Pectobacterium wasabiae, originally causing soft rot disease in horseradish in Japan, was recently found to cause blackleg-like symptoms on potato in the United States, Canada, and Europe. A draft genome sequence of a Canadian potato isolate of P. wasabiae CFIA1002 will enhance the characterization of its pathogenicity and host specificity features.
Diagnostic magnetic resonance (MR) image quality is highly dependent on the position and orientation of the slice groups, due to the intrinsic high in-slice and low through-slice resolutions of MR imaging. Hence, the higher speed, accuracy, and reproducibility of automatic slice positioning, make it highly desirable over manual slice positioning. However, imaging artifacts, diseases, joint articulation, variations across ages and demographics as well as the extremely high performance requirements prevent state-of-the-art methods, such as volumetric registration, to be an off-the-shelf solution. In this paper, we address all these issues through an automatic slice positioning framework based on redundant and hierarchical learning. Our method has two hallmarks that are specifically designed to achieve high robustness and accuracy. 1) A redundant set of anatomy detectors are learned to provide local appearance cues. These detections are pruned and assembled according to a distributed anatomy model, which captures group-wise spatial configurations among anatomy primitives. This strategy brings about a high level of robustness and works even if a large portion of the target is distorted, missing, or occluded. 2) The detectors are learned and invoked in a hierarchical fashion, with each local detection scheduled and iterated according to its intrinsic invariance property. This iterative alignment process is shown to dramatically improve alignment accuracy. The proposed system is extensively validated on a large dataset including 744 clinical MR scans. Compared to state-of-the-art methods, our method exhibits superior performance in terms of robustness, accuracy, and reproducibility. The methodology is general and can be applied to other anatomies and other imaging modalities.
3D knee magnetic resonance (MR) scout scan is an emerging imaging sequence that facilitates technicians in aligning the imaging planes of diagnostic high resolution MR scans. In this paper, we propose a method to automate this process with the goal of improving the accuracy, robustness and speed of the workflow. To tackle the various challenges coming from MR knee scout scans, our auto-alignment method is built upon a redundant, adaptive and hierarchical anatomy detection system. More specifically, we learn 1) a hierarchical redudant set of anatomy detectors, and 2) ensemble of group-wise spatial configurations across different anatomies, from training data. These learned statistics are integrated into a comprehensive objective function optimized using an expectation-maximization (EM) framework. The optimization provides a new framework for hierarchical detection and adaptive selection of anatomy primitives to derive optimal alignment. Being extensively validated on 744 clinical datasets, our method achieves high accuracy (sub-voxel alignment error), robustness (to severe diseases or imaging artifacts) and fast speed ( 5 sees for 10 alignments).
Organ shape plays an important role in various clinical practices, e.g., diagnosis, surgical planning and treatment evaluation. It is usually derived from low level appearance cues in medical images. However, due to diseases and imaging artifacts, low level appearance cues might be weak or misleading. In this situation, shape priors become critical to infer and refine the shape derived by image appearances. Effective modeling of shape priors is challenging because: (1) shape variation is complex and cannot always be modeled by a parametric probability distribution; (2) a shape instance derived from image appearance cues (input shape) may have gross errors; and (3) local details of the input shape are difficult to preserve if they are not statistically significant in the training data. In this paper we propose a novel Sparse Shape Composition model (SSC) to deal with these three challenges in a unified framework. In our method, a sparse set of shapes in the shape repository is selected and composed together to infer/refine an input shape. The a priori information is thus implicitly incorporated on-the-fly. Our model leverages two sparsity observations of the input shape instance: (1) the input shape can be approximately represented by a sparse linear combination of shapes in the shape repository; (2) parts of the input shape may contain gross errors but such errors are sparse. Our model is formulated as a sparse learning problem. Using L1 norm relaxation, it can be solved by an efficient expectation-maximization (EM) type of framework. Our method is extensively validated on two medical applications, 2D lung localization in X-ray images and 3D liver segmentation in low-dose CT scans. Compared to state-of-the-art methods, our model exhibits better performance in both studies.
In this paper, we propose a learning-based algorithm for automatic medical image annotation based on robust aggregation of learned local appearance cues, achieving high accuracy and robustness against severe diseases, imaging artifacts, occlusion, or missing data. The algorithm starts with a number of landmark detectors to collect local appearance cues throughout the image, which are subsequently verified by a group of learned sparse spatial configuration models. In most cases, a decision could already be made at this stage by simply aggregating the verified detections. For the remaining cases, an additional global appearance filtering step is employed to provide complementary information to make the final decision. This approach is evaluated on a large-scale chest radiograph view identification task, demonstrating a very high accuracy ( > 99.9%) for a posteroanterior/anteroposterior (PA-AP) and lateral view position identification task, compared with the recently reported large-scale result of only 98.2% (Luo, , 2006). Our approach also achieved the best accuracies for a three-class and a multiclass radiograph annotation task, when compared with other state of the art algorithms. Our algorithm was used to enhance advanced image visualization workflows by enabling content-sensitive hanging-protocols and auto-invocation of a computer aided detection algorithm for identified PA-AP chest images. Finally, we show that the same methodology could be utilized for several image parsing applications including anatomy/organ region of interest prediction and optimized image visualization.
Hepatocellular carcinoma (HCC) is a highly aggressive cancer. For patients who are diagnosed with advanced stage disease that are not surgical candidates, the disease is universally lethal. Advance has been made to extend survival with molecular target therapy, but durable complete responses are extremely rare. We report an unusual case where a 74-year-old patient with unresectable HCC received eight months of reduced-dose of sorafenib, a tyrosine kinase inhibitor, and achieved a durable complete remission. At the most recent follow up, he remains in remission 16 months after cessation of treatment, without clinical or imaging evidence of disease recurrence.
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JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.
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In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.