Entropy-based particle correspondence for shape populations

Statistical shape analysis of anatomical structures plays an important role in many medical image analysis applications such as understanding the structural changes in anatomy in various stages of growth or disease. Establishing accurate correspondence across object populations is essential for such statistical shape analysis studies

Evaluation of current algorithms for segmentation of scar tissue from late Gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge

Background: Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the left atrium (LA) myocardium. This can be important for treatment stratification of patients with atrial fibrillation (AF) and for assessment of treatment after radio frequency catheter ablation (RFCA). In this paper we present a standardised evaluation benchmarking framework for algorithms segmenting fibrosis and scar from LGE CMR images. The algorithms reported are the response to an open challenge that was put to the medical imaging community through an ISBI (IEEE International Symposium on Biomedical Imaging) workshop. Methods: The image database consisted of 60 multicenter, multivendor LGE CMR image datasets from patients with AF, with 30 images taken before and 30 after RFCA for the treatment of AF. A reference standard for scar and fibrosis was established by merging manual segmentations from three observers. Furthermore, scar was also quantified using 2, 3 and 4 standard deviations (SD) and full-width-at-half-maximum (FWHM) methods. Seven institutions responded to the challenge: Imperial College (IC), Mevis Fraunhofer (MV), Sunnybrook Health Sciences (SY), Harvard/Boston University (HB), Yale School of Medicine (YL), King’s College London (KCL) and Utah CARMA (UTA, UTB). There were 8 different algorithms evaluated in this study. Results: Some algorithms were able to perform significantly better than SD and FWHM methods in both pre- and post-ablation imaging. Segmentation in pre-ablation images was challenging and good correlation with the reference standard was found in post-ablation images. Overlap scores (out of 100) with the reference standard were as follows: Pre: IC = 37, MV = 22, SY = 17, YL = 48, KCL = 30, UTA = 42, UTB = 45; Post: IC = 76, MV = 85, SY = 73, HB = 76, YL = 84, KCL = 78, UTA = 78, UTB = 72. Conclusions: The study concludes that currently no algorithm is deemed clearly better than others. There is scope for further algorithmic developments in LA fibrosis and scar quantification from LGE CMR images. Benchmarking of future scar segmentation algorithms is thus important. The proposed benchmarking framework is made available as open-source and new participants can evaluate their algorithms via a web-based interface

Robust and efficient data management for a distributed hash table

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 59-64).by Josh Cates.M.Eng

Mondrian Memory Protection

Mondrian memory protection (MMP) is a fine-grained protection scheme that allows multiple protection domains to flexibly share memory and export protected services. In contrast to earlier pagebased systems, MMP allows arbitrary permissions control at the granularity of individual words. We use a compressed permissions table to reduce space overheads and employ two levels of permissions caching to reduce run-time overheads. The protection tables in our implementation add less than 9% overhead to the memory space used by the application. Accessing the protection tables adds less than 8% additional memory references to the accesses made by the application. Although it can be layered on top of demandpaged virtual memory, MMP is also well-suited to embedded systems with a single physical address space. We extend MMP to support segment translation which allows a memory segment to appear at another location in the address space. We use this translation to implement zero-copy networking underneath the standard read system call interface, where packet payload fragments are connected together by the translation system to avoid data copying. This saves 52% of the memory references used by a traditional copying network stack

Introducing Tarzan, a Peer-to-Peer Anonymizing Network Layer

We introduce Tarzan, a peer-to-peer anonymous network layer that provides generic IP forwarding. Unlike prior anonymizing layers, Tarzan is flexible, transparent, decentralized, and highly scalable. Tarzan achieves these properties by building anonymous IP tunnels between an open-ended set of peers. Tarzan can provide anonymity to existing applications, such as web browsing and file sharing, without change to those applications. Performance tests show that Tarzan imposes minimal overhead over a corresponding non-anonymous overlay route