Cerebral thrombus segmentation from Computed Tomography Angiography (CTA) images is significant for accurate diagnosis in acute ischemic stroke. Automatic medical image segmentation can help doctors improve diagnosis efficiency in the clinic. In the past years, convolutional neural networks (CNNs) have been widely applied to automatic medical image segmentation and achieved impressive performance. However, since most existing methods fail to make full use of the contextual information of targets, the segmentation results are far from usability for low-contrast and tiny cerebral thrombosis. To tackle this problem, we proposed a Context Pooling Module (CPM) to take full advantages of local and global context information. In addition, segmentation results of present methods are still inaccurate in the marginal area. Thus, we designed a refinement network to reduce deviations in the coarse segmentation results. We applied our approach to thrombus segmentation in CTA images and tumor segmentation in Magnetic Resonance (MR) images. The experimental results showed that the proposed method outperforms the original U-Net and other state-of-the-art methods for thrombus segmentation, and also achieves competitive results on tumor segmentation.

Query ambiguity prevents existing retrieval systems from returning reasonable results for every query. As there is already lots of work done on resolving ambiguity, vague queries could be handled using corresponding approaches separately if they can be identified in advance. Quantification of the degree of (lack of) ambiguity laysthe groundwork for the identification. In this poster, we propose such a measure using query topics based on the topic structure selected from the Open Directory Project (ODP) taxonomy. We introduce clarity score to quantify the lack of ambiguity with respect to data sets constructed from the TREC collections and the rank correlation test results demonstrate a strong positive association between the clarity scores and retrieval precisions for queries.

A number of existing information retrieval systems propose the notion of query context to combine the knowledge of query and user into retrieval to reveal the most exact description of user's information needs. In this paper we interpret query context as a document consisting of sentences related to the current query. This kind of query context is used to re-estimate the relevance probabilities of top-ranked documents and then re-rank top-ranked documents. The experiments show that the proposed context-based approach for information retrieval can greatly improved relevance of search results.