Larger Chinese text spacing and size: effects on older users' experience

With declining vision ability, character spacing and size on smartphones designed for the general population are not accessible for older adults. This study aimed to explore how larger Chinese character spacing and size affect older adults’ user experience (UX). An orthogonal experiment was conducted. The optimal range of font size (FS), word spacing (WS) and line spacing (LS) were proposed utilising subjective evaluations to investigate the correlation of eye movement data with participants perceived UX. The results showed that improvement in different aspects of UX varied when FS, WS and LS increased. Overall, participants preferred larger FS, WS and LS, however, the larger FS, WS and LS values are more likely to cause errors and slower reading speed. These results suggest that the distinct combination of size and spacing depends on the motivation, needs and situation of older people when reading on a smartphone. These findings will help designers to provide better design for the older people

An automated cloud-based big data analytics platform for customer insights

Product reviews have a significant influence on strategic decisions for both businesses and customers on what to produce or buy. However, with the availability of large amounts of online information, manual analysis of reviews is costly and time consuming, as well as being subjective and prone to error. In this work, we present an automated scalable cloud-based system to harness big customer reviews on products for customer insights through data pipeline from data acquisition, analysis to visualisation in an efficient way. The experimental evaluation has shown that the proposed system achieves good performance in terms of accuracy and computing time

A hybrid patient-specific biomechanical model based image registration method for the motion estimation of lungs

This paper presents a new hybrid biomechanical model-based non-rigid image registration method for lung motion estimation. In the proposed method, a patient-specific biomechanical modelling process captures major physically realistic deformations with explicit physical modelling of sliding motion, whilst a subsequent non-rigid image registration process compensates for small residuals. The proposed algorithm was evaluated with 10 4D CT datasets of lung cancer patients. The target registration error (TRE), defined as the Euclidean distance of landmark pairs, was significantly lower with the proposed method (TRE = 1.37 mm) than with biomechanical modelling (TRE = 3.81 mm) and intensity-based image registration without specific considerations for sliding motion (TRE = 4.57 mm). The proposed method achieved a comparable accuracy as several recently developed intensity-based registration algorithms with sliding handling on the same datasets. A detailed comparison on the distributions of TREs with three non-rigid intensity-based algorithms showed that the proposed method performed especially well on estimating the displacement field of lung surface regions (mean TRE = 1.33 mm, maximum TRE = 5.3 mm). The effects of biomechanical model parameters (such as Poisson’s ratio, friction and tissue heterogeneity) on displacement estimation were investigated. The potential of the algorithm in optimising biomechanical models of lungs through analysing the pattern of displacement compensation from the image registration process has also been demonstrated

Detection and modelling of contacts in explicit finite-element simulation of soft tissue biomechanics

Realistic modelling of soft-tissue biomechanics and mechanical interactions between tissues is an important part of surgical simulation, and may become a valuable asset in surgical image-guidance. Unfortunately, it is also computationally very demanding. Explicit matrix-free FEM solvers have been shown to be a good choice for fast tissue simulation, however little work has been done on contact algorithms for such FEM solvers. This work introduces such an algorithm that is capable of handling the scenarios typically encountered in image-guidance. The responses are computed with an evolution of the Lagrange-multiplier method first used by Taylor and Flanagan in PRONTO 3D with spatio-temporal smoothing heuristics for improved stability with coarser meshes and larger time steps. For contact search, a bounding-volume hierarchy (BVH) capable of identifying self collisions, and which is optimised for the small time steps by reducing the number of bounding-volume refittings between iterations through identification of geometry areas with mostly rigid motion and negligible deformation, is introduced. Further optimisation is achieved by integrating the self-collision criterion in the BVH creation and updating algorithms. The effectiveness of the algorithm is demonstrated on a number of artificial test cases and meshes derived from medical image data

A nonlinear biomechanical model based registration method for aligning prone and supine MR breast images

Preoperative diagnostic magnetic resonance (MR) breast images can provide good contrast between different tissues and 3-D information about suspicious tissues. Aligning preoperative diagnostic MR images with a patient in the theatre during breast conserving surgery could assist surgeons in achieving the complete excision of cancer with sufficient margins. Typically, preoperative diagnostic MR breast images of a patient are obtained in the prone position, while surgery is performed in the supine position. The significant shape change of breasts between these two positions due to gravity loading, external forces and related constraints makes the alignment task extremely difficult. Our previous studies have shown that either nonrigid intensity-based image registration or biomechanical modelling alone are limited in their ability to capture such a large deformation. To tackle this problem, we proposed in this paper a nonlinear biomechanical model-based image registration method with a simultaneous optimization procedure for both the material parameters of breast tissues and the direction of the gravitational force. First, finite element (FE) based biomechanical modelling is used to estimate a physically plausible deformation of the pectoral muscle and the major deformation of breast tissues due to gravity loading. Then, nonrigid intensity-based image registration is employed to recover the remaining deformation that FE analyses do not capture due to the simplifications and approximations of biomechanical models and the uncertainties of external forces and constraints. We assess the registration performance of the proposed method using the target registration error of skin fiducial markers and the Dice similarity coefficient (DSC) of fibroglandular tissues. The registration results on prone and supine MR image pairs are compared with those from two alternative nonrigid registration methods for five breasts. Overall, the proposed algorithm achieved the best registration performance on fiducial markers (target registration error, 8.44 ±5.5 mm for 45 fiducial markers) and higher overlap rates on segmentation propagation of fibroglandular tissues (DSC value > 82%)

Technical Note: Error metrics for estimating the accuracy of needle/instrument placement during transperineal magnetic resonance/ultrasound-guided prostate interventions

Purpose Image‐guided systems that fuse magnetic resonance imaging (MRI) with three‐dimensional (3D) ultrasound (US) images for performing targeted prostate needle biopsy and minimally invasive treatments for prostate cancer are of increasing clinical interest. To date, a wide range of different accuracy estimation procedures and error metrics have been reported, which makes comparing the performance of different systems difficult. Methods A set of nine measures are presented to assess the accuracy of MRI‐US image registration, needle positioning, needle guidance, and overall system error, with the aim of providing a methodology for estimating the accuracy of instrument placement using a MR/US‐guided transperineal approach. Results Using the SmartTarget fusion system, an MRI‐US image alignment error was determined to be 2.0 ± 1.0 mm (mean ± SD), and an overall system instrument targeting error of 3.0 ± 1.2 mm. Three needle deployments for each target phantom lesion was found to result in a 100% lesion hit rate and a median predicted cancer core length of 5.2 mm. Conclusions The application of a comprehensive, unbiased validation assessment for MR/US guided systems can provide useful information on system performance for quality assurance and system comparison. Furthermore, such an analysis can be helpful in identifying relationships between these errors, providing insight into the technical behavior of these systems