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.

Clinical and histological characterization of hair coat and glandular tissue of Chinese crested dogs.

BACKGROUND Two varieties exist in the Chinese crested dog breed, namely hairless Chinese crested dogs presenting with hypotrichosis and dentition abnormalities, and the coated powderpuffs. Hairless Chinese crested dogs are obligate heterozygotes for a FOXI3 mutation, and this phenotype is classified as a form of canine ectodermal dysplasia. OBJECTIVES We provide a detailed histological description of hair follicles and their density for the three subphenotypes (true hairless, semi-coated and powderpuffs) of Chinese crested dogs. Apocrine and exocrine glands of the skin and other tissues were compared with findings reported from dogs with X-linked ectodermal dysplasia. ANIMALS Skin biopsies were collected from 22 Chinese crested dogs. Additionally, the glands of the skin and other tissues were examined from another two dogs available for postmortem examination. METHODS Skin biopsies and tissues were processed, stained and evaluated in a blinded fashion. RESULTS Hair follicular anomalies decreased with increasing number of hairs in the different phenotypes. The FOXI3 mutants had only simple primary hair follicles, whereas the nonmutant powderpuffs had compound follicles identical to other dog breeds. All Chinese crested dogs had an anagen-dominated hair cycle. Furthermore, apocrine glands in the skin and respiratory mucous glands of the mutant Chinese crested dogs were present and normal. CONCLUSIONS AND CLINICAL IMPORTANCE We have identified striking histopathological differences between the three subphenotypes of Chinese crested dogs. We clearly demonstrated distinct differences between the canine ectodermal dysplasia in Chinese crested dogs and dogs with X-linked ectodermal dysplasia.

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.

Fully Automatic Segmentation of AP Pelvis X-rays via Random Forest Regression and Hierarchical Sparse Shape Composition

Knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic and robust approach for landmarking and segmentation of both pelvis and femur in a conventional AP X-ray. Our approach is based on random forest regression and hierarchical sparse shape composition. Experiments conducted on 436 clinical AP pelvis x-rays show that our approach achieves an average point-to-curve error around 1.3 mm for femur and 2.2 mm for pelvis, both with success rates around 98%. Compared to existing methods, our approach exhibits better performance in both the robustness and the accuracy.

A Hybrid Model for Multimodal Brain Tumor Segmentation

We present a fully automatic segmentation method for multi-modal brain tumor segmentation. The proposed generative-discriminative hybrid model generates initial tissue probabilities, which are used subsequently for enhancing the classi�cation and spatial regularization. The model has been evaluated on the BRATS2013 training set, which includes multimodal MRI images from patients with high- and low-grade gliomas. Our method is capable of segmenting the image into healthy (GM, WM, CSF) and pathological tissue (necrotic, enhancing and non-enhancing tumor, edema). We achieved state-of-the-art performance (Dice mean values of 0.69 and 0.8 for tumor subcompartments and complete tumor respectively) within a reasonable timeframe (4 to 15 minutes).

The Gender of Chinese Christianity: Evaluating a New Research Perspective

This paper will discuss how the gender perspective can be applied to the study of the Early Modern Jesuit China mission. I will show that the category of gender provides a promising research perspective on Sino-Western cultural exchange, for it brings to the forefront important aspects of social life in the “contact zone” of Chinese Christian communities. I will argue that through the intercultural contact initiated by the Jesuit China mission, gender roles started to shift slightly on both sides. On the one hand, the Jesuits adopted the Confucian ideal of the separation of the sexes (nannü zhi bie), building for example separate Churches for women, something unknown in Europe. On the other hand, Chinese Christians were urged to reconsider aspects of their traditional gender norms, when for instance some men left their concubines in order to become Christian. The paper will be divided in three parts. First, it will focus on the history of concepts and discuss what gender relations meant in the context of the Early Modern China mission. Then it will turn to the representation of female religiosity in Jesuit Annual Letters and show how the gender perspective can lead to a re-evaluation this source genre. Finally, it will reflect on how the gender perspective can give us fresh insights into well-known paradigms on Sino-Western relations, taking the accommodation paradigm as an example.

Prescriptions of Chinese medicinal herbs in Switzerland: the example of suan zao ren (Ziziphi Spinosae Semen)

Introduction According to the Swiss Health Survey 2007, 1.7% of the adult population use traditional Chinese medicine (including Chinese herbal medicine, but excluding acupuncture). In contrast to conventional drugs, that contain single chemically defined substances, prescriptions of Chinese herbs are mixtures of up to 40 ingredients (parts of plants, fungi, animal substances and minerals). Originally they were taken in the form of decoctions, but nowadays granules are more popular. Medium daily dosages of granules range between 8 to 12g. In a recent work we identified the most commonly used Chinese herbs (all ingredients are referred to as herbs for reasons of simplicity) and classical formulas (mixtures). Here we present a short overview and the example of suan zao ren (Ziziphi Spinosae Semen), which is used in the treatment of insomnia and anxiety and contains saponins that have been shown to increase sleep in animal studies. Material and Methods A random sample of 1,053 prescriptions was drawn from the database of Lian Chinaherb AG, Switzerland, and analysed according to the most frequently used individual herbs and classical formulas. Cluster analysis (Jaccard similarity coefficient, complete linkage method) was applied to identify common combinations of herbs. Results The most frequently used herbs were dang gui (Angelicae Sinensis Radix), fu ling (Poria), bai shao (Paeoniae Radix Alba), and gan cao (Glycyrrhizae Radix et Rhizoma); the most frequently used classical formulas were gui pi tang (Restore the Spleen Decoction) and xiao yao san (Rambling Powder). The average number of herbs per prescription was 12.0, and the average daily dosage of granules was 8.7g. 74.3% of the prescriptions were for female, 24.8% for male patients. Suan zao ren was present in 14.2% of all prescriptions. These prescriptions contained on average 13.7 herbs, and the daily dosage of granules was 8.9g. Suan zao ren was more frequently prescribed by practitioners of non-Asian than of Asian origin but equally often for female and male patients. Cluster analysis grouped suan zao ren with yuan zhi (Polygalae Radix), bai zi ren (Platycladi Semen), sheng di huang (Rehmanniae Radix) and dan shen (Salviae Miltiorrhizae Radix et Rhizoma). Discussion Prescriptions including suan zao ren contained on average slightly more herbs than other prescriptions. This might be due to the fact that two of the three most popular classical formulas with suan zao ren are composed of 13 and 12 herbs with the possibility of adding more ingredients when necessary. Cluster analysis resulted in the clustering of suan zao ren with other herbs of the classical formula tian wang bu xin dan (Emperor of Heaven’s Special Pill to Tonify the Heart), indicating the use of suan zao ren for the treatment of insomnia and irritability. Unfortunately, the diagnoses of the patients were unavailable and thus correlations between use of suan zao ren and diseases could not be analysed.