Diagnostics de base, mise en oeuvre et appréciation du potentiel des NTIC (radio, internet, concertation) dans les zones enclavées de la région de Daraina (sous-préfécture de Vohémar, Nord-Est Madagascar)

Rapport d'avancement - février 2005

Postmortem diagnostics using MSCT and MRI of a lethal streptococcus group A infection at infancy: a case report.

Postmortem cross-sectional imaging using multislice computed tomography (MSCT) and magnetic resonance imaging (MRI) was considered as a base for a minimal invasive postmortem investigation in forensic medicine such as within the Virtopsy approach. We present the case of a 3-year-old girl with a lethal streptococcus group A infection and the findings of postmortem imaging in this kind of natural death. Postmortem MSCT and MRI revealed an edematous occlusion of the larynx at the level of the vocal cords, severe pneumonia with atelectatic parts of both upper lobes and complete atelectasis of both lower lobes, purulent fluid-filled right main bronchus, enlargement of cervical lymph nodes and pharyngeal tonsils, and additionally, a remaining glossopharyngeal cyst as well as an ureter fissus of the right kidney. All relevant autopsy findings could be obtained and visualized by postmortem imaging and confirmed by histological and microbiological investigations supporting the idea of a minimal invasive autopsy technique.

Automatic multi-resolution shape modeling of multi-organ structures.

Point Distribution Models (PDM) are among the most popular shape description techniques and their usefulness has been demonstrated in a wide variety of medical imaging applications. However, to adequately characterize the underlying modeled population it is essential to have a representative number of training samples, which is not always possible. This problem is especially relevant as the complexity of the modeled structure increases, being the modeling of ensembles of multiple 3D organs one of the most challenging cases. In this paper, we introduce a new GEneralized Multi-resolution PDM (GEM-PDM) in the context of multi-organ analysis able to efficiently characterize the different inter-object relations, as well as the particular locality of each object separately. Importantly, unlike previous approaches, the configuration of the algorithm is automated thanks to a new agglomerative landmark clustering method proposed here, which equally allows us to identify smaller anatomically significant regions within organs. The significant advantage of the GEM-PDM method over two previous approaches (PDM and hierarchical PDM) in terms of shape modeling accuracy and robustness to noise, has been successfully verified for two different databases of sets of multiple organs: six subcortical brain structures, and seven abdominal organs. Finally, we propose the integration of the new shape modeling framework into an active shape-model-based segmentation algorithm. The resulting algorithm, named GEMA, provides a better overall performance than the two classical approaches tested, ASM, and hierarchical ASM, when applied to the segmentation of 3D brain MRI.

Development of a method for fast and automatic radiocarbon measurement of aerosol samples by online coupling of an elemental analyzer with a MICADAS AMS

A fast and automatic method for radiocarbon analysis of aerosol samples is presented. This type of analysis requires high number of sample measurements of low carbon masses, but accepts precisions lower than for carbon dating analysis. The method is based on online Trapping CO2 and coupling an elemental analyzer with a MICADAS AMS by means of a gas interface. It gives similar results to a previously validated reference method for the same set of samples. This method is fast and automatic and typically provides uncertainties of 1.5–5% for representative aerosol samples. It proves to be robust and reliable and allows for overnight and unattended measurements. A constant and cross contamination correction is included, which indicates a constant contamination of 1.4 ± 0.2 μg C with 70 ± 7 pMC and a cross contamination of (0.2 ± 0.1)% from the previous sample. A Real-time online coupling version of the method was also investigated. It shows promising results for standard materials with slightly higher uncertainties than the Trapping online approach.

A New Approach for Automatic Removal of Movement Artifacts in Near-Infrared Spectroscopy Time Series by Means of Acceleration Data

Abstract: Near-infrared spectroscopy (NIRS) enables the non-invasive measurement of changes in hemodynamics and oxygenation in tissue. Changes in light-coupling due to movement of the subject can cause movement artifacts (MAs) in the recorded signals. Several methods have been developed so far that facilitate the detection and reduction of MAs in the data. However, due to fixed parameter values (e.g., global threshold) none of these methods are perfectly suitable for long-term (i.e., hours) recordings or were not time-effective when applied to large datasets. We aimed to overcome these limitations by automation, i.e., data adaptive thresholding specifically designed for long-term measurements, and by introducing a stable long-term signal reconstruction. Our new technique (“acceleration-based movement artifact reduction algorithm”, AMARA) is based on combining two methods: the “movement artifact reduction algorithm” (MARA, Scholkmann et al. Phys. Meas. 2010, 31, 649–662), and the “accelerometer-based motion artifact removal” (ABAMAR, Virtanen et al. J. Biomed. Opt. 2011, 16, 087005). We describe AMARA in detail and report about successful validation of the algorithm using empirical NIRS data, measured over the prefrontal cortex in adolescents during sleep. In addition, we compared the performance of AMARA to that of MARA and ABAMAR based on validation data.

Fully automatic GBM segmentation in the TCGA-GBM dataset: Prognosis and correlation with VASARI features.

Reproducible definition and quantification of imaging biomarkers is essential. We evaluated a fully automatic MR-based segmentation method by comparing it to manually defined sub-volumes by experienced radiologists in the TCGA-GBM dataset, in terms of sub-volume prognosis and association with VASARI features. MRI sets of 109 GBM patients were downloaded from the Cancer Imaging archive. GBM sub-compartments were defined manually and automatically using the Brain Tumor Image Analysis (BraTumIA). Spearman's correlation was used to evaluate the agreement with VASARI features. Prognostic significance was assessed using the C-index. Auto-segmented sub-volumes showed moderate to high agreement with manually delineated volumes (range (r): 0.4 - 0.86). Also, the auto and manual volumes showed similar correlation with VASARI features (auto r = 0.35, 0.43 and 0.36; manual r = 0.17, 0.67, 0.41, for contrast-enhancing, necrosis and edema, respectively). The auto-segmented contrast-enhancing volume and post-contrast abnormal volume showed the highest AUC (0.66, CI: 0.55-0.77 and 0.65, CI: 0.54-0.76), comparable to manually defined volumes (0.64, CI: 0.53-0.75 and 0.63, CI: 0.52-0.74, respectively). BraTumIA and manual tumor sub-compartments showed comparable performance in terms of prognosis and correlation with VASARI features. This method can enable more reproducible definition and quantification of imaging based biomarkers and has potential in high-throughput medical imaging research.

Fully Automatic Segmentation of Hip CT Images

Automatic segmentation of the hip joint with pelvis and proximal femur surfaces from CT images is essential for orthopedic diagnosis and surgery. It remains challenging due to the narrowness of hip joint space, where the adjacent surfaces of acetabulum and femoral head are hardly distinguished from each other. This chapter presents a fully automatic method to segment pelvic and proximal femoral surfaces from hip CT images. A coarse-to-fine strategy was proposed to combine multi-atlas segmentation with graph-based surface detection. The multi-atlas segmentation step seeks to coarsely extract the entire hip joint region. It uses automatically detected anatomical landmarks to initialize and select the atlas and accelerate the segmentation. The graph based surface detection is to refine the coarsely segmented hip joint region. It aims at completely and efficiently separate the adjacent surfaces of the acetabulum and the femoral head while preserving the hip joint structure. The proposed strategy was evaluated on 30 hip CT images and provided an average accuracy of 0.55, 0.54, and 0.50 mm for segmenting the pelvis, the left and right proximal femurs, respectively.

Particle Accelerators and Detectors for medical Diagnostics and Therapy

This Habilitationsschrift (Habilitation thesis) is focused on my research activities on medical applications of particle physics and was written in 2013 to obtain the Venia Docendi (Habilitation) in experimental physics at the University of Bern. It is based on selected publications, which represented at that time my major scientific contributions as an experimental physicist to the field of particle accelerators and detectors applied to medical diagnostics and therapy. The thesis is structured in two parts. In Part I, Chapter 1 presents an introduction to accelerators and detectors applied to medicine, with particular focus on cancer hadrontherapy and on the production of radioactive isotopes. In Chapter 2, my publications on medical particle accelerators are introduced and put into their perspective. In particular, high frequency linear accelerators for hadrontherapy are discussed together with the new Bern cyclotron laboratory. Chapter 3 is dedicated to particle detectors with particular emphasis on three instruments I contributed to propose and develop: segmented ionization chambers for hadrontherapy, a proton radiography apparatus with nuclear emulsion films, and a beam monitor detector for ion beams based on doped silica fibres. Selected research and review papers are contained in Part II. For copyright reasons, they are only listed and not reprinted in this on-line version. They are available on the websites of the journals.

First international external quality assessment of molecular diagnostics for Mers-CoV

BACKGROUND: Since the discovery of Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012, diagnostic protocols were quickly published and deployed globally. OBJECTIVES: We set out to assess the quality of MERS-CoV molecular diagnostics worldwide. STUDY DESIGN: Both sensitivity and specificity were assessed using 12 samples containing different viral loads of MERS-CoV or common coronaviruses (OC43, 229E, NL63, HKU1). RESULTS: The panel was sent to more than 106 participants, of which 99 laboratories from 6 continents returned 189 panel results.Scores ranged from 100% (84 laboratories) to 33% (1 laboratory). 15% of respondents reported quantitative results, 61% semi-quantitative (Ct-values or time to positivity) and 24% reported qualitative results. The major specific technique used was real-time RT-PCR using the WHO recommended targets upE, ORF1a and ORF1b. The evaluation confirmed that RT-PCRs targeting the ORF1b are less sensitive, and therefore not advised for primary diagnostics. CONCLUSIONS: The first external quality assessment MERS-CoV panel gives a good insight in molecular diagnostic techniques and their performances for sensitive and specific detection of MERS-CoV RNA globally. Overall, all laboratories were capable of detecting MERS-CoV with some differences in sensitivity. The observation that 8% of laboratories reported false MERS-CoV positive single assay results shows room for improvement, and the importance of using confirmatory targets.

Automatic identification of discourse markers in dialogues: An in-depth study of like and well

The lexical items like and well can serve as discourse markers (DMs), but can also play numerous other roles, such as verb or adverb. Identifying the occurrences that function as DMs is an important step for language understanding by computers. In this study, automatic classifiers using lexical, prosodic/positional and sociolinguistic features are trained over transcribed dialogues, manually annotated with DM information. The resulting classifiers improve state-of-the-art performance of DM identification, at about 90% recall and 79% precision for like (84.5% accuracy, κ = 0.69), and 99% recall and 98% precision for well (97.5% accuracy, κ = 0.88). Automatic feature analysis shows that lexical collocations are the most reliable indicators, followed by prosodic/positional features, while sociolinguistic features are marginally useful for the identification of DM like and not useful for well. The differentiated processing of each type of DM improves classification accuracy, suggesting that these types should be treated individually.

Towards Automatic Identification of Discourse Markers in Dialogs: the Case of 'Like'

This article discusses the detection of discourse markers (DM) in dialog transcriptions, by human annotators and by automated means. After a theoretical discussion of the definition of DMs and their relevance to natural language processing, we focus on the role of like as a DM. Results from experiments with human annotators show that detection of DMs is a difficult but reliable task, which requires prosodic information from soundtracks. Then, several types of features are defined for automatic disambiguation of like: collocations, part-of-speech tags and duration-based features. Decision-tree learning shows that for like, nearly 70% precision can be reached, with near 100% recall, mainly using collocation filters. Similar results hold for well, with about 91% precision at 100% recall.