconstructed an anatomically more plausible fiber arrangement than the previous method.

Hong Y, Golland P, Zhang M. Fast Geodesic Regression for Population-Based Image Analysis. Int Conf Med Image Comput Comput Assist Interv. 2017;20(Pt1):317–25.

Geodesic regression on images enables studies of brain development and degeneration, disease progression, and tumor growth. The high-dimensional nature of image data presents significant computational challenges for the current regression approaches and prohibits large scale studies. In this paper, we present a fast geodesic regression method that dramatically decreases the computational cost of the inference procedure while maintaining prediction accuracy. We employ an efficient low dimensional representation of diffeomorphic transformations derived from the image data and characterize the regressed trajectory in the space of diffeomorphisms by its initial conditions, i.e., an initial image template and an initial velocity field computed as a weighted average of pairwise diffeomorphic image registration results. This construction is achieved by using a first-order approximation of pairwise distances between images. We demonstrate the efficiency of our model on a set of 3D brain MRI scans from the OASIS dataset and show that it is dramatically faster than the state-of-the-art regression methods while producing equally good regression results on the large subject cohort.

Herz C, Fillion-Robin JC, Onken M, Riesmeier J, Lasso A, Pinter C, Fichtinger G, Pieper S, Clunie D, Kikinis R, Fedorov A. DCMQI: An Open Source Library for Standardized Communication of Quantitative Image Analysis Results using DICOM. Cancer Research. 2017;77(21):e87-e90.

Quantitative analysis of clinical image data is an active area of research that holds promise for precision medicine, early assessment of treatment response, and objective characterization of the disease. Interoperability, data sharing, and the ability to mine the resulting data are of increasing importance, given the explosive growth in the number of quantitative analysis methods being proposed. The Digital Imaging and Communications in Medicine (DICOM) standard is widely adopted for image and metadata in radiology. dcmqi (DICOM for Quantitative Imaging) is a free, open source library that implements conversion of the data stored in commonly used research formats into the standard DICOM representation. dcmqi source code is distributed under BSD-style license. It is freely available as a precompiled binary package for every major operating system, as a Docker image, and as an extension to 3D Slicer. Installation and usage instructions are provided on Harvard DASH.

Halle M, Talos IF, Jakab M, Makris N, Meier D, Wald LL, Fischl B, Kikinis R. Multi-modality MRI-based Atlas of the Brain. 2017.

The Neuroimage Analysis Center’s Computational Clinical Anatomy Core and the Surgical Planning Laboratory at Brigham and Women’s Hospital is pleased to make available a multi-modality MRI-based atlas of the brain. Data was acquired at the Martinos Center for Biomedical Imaging (courtesy Dr. Lawrence Wald) on a Siemens 3T scanner, using a multi-array head coil, in a healthy, 42 year old male. The data set consists of : 1. a volumetric whole head MPRAGE series (voxel size 0.75 mm isotropic). 2. a volumetric whole head T2-weighted series (voxel size 0.75 mm isotropic). 3. a downsampled version of both acquisitions at 1mm isotropic resolution. 4. a per voxel labeling of the structures based on the 1mm volumes. 5. a color file mapping label values to RadLex-ontology derived names and colors suitable for display. 6. MRML files for displaying the volumes in 3D Slicer version 3.6 or greater, available for download. The atlas data is made available under terms of the 3D Slicer License section B.The Slicer4 version also consists of 1. hypotalamic parcellation (courtesy Nikos Makris [Neuroimage. 2013]) 2. cerebellar parcellation (courtesy Nikos Makris [J Cogn Neurosci. 2003], [Neuroimage. 2005]) 3.head and neck muscles segmentation 4. anatomical model hierarchy 5. several pre-defined Scene Views (“anatomy teaching files”). All in a mrb (Medical Reality Bundle) archive file that contains the mrml scene file and all data for loading into Slicer 4 for displaying the volumes in 3D Slicer version 4.0 or greater, available for download.This work is funded as part of the Neuroimaging Analysis Center, grant number P41 RR013218, by the NIH’s National Center for Research Resources (NCRR) and grant number P41 EB015902, by the NIH’s National Institute of Biomedical Imaging and Bioengineering (NIBIB) and the Google Faculty Research Award.Contributors: Ilwoo Lyu and Martin Styner: Sulcal Curves, Samira Farough: Ventricular System, Ibraheem Naeem and Maria Naeem: Head and Neck Muscles, George Papadimitriou: Cerebellar Parcellation, Madiha Tahir: White Matter.This atlas maybe viewed with our Open Anatomy Browser.

Niethammer M, Pohl KM, Janoos F, Wells WM. Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models. SIAM J. Imaging Sci. 2017;10(3):1069–1103.

Segmentation is a fundamental task for extracting semantically meaningful regions from an image. The goal of segmentation algorithms is to accurately assign object labels to each image location. However, image noise, shortcomings of algorithms, and image ambiguities cause uncertainty in label assignment. Estimating this uncertainty is important in multiple application domains, such as segmenting tumors from medical images for radiation treatment planning. One way to estimate these uncertainties is through the computation of posteriors of Bayesian models, which is computationally prohibitive for many practical applications. However, most computationally efficient methods fail to estimate label uncertainty. We therefore propose in this paper the active mean fields (AMF) approach, a technique based on Bayesian modeling that uses a mean-field approximation to efficiently compute a segmentation and its corresponding uncertainty. Based on a variational formulation, the resulting convex model combines any label-likelihood measure with a prior on the length of the segmentation boundary. A specific implementation of that model is the Chan-Vese segmentation model, in which the binary segmentation task is defined by a Gaussian likelihood and a prior regularizing the length of the segmentation boundary. Furthermore, the Euler-Lagrange equations derived from the AMF model are equivalent to those of the popular Rudin-Osher-Fatemi (ROF) model for image denoising. Solutions to the AMF model can thus be implemented by directly utilizing highly efficient ROF solvers on log-likelihood ratio fields. We qualitatively assess the approach on synthetic data as well as on real natural and medical images. For a quantitative evaluation, we apply our approach to the tt icgbench dataset.

Norton I, Ibn Essayed W, Zhang F, Pujol S, Yarmarkovich A, Golby AJ, Kindlmann G, Wasserman D, Estepar RSJ, Rathi Y, Pieper S, Kikinis R, Johnson HJ, Westin CF, O’Donnell LJ. SlicerDMRI: Open Source Diffusion MRI Software for Brain Cancer Research. Cancer Res. 2017;77(21):e101-e103.

Diffusion MRI (dMRI) is the only noninvasive method for mapping white matter connections in the brain. We describe SlicerDMRI, a software suite that enables visualization and analysis of dMRI for neuroscientific studies and patient-specific anatomic assessment. SlicerDMRI has been successfully applied in multiple studies of the human brain in health and disease, and here, we especially focus on its cancer research applications. As an extension module of the 3D Slicer medical image computing platform, the SlicerDMRI suite enables dMRI analysis in a clinically relevant multimodal imaging workflow. Core SlicerDMRI functionality includes diffusion tensor estimation, white matter tractography with single and multi-fiber models, and dMRI quantification. SlicerDMRI supports clinical DICOM and research file formats, is open-source and cross-platform, and can be installed as an extension to 3D Slicer ( More information, videos, tutorials, and sample data are available at Cancer Res; 77(21); e101-3. ©2017 AACR.

Wachinger C, Brennan M, Sharp GC, Golland P. Efficient Descriptor-Based Segmentation of Parotid Glands With Nonlocal Means. IEEE Trans Biomed Eng. 2017;64(7):1492–1502.

OBJECTIVE: We introduce descriptor-based segmentation that extends existing patch-based methods by combining intensities, features, and location information. Since it is unclear which image features are best suited for patch selection, we perform a broad empirical study on a multitude of different features. METHODS: We extend nonlocal means segmentation by including image features and location information. We search larger windows with an efficient nearest neighbor search based on kd-trees. We compare a large number of image features. RESULTS: The best results were obtained for entropy image features, which have not yet been used for patch-based segmentation. We further show that searching larger image regions with an approximate nearest neighbor search and location information yields a significant improvement over the bounded nearest neighbor search traditionally employed in patch-based segmentation methods. CONCLUSION: Features and location information significantly increase the segmentation accuracy. The best features highlight boundaries in the image. SIGNIFICANCE: Our detailed analysis of several aspects of nonlocal means-based segmentation yields new insights about patch and neighborhood sizes together with the inclusion of location information. The presented approach advances the state-of-the-art in the segmentation of parotid glands for radiation therapy planning.

Chen Y, Georgiou TT, Ning L, Tannenbaum A. Matricial Wasserstein-1 Distance. IEEE Control Syst Lett. 2017;1(1):14–9.

We propose an extension of the Wasserstein 1-metric (W1) for density matrices, matrix-valued density measures, and an unbalanced interpretation of mass transport. We use duality theory and, in particular, a "dual of the dual" formulation of W1. This matrix analogue of the Earth Mover’s Distance has several attractive features including ease of computation.