Research Partnerships for Addressing Global Challenges: Opening Speech

Humankind today is challenged by numerous threats brought about by global change. Climate has been and is being modified by human activities, which calls for mitigation and adaptation measures at an unprecedented scale. Natural resources have been degraded by human development by means of land cover and land use changes, for which protective and restoration measures have to be taken by land users and governments in most countries of the North and South. Low levels of economic development and insufficient policies in most developing countries have led to widespread poverty, which affects nearly half of the world’s population and directly threatens almost one billion people. Finally, uncontrolled economic growth has increased disparities between and within populations and has led to widespread environmental problems in many nations. Generating and sharing knowledge is a key to addressing such global challenges. Knowledge can be used to develop the best solutions and to avoid or repair threats. Research partnerships have proven to be suitable means to bridge the divides and disparities between knowledge societies and developing countries, thereby reducing gaps. Research partnerships are tools for further capacity development and thereby lead to societal empowerment. Institutional settings allowing for research partnerships are needed both in the North and the South, so that the different networks can work together in a long-term enabling environment.

Space–oriented EEG segmentation reveals changes in brain electric–field maps under the influence of a nootropic drug

Map landscape-based segmentation of the sequences of momentary potential distribution maps (42-channel recordings) into brain microstates during spontaneous brain activity was used to study brain electric field spatial effects of single doses of piracetam (2.9, 4.8, and 9.6 g Nootropil® UCB and placebo) in a double-blind study of five normal young volunteers. Four 15-second epochs were analyzed from each subject and drug condition. The most prominent class of microstates (covering 49% of the time) consisted of potential maps with a generally anterior-posterior field orientation. The map orientation of this microstate class showed an increasing clockwise deviation from the placebo condition with increasing drug doses (Fisher's probability product, p < 0.014). The results of this study suggest the use of microstate segmentation analysis for the assessment of central effects of medication in spontaneous multichannel electroencephalographic data, as a complementary approach to frequency-domain analysis.

Influence of directionality and maximal power output on speech understanding with bone anchored hearing implants in single sided deafness

Bone-anchored hearing implants (BAHI) are routinely used to alleviate the effects of the acoustic head shadow in single-sided sensorineural deafness (SSD). In this study, the influence of the directional microphone setting and the maximum power output of the BAHI sound processor on speech understanding in noise in a laboratory setting were investigated. Eight adult BAHI users with SSD participated in this pilot study. Speech understanding in noise was measured using a new Slovak speech-in-noise test in two different spatial settings, either with noise coming from the front and noise from the side of the BAHI (S90N0) or vice versa (S0N90). In both spatial settings, speech understanding was measured without a BAHI, with a Baha BP100 in omnidirectional mode, with a BP100 in directional mode, with a BP110 power in omnidirectional and with a BP110 power in directional mode. In spatial setting S90N0, speech understanding in noise with either sound processor and in either directional mode was improved by 2.2-2.8 dB (p = 0.004-0.016). In spatial setting S0N90, speech understanding in noise was reduced by either BAHI, but was significantly better by 1.0-1.8 dB, if the directional microphone system was activated (p = 0.046), when compared to the omnidirectional setting. With the limited number of subjects in this study, no statistically significant differences were found between the two sound processors.

Internet video telephony allows speech reading by deaf individuals and improves speech perception by cochlear implant users

OBJECTIVE To analyze speech reading through Internet video calls by profoundly hearing-impaired individuals and cochlear implant (CI) users. METHODS Speech reading skills of 14 deaf adults and 21 CI users were assessed using the Hochmair Schulz Moser (HSM) sentence test. We presented video simulations using different video resolutions (1280 × 720, 640 × 480, 320 × 240, 160 × 120 px), frame rates (30, 20, 10, 7, 5 frames per second (fps)), speech velocities (three different speakers), webcameras (Logitech Pro9000, C600 and C500) and image/sound delays (0-500 ms). All video simulations were presented with and without sound and in two screen sizes. Additionally, scores for live Skype™ video connection and live face-to-face communication were assessed. RESULTS Higher frame rate (>7 fps), higher camera resolution (>640 × 480 px) and shorter picture/sound delay (<100 ms) were associated with increased speech perception scores. Scores were strongly dependent on the speaker but were not influenced by physical properties of the camera optics or the full screen mode. There is a significant median gain of +8.5%pts (p = 0.009) in speech perception for all 21 CI-users if visual cues are additionally shown. CI users with poor open set speech perception scores (n = 11) showed the greatest benefit under combined audio-visual presentation (median speech perception +11.8%pts, p = 0.032). CONCLUSION Webcameras have the potential to improve telecommunication of hearing-impaired individuals.

Hierarchical segmentation-assisted multimodal registration for MR brain images

Information theory-based metric such as mutual information (MI) is widely used as similarity measurement for multimodal registration. Nevertheless, this metric may lead to matching ambiguity for non-rigid registration. Moreover, maximization of MI alone does not necessarily produce an optimal solution. In this paper, we propose a segmentation-assisted similarity metric based on point-wise mutual information (PMI). This similarity metric, termed SPMI, enhances the registration accuracy by considering tissue classification probabilities as prior information, which is generated from an expectation maximization (EM) algorithm. Diffeomorphic demons is then adopted as the registration model and is optimized in a hierarchical framework (H-SPMI) based on different levels of anatomical structure as prior knowledge. The proposed method is evaluated using Brainweb synthetic data and clinical fMRI images. Both qualitative and quantitative assessment were performed as well as a sensitivity analysis to the segmentation error. Compared to the pure intensity-based approaches which only maximize mutual information, we show that the proposed algorithm provides significantly better accuracy on both synthetic and clinical data.

Integrated Segmentation of Brain Tumor Images for Radiotherapy and Neurosurgery

Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multi-scale, multi-physics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlasbased segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.

Fully automatic X-ray image segmentation via joint estimation of image displacements.

We propose a new method for fully-automatic landmark detection and shape segmentation in X-ray images. Our algorithm works by estimating the displacements from image patches to the (unknown) landmark positions and then integrating them via voting. The fundamental contribution is that, we jointly estimate the displacements from all patches to multiple landmarks together, by considering not only the training data but also geometric constraints on the test image. The various constraints constitute a convex objective function that can be solved efficiently. Validated on three challenging datasets, our method achieves high accuracy in landmark detection, and, combined with statistical shape model, gives a better performance in shape segmentation compared to the state-of-the-art methods.

Statistical model-based segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs.

PURPOSE    Segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs is required to create a three-dimensional model of the hip joint for use in planning and treatment. However, manually extracting the femoral contour is tedious and prone to subjective bias, while automatic segmentation must accommodate poor image quality, anatomical structure overlap, and femur deformity. A new method was developed for femur segmentation in AP pelvic radiographs. METHODS    Using manual annotations on 100 AP pelvic radiographs, a statistical shape model (SSM) and a statistical appearance model (SAM) of the femur contour were constructed. The SSM and SAM were used to segment new AP pelvic radiographs with a three-stage approach. At initialization, the mean SSM model is coarsely registered to the femur in the AP radiograph through a scaled rigid registration. Mahalanobis distance defined on the SAM is employed as the search criteria for each annotated suggested landmark location. Dynamic programming was used to eliminate ambiguities. After all landmarks are assigned, a regularized non-rigid registration method deforms the current mean shape of SSM to produce a new segmentation of proximal femur. The second and third stages are iteratively executed to convergence. RESULTS    A set of 100 clinical AP pelvic radiographs (not used for training) were evaluated. The mean segmentation error was [Formula: see text], requiring [Formula: see text] s per case when implemented with Matlab. The influence of the initialization on segmentation results was tested by six clinicians, demonstrating no significance difference. CONCLUSIONS    A fast, robust and accurate method for femur segmentation in digital AP pelvic radiographs was developed by combining SSM and SAM with dynamic programming. This method can be extended to segmentation of other bony structures such as the pelvis.

Robust Proximal Femur Segmentation in Conventional X-Ray Images via Random Forest Regression on Multi-resolution Gradient Features

In this paper, we propose a fully automatic, robust approach for segmenting proximal femur in conventional X-ray images. Our method is based on hierarchical landmark detection by random forest regression, where the detection results of 22 global landmarks are used to do the spatial normalization, and the detection results of the 59 local landmarks serve as the image cue for instantiation of a statistical shape model of the proximal femur. To detect landmarks in both levels, we use multi-resolution HoG (Histogram of Oriented Gradients) as features which can achieve better accuracy and robustness. The efficacy of the present method is demonstrated by experiments conducted on 150 clinical x-ray images. It was found that the present method could achieve an average point-to-curve error of 2.0 mm and that the present method was robust to low image contrast, noise and occlusions caused by implants.