Diagnostic performance on briefly presented digital pathology images

Background: Identifying new and more robust assessments of proficiency/expertise (finding new "biomarkers of expertise") in histopathology is desirable for many reasons. Advances in digital pathology permit new and innovative tests such as flash viewing tests and eye tracking and slide navigation analyses that would not be possible with a traditional microscope. The main purpose of this study was to examine the usefulness of time-restricted testing of expertise in histopathology using digital images.
Methods: 19 novices (undergraduate medical students), 18 intermediates (trainees), and 19 experts (consultants) were invited to give their opinion on 20 general histopathology cases after 1 s and 10 s viewing times. Differences in performance between groups were measured and the internal reliability of the test was calculated.
Results: There were highly significant differences in performance between the groups using the Fisher's least significant difference method for multiple comparisons. Differences between groups were consistently greater in the 10-s than the 1-s test. The Kuder-Richardson 20 internal reliability coefficients were very high for both tests: 0.905 for the 1-s test and 0.926 for the 10-s test. Consultants had levels of diagnostic accuracy of 72% at 1 s and 83% at 10 s.
Conclusions: Time-restricted tests using digital images have the potential to be extremely reliable tests of diagnostic proficiency in histopathology. A 10-s viewing test may be more reliable than a 1-s test. Over-reliance on "at a glance" diagnoses in histopathology is a potential source of medical error due to over-confidence bias and premature closure.

Tracking biological invasions in space and time: elucidating the invasive history of the green alga Codium fragile using old DNA

With the advent of 'ancient DNA' studies on preserved material of extant and extinct species, museums and herbaria now represent an important although still underutilized resource in molecular ecology. The ability to obtain sequence data from archived specimens can reveal the recent history of cryptic species and introductions. We have analysed extant and herbarium samples of the highly invasive green alga Codium fragile, many over 100 years old, to identify cryptic accessions of the invasive strain known as C. fragile ssp. tomentosoides, which can be identified by a unique haplotype. Molecular characterization of specimens previously identified as native in various regions shows that the invasive tomentosoides strain has been colonizing new habitats across the world for longer than records indicate, in some cases nearly 100 years before it was noticed. It can now be found in the ranges of all the other native haplotypes detected, several of which correspond to recognized subspecies. Within regions in the southern hemisphere there was a greater diversity of haplotypes than in the northern hemisphere, probably as a result of dispersal by the Antarctic Circumpolar Current. The findings of this study highlight the importance of herbaria in preserving contemporaneous records of invasions as they occur, especially when invasive taxa are cryptic.

Target Detection and Tracking With Heterogeneous Sensors

We present a multimodal detection and tracking algorithm for sensors composed of a camera mounted between two microphones. Target localization is performed on color-based change detection in the video modality and on time difference of arrival (TDOA) estimation between the two microphones in the audio modality. The TDOA is computed by multiband generalized cross correlation (GCC) analysis. The estimated directions of arrival are then postprocessed using a Riccati Kalman filter. The visual and audio estimates are finally integrated, at the likelihood level, into a particle filter (PF) that uses a zero-order motion model, and a weighted probabilistic data association (WPDA) scheme. We demonstrate that the Kalman filtering (KF) improves the accuracy of the audio source localization and that the WPDA helps to enhance the tracking performance of sensor fusion in reverberant scenarios. The combination of multiband GCC, KF, and WPDA within the particle filtering framework improves the performance of the algorithm in noisy scenarios. We also show how the proposed audiovisual tracker summarizes the observed scene by generating metadata that can be transmitted to other network nodes instead of transmitting the raw images and can be used for very low bit rate communication. Moreover, the generated metadata can also be used to detect and monitor events of interest.

Object tracking using SIFT features and mean shift

A scale invariant feature transform (SIFT) based mean shift algorithm is presented for object tracking in real scenarios. SIFT features are used to correspond the region of interests across frames. Meanwhile, mean shift is applied to conduct similarity search via color histograms. The probability distributions from these two measurements are evaluated in an expectation–maximization scheme so as to achieve maximum likelihood estimation of similar regions. This mutual support mechanism can lead to consistent tracking performance if one of the two measurements becomes unstable. Experimental work demonstrates that the proposed mean shift/SIFT strategy improves the tracking performance of the classical mean shift and SIFT tracking algorithms in complicated real scenarios.

Automated detection and tracking of solar magnetic bright points

Magnetic bright points (MBPs) in the internetwork are among the smallest objects in the solar photosphere and appear bright against the ambient environment. An algorithm is presented that can be used for the automated detection of the MBPs in the spatial and temporal domains. The algorithm works by mapping the lanes through intensity thresholding. A compass search, combined with a study of the intensity gradient across the detected objects, allows the disentanglement of MBPs from bright pixels within the granules. Object growing is implemented to account for any pixels that might have been removed when mapping the lanes. The images are stabilized by locating long-lived objects that may have been missed due to variable light levels and seeing quality. Tests of the algorithm, employing data taken with the Swedish Solar Telescope, reveal that approximate to 90 per cent of MBPs within a 75 x 75 arcsec(2) field of view are detected.

Convergence and tracking analysis of a variable normalised LMF (XE-NLMF) algorithm

The least-mean-fourth (LMF) algorithm is known for its fast convergence and lower steady state error, especially in sub-Gaussian noise environments. Recent work on normalised versions of the LMF algorithm has further enhanced its stability and performance in both Gaussian and sub-Gaussian noise environments. For example, the recently developed normalised LMF (XE-NLMF) algorithm is normalised by the mixed signal and error powers, and weighted by a fixed mixed-power parameter. Unfortunately, this algorithm depends on the selection of this mixing parameter. In this work, a time-varying mixed-power parameter technique is introduced to overcome this dependency. A convergence analysis, transient analysis, and steady-state behaviour of the proposed algorithm are derived and verified through simulations. An enhancement in performance is obtained through the use of this technique in two different scenarios. Moreover, the tracking analysis of the proposed algorithm is carried out in the presence of two sources of nonstationarities: (1) carrier frequency offset between transmitter and receiver and (2) random variations in the environment. Close agreement between analysis and simulation results is obtained. The results show that, unlike in the stationary case, the steady-state excess mean-square error is not a monotonically increasing function of the step size. (c) 2007 Elsevier B.V. All rights reserved.