Multiple Sclerosis Lesion Detection Using Constrained GMM and Curve Evolution

This paper focuses on the detection and segmentation of Multiple Sclerosis (MS) lesions in magnetic resonance (MRI) brain images. To capture the complex tissue spatial layout, a probabilistic model termed Constrained Gaussian Mixture Model (CGMM) is proposed based on a mixture of multiple spatially oriented Gaussians per tissue. The intensity of a tissue is considered a global parameter and is constrained, by a parameter-tying scheme, to be the same value for the entire set of Gaussians that are related to the same tissue. MS lesions are identified as outlier Gaussian components and are grouped to form a new class in addition to the healthy tissue classes. A probability-based curve evolution technique is used to refine the delineation of lesion boundaries. The proposed CGMM-CE algorithm is used to segment 3D MRI brain images with an arbitrary number of channels. The CGMM-CE algorithm is automated and does not require an atlas for initialization or parameter learning. Experimental results on both standard brain MRI simulation data and real data indicate that the proposed method outperforms previously suggested approaches, especially for highly noisy data

Model Transport: Towards Scalable Transfer Learning on Manifolds

We consider the intersection of two research fields: transfer learning and statistics on manifolds. In particular, we consider, for manifold-valued data, transfer learning of tangent-space models such as Gaussians distributions, PCA, regression, or classifiers. Though one would hope to simply use ordinary Rn-transfer learning ideas, the manifold structure prevents it. We overcome this by basing our method on inner-product-preserving parallel transport, a well-known tool widely used in other problems of statistics on manifolds in computer vision. At first, this straightforward idea seems to suffer from an obvious shortcoming: Transporting large datasets is prohibitively expensive, hindering scalability. Fortunately, with our approach, we never transport data. Rather, we show how the statistical models themselves can be transported, and prove that for the tangent-space models above, the transport “commutes” with learning. Consequently, our compact framework, applicable to a large class of manifolds, is not restricted by the size of either the training or test sets. We demonstrate the approach by transferring PCA and logistic-regression models of real-world data involving 3D shapes and image descriptors

A Deep Moving-camera Background Model

In video analysis, background models have many applications such as background/foreground separation, change detection, anomaly detection, tracking, and more. However, while learning such a model in a video captured by a static camera is a fairly-solved task, in the case of a Moving-camera Background Model (MCBM), the success has been far more modest due to algorithmic and scalability challenges that arise due to the camera motion. Thus, existing MCBMs are limited in their scope and their supported camera-motion types. These hurdles also impeded the employment, in this unsupervised task, of end-to-end solutions based on deep learning (DL). Moreover, existing MCBMs usually model the background either on the domain of a typically-large panoramic image or in an online fashion. Unfortunately, the former creates several problems, including poor scalability, while the latter prevents the recognition and leveraging of cases where the camera revisits previously-seen parts of the scene. This paper proposes a new method, called DeepMCBM, that eliminates all the aforementioned issues and achieves state-of-the-art results. Concretely, first we identify the difficulties associated with joint alignment of video frames in general and in a DL setting in particular. Next, we propose a new strategy for joint alignment that lets us use a spatial transformer net with neither a regularization nor any form of specialized (and non-differentiable) initialization. Coupled with an autoencoder conditioned on unwarped robust central moments (obtained from the joint alignment), this yields an end-to-end regularization-free MCBM that supports a broad range of camera motions and scales gracefully. We demonstrate DeepMCBM's utility on a variety of videos, including ones beyond the scope of other methods. Our code is available at https://github.com/BGU-CS-VIL/DeepMCBM .Comment: 26 paged, 5 figures. To be published in ECCV 202

A Mixture of Manhattan Frames: Beyond the Manhattan World

Objects and structures within man-made environments typically exhibit a high degree of organization in the form of orthogonal and parallel planes. Traditional approaches to scene representation exploit this phenomenon via the somewhat restrictive assumption that every plane is perpendicular to one of the axes of a single coordinate system. Known as the Manhattan-World model, this assumption is widely used in computer vision and robotics. The complexity of many real-world scenes, however, necessitates a more flexible model. We propose a novel probabilistic model that describes the world as a mixture of Manhattan frames: each frame defines a different orthogonal coordinate system. This results in a more expressive model that still exploits the orthogonality constraints. We propose an adaptive Markov-Chain Monte-Carlo sampling algorithm with Metropolis-Hastings split/merge moves that utilizes the geometry of the unit sphere. We demonstrate the versatility of our Mixture-of-Manhattan-Frames model by describing complex scenes using depth images of indoor scenes as well as aerial-LiDAR measurements of an urban center. Additionally, we show that the model lends itself to focal-length calibration of depth cameras and to plane segmentation.United States. Office of Naval Research. Multidisciplinary University Research Initiative (Award N00014-11-1-0688)United States. Defense Advanced Research Projects Agency (Award FA8650-11-1-7154)Technion, Israel Institute of Technology (MIT Postdoctoral Fellowship Program

Evidence-Based Guidelines for Empirical Therapy of Neutropenic Fever in Korea

Neutrophils play an important role in immunological function. Neutropenic patients are vulnerable to infection, and except fever is present, inflammatory reactions are scarce in many cases. Additionally, because infections can worsen rapidly, early evaluation and treatments are especially important in febrile neutropenic patients. In cases in which febrile neutropenia is anticipated due to anticancer chemotherapy, antibiotic prophylaxis can be used, based on the risk of infection. Antifungal prophylaxis may also be considered if long-term neutropenia or mucosal damage is expected. When fever is observed in patients suspected to have neutropenia, an adequate physical examination and blood and sputum cultures should be performed. Initial antibiotics should be chosen by considering the risk of complications following the infection; if the risk is low, oral antibiotics can be used. For initial intravenous antibiotics, monotherapy with a broad-spectrum antibiotic or combination therapy with two antibiotics is recommended. At 3-5 days after beginning the initial antibiotic therapy, the condition of the patient is assessed again to determine whether the fever has subsided or symptoms have worsened. If the patient's condition has improved, intravenous antibiotics can be replaced with oral antibiotics; if the condition has deteriorated, a change of antibiotics or addition of antifungal agents should be considered. If the causative microorganism is identified, initial antimicrobial or antifungal agents should be changed accordingly. When the cause is not detected, the initial agents should continue to be used until the neutrophil count recovers