Automatic Liver Segmentation Using an Adversarial Image-to-Image Network

Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment. However, it is still a very challenging task due to the complex background, fuzzy boundary, and various appearance of liver. In this paper, we propose an automatic and efficient algorithm to segment liver from 3D CT volumes. A deep image-to-image network (DI2IN) is first deployed to generate the liver segmentation, employing a convolutional encoder-decoder architecture combined with multi-level feature concatenation and deep supervision. Then an adversarial network is utilized during training process to discriminate the output of DI2IN from ground truth, which further boosts the performance of DI2IN. The proposed method is trained on an annotated dataset of 1000 CT volumes with various different scanning protocols (e.g., contrast and non-contrast, various resolution and position) and large variations in populations (e.g., ages and pathology). Our approach outperforms the state-of-the-art solutions in terms of segmentation accuracy and computing efficiency.Comment: Accepted by MICCAI 201

Dielectric resonances of ordered passive arrays

The electrical and optical properties of ordered passive arrays, constituted of inductive and capacitive components, are usually deduced from Kirchhoff's rules. Under the assumption of periodic boundary conditions, comparable results may be obtained via an approach employing transfer matrices. In particular, resonances in the dielectric spectrum are demonstrated to occur if all eigenvalues of the transfer matrix of the entire array are unity. The latter condition, which is shown to be equivalent to the habitual definition of a resonance in impedance for an array between electrodes, allows for a convenient and accurate determination of the resonance frequencies, and may thus be used as a tool for the design of materials with a specific dielectric response. For the opposite case of linear arrays in a large network, where periodic boundary condition do not apply, several asymptotic properties are derived. Throughout the article, the derived analytic results are compared to numerical models, based on either Exact Numerical Renormalisation or the spectral method.Comment: 12 pages, 12 figure

Resolving the wave-vector in negative refractive media: The sign of Z\sqrt{Z}

We address the general issue of resolving the wave-vector in complex electromagnetic media including negative refractive media. This requires us to make a physical choice for the sign of a square-root imposed merely by conditions of causality. By considering the analytic behaviour of the wave-vector in the complex plane, it is shown that there are a total of eight physically distinct cases in the four quadrants of two Riemann sheets.Comment: 3 pages, 2 figures, RevTe

3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes

While deep convolutional neural networks (CNN) have been successfully applied for 2D image analysis, it is still challenging to apply them to 3D anisotropic volumes, especially when the within-slice resolution is much higher than the between-slice resolution and when the amount of 3D volumes is relatively small. On one hand, direct learning of CNN with 3D convolution kernels suffers from the lack of data and likely ends up with poor generalization; insufficient GPU memory limits the model size or representational power. On the other hand, applying 2D CNN with generalizable features to 2D slices ignores between-slice information. Coupling 2D network with LSTM to further handle the between-slice information is not optimal due to the difficulty in LSTM learning. To overcome the above challenges, we propose a 3D Anisotropic Hybrid Network (AH-Net) that transfers convolutional features learned from 2D images to 3D anisotropic volumes. Such a transfer inherits the desired strong generalization capability for within-slice information while naturally exploiting between-slice information for more effective modelling. The focal loss is further utilized for more effective end-to-end learning. We experiment with the proposed 3D AH-Net on two different medical image analysis tasks, namely lesion detection from a Digital Breast Tomosynthesis volume, and liver and liver tumor segmentation from a Computed Tomography volume and obtain the state-of-the-art results

Life history parameters in acellular extrinsic fiber cementum microstructure

Life-history parameters such as pregnancies, skeletal trauma, and renal disease have previously been identified from hypomineralized growth layers (incremental lines) of acellular extrinsic fiber cementum (AEFC). The precise periodicity of these growth layers remains vaguely approximated, so causal life-history explanations using tooth cementum cannot yet be rigorously calculated or tested. On the other hand, we show how life history parameters in AEFC can be identified by two contrasting elemental detection methods. Based on our results we reject the possibility of accurate estimation of pregnancies and other life history parameters from cementum using scanning electron microscopy alone. Here, we propose a new methodological approach for cementum research, Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS), to measure degree and distribution of mineralization of cementum growth layers. Our results show that Tof-SIMS can significantly increase our knowledge of cementum composition and is therefore a powerful new tool for life history researchers

A spherical perfect lens

It has been recently proved that a slab of negative refractive index material acts as a perfect lens in that it makes accessible the sub-wavelength image information contained in the evanescent modes of a source. Here we elaborate on perfect lens solutions to spherical shells of negative refractive material where magnification of the near-field images becomes possible. The negative refractive materials then need to be spatially dispersive with $\epsilon(r) \sim 1/r$ and $\mu(r)\sim 1/r$. We concentrate on lens-like solutions for the extreme near-field limit. Then the conditions for the TM and TE polarized modes become independent of $\mu$ and $\epsilon$ respectively.Comment: Revtex4, 9 pages, 2 figures (eps

Temperature range of superconducting fluctuations above T_c in YBa_2Cu_3O_{7-\delta} single crystals

Microwave absorption measurements in magnetic fields from zero up to 16 T were used to determine the temperature range of superconducting fluctuations above the superconducting critical temperature T_c in YBa_2Cu_3O_{7-\delta}. Measurements were performed on deeply underdoped, slightly underdoped, and overdoped single crystals. The temperature range of the superconducting fluctuations above T_c is determined by an experimental method which is free from arbitrary assumptions about subtracting the nonsuperconducting contributions to the total measured signal, and/or theoretical models to extract the unknown parameters. The superconducting fluctuations are detected in the ab-plane, and c-axis conductivity, by identifying the onset temperature T'. Within the sensitivity of the method, this fluctuation regime is found only within a fairly narrow region above T_c. Its width increases from 7 K in the overdoped sample (T_c = 89 K), to at most 23 K in the deeply underdoped sample (T_c = 57 K), so that T' falls well below the pseudogap temperature T*. Implications of these findings are discussed in the context of other experimental probes of superconducting fluctuations in the cuprates