Deep Neural Network with l2-norm Unit for Brain Lesions Detection

Automated brain lesions detection is an important and very challenging clinical diagnostic task because the lesions have different sizes, shapes, contrasts, and locations. Deep Learning recently has shown promising progress in many application fields, which motivates us to apply this technology for such important problem. In this paper, we propose a novel and end-to-end trainable approach for brain lesions classification and detection by using deep Convolutional Neural Network (CNN). In order to investigate the applicability, we applied our approach on several brain diseases including high and low-grade glioma tumor, ischemic stroke, Alzheimer diseases, by which the brain Magnetic Resonance Images (MRI) have been applied as an input for the analysis. We proposed a new operating unit which receives features from several projections of a subset units of the bottom layer and computes a normalized l2-norm for next layer. We evaluated the proposed approach on two different CNN architectures and number of popular benchmark datasets. The experimental results demonstrate the superior ability of the proposed approach.Comment: Accepted for presentation in ICONIP-201

HeMIS: Hetero-Modal Image Segmentation

We introduce a deep learning image segmentation framework that is extremely robust to missing imaging modalities. Instead of attempting to impute or synthesize missing data, the proposed approach learns, for each modality, an embedding of the input image into a single latent vector space for which arithmetic operations (such as taking the mean) are well defined. Points in that space, which are averaged over modalities available at inference time, can then be further processed to yield the desired segmentation. As such, any combinatorial subset of available modalities can be provided as input, without having to learn a combinatorial number of imputation models. Evaluated on two neurological MRI datasets (brain tumors and MS lesions), the approach yields state-of-the-art segmentation results when provided with all modalities; moreover, its performance degrades remarkably gracefully when modalities are removed, significantly more so than alternative mean-filling or other synthesis approaches.Comment: Accepted as an oral presentation at MICCAI 201

Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction

Deep learning for regression tasks on medical imaging data has shown promising results. However, compared to other approaches, their power is strongly linked to the dataset size. In this study, we evaluate 3D-convolutional neural networks (CNNs) and classical regression methods with hand-crafted features for survival time regression of patients with high grade brain tumors. The tested CNNs for regression showed promising but unstable results. The best performing deep learning approach reached an accuracy of 51.5% on held-out samples of the training set. All tested deep learning experiments were outperformed by a Support Vector Classifier (SVC) using 30 radiomic features. The investigated features included intensity, shape, location and deep features. The submitted method to the BraTS 2018 survival prediction challenge is an ensemble of SVCs, which reached a cross-validated accuracy of 72.2% on the BraTS 2018 training set, 57.1% on the validation set, and 42.9% on the testing set. The results suggest that more training data is necessary for a stable performance of a CNN model for direct regression from magnetic resonance images, and that non-imaging clinical patient information is crucial along with imaging information.Comment: Contribution to The International Multimodal Brain Tumor Segmentation (BraTS) Challenge 2018, survival prediction tas

Modification of the ω\omega-Meson Lifetime in Nuclear Matter

The photo production of $\omega$ mesons on the nuclei C, Ca, Nb and Pb has been measured using the Crystal Barrel/TAPS detector at the ELSA tagged photon facility in Bonn. The dependence of the $\omega$ meson cross section on the nuclear mass number has been compared with three different types of models, a Glauber analysis, a BUU analysis of the Giessen theory group and a calculation by the Valencia theory group. In all three cases, the inelastic $\omega$ width is found to be $130-150 \rm{MeV/c^2}$ at normal nuclear matter density for an average 3-momentum of 1.1 GeV/c. In the restframe of the $\omega$ meson, this inelastic $\omega$ width corresponds to a reduction of the $\omega$ lifetime by a factor $\approx 30$. For the first time, the momentum dependent $\omega$N cross section has been extracted from the experiment and is in the range of 70 mb.Comment: 5 pages, 4 figure

Photoproduction of pi0 omega off protons for E(gamma) < 3 GeV

Differential and total cross-sections for photoproduction of gamma proton to proton pi0 omega and gamma proton to Delta+ omega were determined from measurements of the CB-ELSA experiment, performed at the electron accelerator ELSA in Bonn. The measurements covered the photon energy range from the production threshold up to 3GeV.Comment: 8 pages, 13 figure

In-medium ω\omega mass from the γ+Nb→π0γ+X\gamma + Nb \to \pi^{0}\gamma + X reaction

Data on the photoproduction of $\omega$ mesons on nuclei have been re-analyzed in a search for in-medium modifications. The data were taken with the Crystal Barrel(CB)/TAPS detector system at the ELSA accelerator facility in Bonn. First results from the analysis of the data set were published by D. Trnka et al. in Phys. Rev. Lett 94 (2005) 192303 \cite{david}, claiming a lowering of the $\omega$ mass in the nuclear medium by 14$$ at normal nuclear matter density. The extracted $\omega$ line shape was found to be sensitive to the background subtraction. For this reason a re-analysis of the same data set has been initiated and a new method has been developed to reduce the background and to determine the shape and absolute magnitude of the background directly from the data. Details of the re-analysis and of the background determination are described. The $\omega$ signal on the $Nb$ target, extracted in the re-analysis, does not show a deviation from the corresponding line shape on a $LH_2$ target, measured as reference. The earlier claim of an in-medium mass shift is thus not confirmed. The sensitivity of the $\omega$ line shape to different in-medium modification scenarios is discussed.Comment: 13 pages and 11 figures, submitted for publicatio

The helicity amplitudes A1/2_{1/2} and A3/2_{3/2} for the D13(1520)_{13}(1520) resonance obtained from the γ⃗p⃗→pπ0\vec{\gamma} \vec{p} \to p \pi^0 reaction}

The helicity dependence of the $\vec{\gamma} \vec{p} \to p \pi^0$ reaction has been measured for the first time in the photon energy range from 550 to 790 MeV. The experiment, performed at the Mainz microtron MAMI, used a 4$\pi$-detector system, a circularly polarized, tagged photon beam, and a longitudinally polarized frozen-spin target. These data are predominantly sensitive to the $D_{13}(1520)$ resonance and are used to determine its parameters.Comment: 5 pages, 4 figure

Quasi-free photoproduction of eta-mesons of the neutron

Quasi-free photoproduction of eta-mesons off nucleons bound in the deuteron has been measured with the CBELSA/TAPS detector for incident photon energies up to 2.5 GeV at the Bonn ELSA accelerator. The eta-mesons have been detected in coincidence with recoil protons and recoil neutrons, which allows a detailed comparison of the quasi-free n(gamma,eta)n and p(gamma,eta)p reactions. The excitation function for eta-production off the neutron shows a pronounced bump-like structure at W=1.68 GeV (E_g ~ 1 GeV), which is absent for the proton.Comment: accepted for publication in Phys. Rev. Let

The Suppressor of AAC2 Lethality SAL1 Modulates Sensitivity of Heterologously Expressed Artemia ADP/ATP Carrier to Bongkrekate in Yeast

The ADP/ATP carrier protein (AAC) expressed in Artemia franciscana is refractory to bongkrekate. We generated two strains of Saccharomyces cerevisiae where AAC1 and AAC3 were inactivated and the AAC2 isoform was replaced with Artemia AAC containing a hemagglutinin tag (ArAAC-HA). In one of the strains the suppressor of ΔAAC2 lethality, SAL1, was also inactivated but a plasmid coding for yeast AAC2 was included, because the ArAACΔsal1Δ strain was lethal. In both strains ArAAC-HA was expressed and correctly localized to the mitochondria. Peptide sequencing of ArAAC expressed in Artemia and that expressed in the modified yeasts revealed identical amino acid sequences. The isolated mitochondria from both modified strains developed 85% of the membrane potential attained by mitochondria of control strains, and addition of ADP yielded bongkrekate-sensitive depolarizations implying acquired sensitivity of ArAAC-mediated adenine nucleotide exchange to this poison, independent from SAL1. However, growth of ArAAC-expressing yeasts in glycerol-containing media was arrested by bongkrekate only in the presence of SAL1. We conclude that the mitochondrial environment of yeasts relying on respiratory growth conferred sensitivity of ArAAC to bongkrekate in a SAL1-dependent manner. © 2013 Wysocka-Kapcinska et al