Generation of Multi-Lobe Chua Corsage Memristor and Its Neural Oscillation.

The Chua corsage memristor (CCM) is considered as one of the candidates for the realization of biological neuron models due to its rich neuromorphic behaviors. In this paper, a universal model for -lobe CCM memristor is proposed. Moreover, a novel small-signal equivalent circuit with one capacitor is derived based on the proposed model to determine the edge of chaos and obtain the zero-pole diagrams and analyze the frequency response and oscillation mechanism of the -lobe CCM system, which are discussed in detail.

Enhanced Terahertz Phase Retrieval Imaging by Unequal Spaced Measurement.

Terahertz lensless phase retrieval imaging is a promising technique for non-destructive inspection applications. In the conventional multiple-plane phase retrieval method, the convergence speed due to wave propagations and measures with equal interval distance is slow and leads to stagnation. To address this drawback, we propose a nonlinear unequal spaced measurement scheme in which the interval space between adjacent measurement planes is gradually increasing, it can significantly increase the diversity of the intensity with a smaller number of required images.

Attention-guided duplex adversarial U-net for pancreatic segmentation from computed tomography images.

Purpose: Segmenting the organs from computed tomography (CT) images is crucial to early diagnosis and treatment. Pancreas segmentation is especially challenging because the pancreas has a small volume and a large variation in shape.

Methods: To mitigate this issue, an attention-guided duplex adversarial U-Net (ADAU-Net) for pancreas segmentation is proposed in this work.

Multi-scale Selection and Multi-channel Fusion Model for Pancreas Segmentation Using Adversarial Deep Convolutional Nets.

Organ segmentation from existing imaging is vital to the medical image analysis and disease diagnosis. However, the boundary shapes and area sizes of the target region tend to be diverse and flexible. And the frequent applications of pooling operations in traditional segmentor result in the loss of spatial information which is advantageous to segmentation.

Accurate pancreas segmentation using multi-level pyramidal pooling residual U-Net with adversarial mechanism.

Background: A novel multi-level pyramidal pooling residual U-Net with adversarial mechanism was proposed for organ segmentation from medical imaging, and was conducted on the challenging NIH Pancreas-CT dataset.

Methods: The 82 pancreatic contrast-enhanced abdominal CT volumes were split via four-fold cross validation to test the model performance. In order to achieve accurate segmentation, we firstly involved residual learning into an adversarial U-Net to achieve a better gradient information flow for improving segmentation performance.

A Minimally Interactive Method for Labeling Respiratory Phases in Free-Breathing Thoracic Dynamic MRI for Constructing 4D Images.

Objective: Determination of end-expiration (EE) and end-inspiration (EI) time points in the respiratory cycle in free-breathing slice image acquisitions of the thorax is one key step needed for 4D image construction via dynamic magnetic resonance imaging. The purpose of this paper is to realize the automation of the labeling process.

Methods: The diaphragm is used as a surrogate for tracking respiratory motion and determining the state of breathing.

Microwave photonic radar for distance and velocity measurement based on optical mixing and compressive sensing.

We propose a microwave photonic compressive sensing radar for distance and velocity measurement. First, a de-chirped signal that carries distance or velocity information is extracted between the transmitted and received signals in the proposed system. Then it is mixed with a pseudo-random bit sequence in the optical domain using a Mach-Zehnder modulator.

Dual adversarial convolutional networks with multilevel cues for pancreatic segmentation.

Accurate organ segmentation is a relatively challenging subject in medical imaging, especially for the pancreas, whose morphological characteristics are subtle but variable. In this paper, a novel dual adversarial convolutional network with multilevel cues (DACN-MC) is proposed to segment the pancreas in computerized tomography (CT). DACN-MC first involves a duplex adversarial network using a conventional model for biomedical image segmentation, which ensures the veracity of the predicted probability volumes and ultimately enhances the quality of the obtained maps.

Automated measurement of hip-knee-ankle angle on the unilateral lower limb X-rays using deep learning.

Significant inherent extra-articular varus angulation is associated with abnormal postoperative hip-knee-ankle (HKA) angle. At present, HKA is manually measured by orthopedic surgeons and it increases the doctors' workload. To automatically determine HKA, a deep learning-based automated method for measuring HKA on the unilateral lower limb X-rays was developed and validated.

Pancreas segmentation based on an adversarial model under two-tier constraints.

Pancreas segmentation is vital for the effective diagnosis and treatment of diabetic or pancreatic diseases. However, the irregular shape and strong variability of the pancreas in medical images pose significant challenges to accurate segmentation. In this paper, we propose a novel segmentation algorithm that imposes two-tier constraints on a conventional network through adversarial learning, namely UDCGAN.

Organic Transistor Nonvolatile Memory with Three-Level Information Storage and Optical Detection Functions.

By the current processing technology, it is a challenge to obtain ultrahigh-density information storage in the conventional binary floating-gate-based organic field-effect transistor (FG-OFET) nonvolatile memories (NVMs). To develop a multilevel memory in one cell is a feasible solution. In this work, we demonstrate FG-OFET NVMs with an integrated polymer floating-gate/tunneling (I-FG/T) layer consisting of poly(9,9-dioctylfluorene--benzothiadiazole) (F8BT) and polystyrene.

Development and assessment of an individualized nomogram to predict colorectal cancer liver metastases.

Background: This article aims to develop and assess the radiomics paradigm for predicting colorectal cancer liver metastasis (CRLM) from the primary tumor.

Methods: This retrospective study included 100 patients from the First Hospital of Jilin University from June 2017 to December 2017. The 100 patients comprised 50 patients with and 50 without CRLM.

Accurate colorectal tumor segmentation for CT scans based on the label assignment generative adversarial network.

Purpose: Colorectal tumor segmentation is an important step in the analysis and diagnosis of colorectal cancer. This task is a time consuming one since it is often performed manually by radiologists. This paper presents an automatic postprocessing module to refine the segmentation of deep networks.

Ratiometric Fluorescent Detection of Intracellular Singlet Oxygen by Semiconducting Polymer Dots.

Singlet oxygen (O) plays important roles in many biological processes. However, it is very difficult to detect O in the intracellular environment because of its relatively low concentration and short lifetime. Here, we developed a ratiometric probe based on semiconducting polymer dots (Pdots) that can sensitively detect O in live cells.

Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks.

Accurate segmentation of specific organ from computed tomography (CT) scans is a basic and crucial task for accurate diagnosis and treatment. To avoid time-consuming manual optimization and to help physicians distinguish diseases, an automatic organ segmentation framework is presented. The framework utilized convolution neural networks (CNN) to classify pixels.

A level set method for multiple sclerosis lesion segmentation.

In this paper, we present a level set method for multiple sclerosis (MS) lesion segmentation from FLAIR images in the presence of intensity inhomogeneities. We use a three-phase level set formulation of segmentation and bias field estimation to segment MS lesions and normal tissue region (including GM and WM) and CSF and the background from FLAIR images. To save computational load, we derive a two-phase formulation from the original multi-phase level set formulation to segment the MS lesions and normal tissue regions.

Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs.

This paper presents a novel, fully automatic approach based on a fully convolutional network (FCN) for segmenting liver tumors from CT images. Specifically, we designed a multi-channel fully convolutional network (MC-FCN) to segment liver tumors from multiphase contrast-enhanced CT images. Because each phase of contrast-enhanced data provides distinct information on pathological features, we trained one network for each phase of the CT images and fused their high-layer features together.

An Improved High-Sensitivity Airborne Transient Electromagnetic Sensor for Deep Penetration.

The investigation depth of transient electromagnetic sensors can be effectively increased by reducing the system noise, which is mainly composed of sensor internal noise, electromagnetic interference (EMI), and environmental noise, etc. A high-sensitivity airborne transient electromagnetic (AEM) sensor with low sensor internal noise and good shielding effectiveness is of great importance for deep penetration. In this article, the design and optimization of such an AEM sensor is described in detail.

Generation and dynamics analysis of N-scrolls existence in new translation-type chaotic systems.

In this paper, we propose two kinds of translation type chaotic systems for creating 2 N + 1-and 2(N + 1)-scrolls chaotic attractors from a simple three-dimensional system, which are named the translation-2 chaotic system (aa < 0) and the translation-3 chaotic system (aa > 0). We also propose the successful design criterion for constructing 2 N + 1-and 2(N + 1)-scrolls, respectively. Then, the dynamics property of the translation-2 chaotic system is studied in detail.

An adaptive reconstruction algorithm for spectral CT regularized by a reference image.

The photon counting detector based spectral CT system is attracting increasing attention in the CT field. However, the spectral CT is still premature in terms of both hardware and software. To reconstruct high quality spectral images from low-dose projections, an adaptive image reconstruction algorithm is proposed that assumes a known reference image (RI).

Generation of 2N + 1-scroll existence in new three-dimensional chaos systems.

We propose a systematic methodology for creating 2N + 1-scroll chaotic attractors from a simple three-dimensional system, which is named as the translation chaotic system. It satisfies the condition a12a21 = 0, while the Chua system satisfies a12a21 > 0. In this paper, we also propose a successful (an effective) design and an analytical approach for constructing 2N + 1-scrolls, the translation transformation principle.

An energy minimization method for MS lesion segmentation from T1-w and FLAIR images.

In this paper, we extend the multiplicative intrinsic component optimization (MICO) algorithm to multichannel MR image segmentation, with focus on segmentation of multiple sclerosis (MS) lesions. The MICO algorithm was originally proposed by Li et al. in Ref.

Distance regularized two level sets for segmentation of left and right ventricles from cine-MRI.

This paper presents a new level set method for segmentation of cardiac left and right ventricles. We extend the edge based distance regularized level set evolution (DRLSE) model in Li et al. (2010) to a two-level-set formulation, with the 0-level set and k-level set representing the endocardium and epicardium, respectively.

Object reconstitution using pseudo-inverse for ghost imaging.

We propose a novel method for object reconstruction of ghost imaging based on Pseudo-Inverse, where the original objects are reconstructed by computing the pseudo-inverse of the matrix constituted by the row vectors of each speckle field. We conduct reconstructions for binary images and gray-scale images. With equal number of measurements, our method presents a satisfying performance on enhancing Peak Signal to Noise Ratio (PSNR) and reducing computing time.