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General General

Image Interpolation Using Multi-scale Attention-aware Inception Network.

In IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

A new multi-scale deep learning (MDL) framework is proposed and exploited for conducting image interpolation in this paper. The core of the framework is a seeding network that needs to be designed for the targeted task. For image interpolation, a novel attention-aware inception network (AIN) is developed as the seeding network; it has two key stages: 1) feature extraction based on the low-resolution input image; and 2) feature-to-image mapping to enlarge image's size or resolution. Note that the designed seeding network, AIN, needs to be trained with a matched training dataset at each scale. For that, multi-scale image patches are generated using our proposed pyramid cut, which outperforms the conventional image pyramid method by completely avoiding aliasing issue. After training, the trained AINs are then combined for processing the input image in the testing stage. Extensive experimental simulation results obtained from seven image datasets (comprising 359 images in total) have clearly shown that the proposed MAIN consistently delivers highly accurate interpolated images.

Ji Jiahuan, Zhong Baojiang, Ma Kai-Kuang

2020-Sep-30

General General

Supervised Descent Learning for Thoracic Electrical Impedance Tomography.

In IEEE transactions on bio-medical engineering

OBJECTIVE : The absolute image reconstruction problem of electrical impedance tomography (EIT) is ill-posed. Traditional methods usually solve a nonlinear least squares problem with some kind of regularization. These methods suffer from low accuracy, poor anti-noise performance, and long computation time. Besides, the integration of a priori information is not very flexible. This work tries to solve EIT inverse problem using a machine learning algorithm for the application of thorax imaging.

METHODS : We developed the supervised descent learning EIT (SDL-EIT) inversion algorithm based on the idea of supervised descent method (SDM). The algorithm approximates the mapping from measured data to the conductivity image by a series of descent directions learned from training samples. We designed a training data set in which the thorax contour and some general structure of lungs and heart are embedded. The algorithm is implemented in both two- and three-dimensional cases, and is evaluated using synthetic and measured thoracic data.

RESULTS AND CONCLUSION : For synthetic data, SDL-EIT shows better accuracy and anti-noise performance compared with traditional Gauss Newton inversion (GNI) method. For measured data, the result of SDL-EIT is reasonable compared with computed tomography (CT) scan image.

SIGNIFICANCE : Using SDL-EIT, prior information can be easily integrated through the specifically designed training data set, and the image reconstruction process can be accelerated. The algorithm is effective in inverting measured thoracic data. It is a potential algorithm for human thorax imaging.

Zhang Ke, Guo Rui, Li Maokun, Yang Fan, Xu Shenheng, Abubakar Aria

2020-Sep-30

General General

Online control of reach accuracy in mice.

In Journal of neurophysiology ; h5-index 46.0

Reaching movements, as a basic yet complex motor behavior, are a foundational model system in neuroscience. In particular, there has been a significant recent expansion of investigation into the neural circuit mechanisms of reach behavior in mice. Nevertheless, quantification of mouse reach kinematics remains lacking, limiting comparison to the primate literature. In this study, we quantitatively demonstrate the homology of mouse reach kinematics to primate reach, and also discover novel late-phase correlational structure that implies online control. Overall, our results highlight the decelerative phase of reach as important in driving successful outcome. Specifically, we develop and implement a novel statistical machine learning algorithm to identify kinematic features associated with successful reaches and find that late-phase kinematics are most predictive of outcome, signifying online reach control as opposed to pre-planning. Moreover, we identify and characterize late-phase kinematic adjustments that are yoked to mid-flight position and velocity of the limb, allowing for dynamic correction of initial variability, with head-fixed reaches being less dependent on position in comparison to freely-behaving reaches. Furthermore, consecutive reaches exhibit positional error-correction but not hot-handedness, implying opponent regulation of motor variability. Overall, our results establish foundational mouse reach kinematics in the context of neuroscientific investigation, characterizing mouse reach production as an active process that relies on dynamic online control mechanisms.

Becker Matthew I, Calame Dylan J, Wrobel Julia, Person Abigail L

2020-Sep-30

in-flight correction, kinematics, motor control, mouse, reach

General General

Detection Methods of COVID-19.

In SLAS technology

Since being first detected in China, coronavirus disease 2019 (COVID-19) has spread rapidly across the world, triggering a global pandemic with no viable cure in sight. As a result, national responses have focused on the effective minimization of the spread. Border control measures and travel restrictions have been implemented in a number of countries to limit the import and export of the virus. The detection of COVID-19 is a key task for physicians. The erroneous results of early laboratory tests and their delays led researchers to focus on different options. Information obtained from computed tomography (CT) and radiological images is important for clinical diagnosis. Therefore, it is worth developing a rapid method of detection of viral diseases through the analysis of radiographic images. We propose a novel method of detection of COVID-19. The purpose is to provide clinical decision support to healthcare workers and researchers. The article is to support researchers working on early detection of COVID-19 as well as similar viral diseases.

Echtioui Amira, Zouch Wassim, Ghorbel Mohamed, Mhiri Chokri, Hamam Habib

2020-Sep-30

CNN, COVID-19, convolutional neural network, deep learning, diagnosis

General General

Risk Factors for Mortality in Critically Ill Patients with COVID-19 in Huanggang, China: A Single-Centre Multivariate Pattern Analysis.

In Journal of medical virology

To date, the coronavirus disease 2019 (COVID-19) has a worldwide distribution. Risk factors for mortality in critically ill patients, especially detailed self-evaluation indicators and laboratory-examination indicators, have not been well described. In this paper, a total of 192 critically ill patients (142 were discharged and 50 died in the hospital) with COVID-19 were included. Self-evaluation indicators including demographics, baseline characteristics and symptoms and detailed lab-examination indicators were extracted. Data were first compared between survivors and non-survivors. Multivariate pattern analysis (MVPA) was performed to identify possible risk factors for mortality of COVID-19 patients. MVPA achieved a relatively high classification accuracy of 93% when using both self-evaluation indicators and laboratory-examination indicators. Several self-evaluation factors related to COVID-19 were highly associated with mortality, including age, duration (time from illness onset to admission), and the Barthel index score. When the duration, age and Barthel index increased by one day, one year and one point, the mortality increased by 3.6%, 2.4% and 0.9% respectively. Laboratory-examination indicators including C-reactive protein (CRP), white blood cell (WBC) count, platelet count, fibrin degradation products (FDP), oxygenation index (OI), lymphocyte count and D-dimer were also risk factors. Among them, duration was the strongest predictor of all-cause mortality. Several self-evaluation indicators that can simply be obtained by questionnaires and without clinical examination were the risk factors of all-cause mortality in critically ill COVID-19 patients. The prediction model can be used by individuals to improve health awareness, and by clinicians to identify high-risk individuals. This article is protected by copyright. All rights reserved.

Chen Yinyin, Linli Zeqiang, Lei Yuting, Yang Yiya, Liu Zhipeng, Xia Youchun, Liang Yumei, Zhu Huabo, Guo Shuixia

2020-Sep-30

COVID-19, Clinical indicators, Machine learning, Risk factor, Self-evaluation

General General

4D deep learning for real-time volumetric optical coherence elastography.

In International journal of computer assisted radiology and surgery

PURPOSE : Elasticity of soft tissue provides valuable information to physicians during treatment and diagnosis of diseases. A number of approaches have been proposed to estimate tissue stiffness from the shear wave velocity. Optical coherence elastography offers a particularly high spatial and temporal resolution. However, current approaches typically acquire data at different positions sequentially, making it slow and less practical for clinical application.

METHODS : We propose a new approach for elastography estimations using a fast imaging device to acquire small image volumes at rates of 831 Hz. The resulting sequence of phase image volumes is fed into a 4D convolutional neural network which handles both spatial and temporal data processing. We evaluate the approach on a set of image data acquired for gelatin phantoms of known elasticity.

RESULTS : Using the neural network, the gelatin concentration of unseen samples was predicted with a mean error of 0.65 ± 0.81 percentage points from 90 subsequent volumes of phase data only. We achieve a data acquisition and data processing time of under 12 ms and 22 ms, respectively.

CONCLUSIONS : We demonstrate direct volumetric optical coherence elastography from phase image data. The approach does not rely on particular stimulation or sampling sequences and allows the estimation of elastic tissue properties of up to 40 Hz.

Neidhardt M, Bengs M, Latus S, Schl├╝ter M, Saathoff T, Schlaefer A

2020-Sep-30

Convolutional neuronal networks, Deep learning, Optical coherence elastography, Real-time imaging