A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Non-contact strain determination of cell traction effects

Irreversible tissue damage leading to organ failure is a common health problem in today's world. Regenerating these damaged tissues with the help of scaffolds is the solution offered by tissue engineering. In cases where the extra-cellular matrix (ECM) is to be replaced by an artificial substrate (scaffold) or matrix, cellular traction forces (CTF) are exerted by the cells on the scaffold surface. An ideal scaffold should exhibit mechanical characteristics similar to those of the ECM it is intended to replace. In other words, the capacity of a scaffold to withstand deformation should be comparable to that of a natural ECM. And with knowledge of those forces and deformations the properties of the scaffolds may be inferred. Digital Image Correlation (DIC), a non-contact image analysis technique enables us to measure point to point deformation of the scaffold by comparing a sequence of images captured during the process of scaffold deformation. This review discusses the methodology involved and implementation of DIC to measure displacements and strain.Irreversible tissue damage leading to organ failure is a common health problem in today's world. Regenerating these damaged tissues with the help of scaffolds is the solution offered by tissue engineering. In cases where the extra-cellular matrix (ECM) is to be replaced by an artificial substrate (scaffold) or matrix, cellular traction forces (CTF) are exerted by the cells on the scaffold surface. An ideal scaffold should exhibit mechanical characteristics similar to those of the ECM it is intended to replace. In other words, the capacity of a scaffold to withstand deformation should be comparable to that of a natural ECM. And with knowledge of those forces and deformations the properties of the scaffolds may be inferred. Digital Image Correlation (DIC), a non-contact image analysis technique enables us to measure point to point deformation of the scaffold by comparing a sequence of images captured during the process of scaffold deformation. This review discusses the methodology involved and implementation of DIC to measure displacements and strain

Imaging technologies for preclinical models of bone and joint disorders

Preclinical models for musculoskeletal disorders are critical for understanding the pathogenesis of bone and joint disorders in humans and the development of effective therapies. The assessment of these models primarily relies on morphological analysis which remains time consuming and costly, requiring large numbers of animals to be tested through different stages of the disease. The implementation of preclinical imaging represents a keystone in the refinement of animal models allowing longitudinal studies and enabling a powerful, non-invasive and clinically translatable way for monitoring disease progression in real time. Our aim is to highlight examples that demonstrate the advantages and limitations of different imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), single-photon emission computed tomography (SPECT) and optical imaging. All of which are in current use in preclinical skeletal research. MRI can provide high resolution of soft tissue structures, but imaging requires comparatively long acquisition times; hence, animals require long-term anaesthesia. CT is extensively used in bone and joint disorders providing excellent spatial resolution and good contrast for bone imaging. Despite its excellent structural assessment of mineralized structures, CT does not provide in vivo functional information of ongoing biological processes. Nuclear medicine is a very promising tool for investigating functional and molecular processes in vivo with new tracers becoming available as biomarkers. The combined use of imaging modalities also holds significant potential for the assessment of disease pathogenesis in animal models of musculoskeletal disorders, minimising the use of conventional invasive methods and animal redundancy

Review of pore network modelling of porous media: experimental characterisations, network constructions and applications to reactive transport

AbstractPore network models have been applied widely for simulating a variety of different physical and chemical processes, including phase exchange, non-Newtonian displacement, non-Darcy flow, reactive transport and thermodynamically consistent oil layers. The realism of such modelling, i.e. the credibility of their predictions, depends to a large extent on the quality of the correspondence between the pore space of a given medium and the pore network constructed as its representation. The main experimental techniques for pore space characterisation, including direct imaging, mercury intrusion porosimetry and gas adsorption, are firstly summarised. A review of the main pore network construction techniques is then presented. Particular focus is given on how such constructions are adapted to the data from experimentally characterised pore systems. Current applications of pore network models are considered, with special emphasis on the effects of adsorption, dissolution and precipitation, as well as biomass growth, on transport coefficients. Pore network models are found to be a valuable tool for understanding and predicting meso-scale phenomena, linking single pore processes, where other techniques are more accurate, and the homogenised continuum porous media, used by engineering community

Cochlear Compartments Segmentation and Pharmacokinetics using Micro Computed Tomography Images

Local drug delivery to the inner ear via micropump implants has the potential to be much more effective than oral drug delivery for treating patients with sensorineural hearing loss and to protect hearing from ototoxic insult due to noise exposure. Delivering appropriate concentrations of drugs to the necessary cochlear compartments is of paramount importance; however, directly measuring local drug concentrations over time throughout the cochlea is not possible. Indirect measurement using otoacoustic emissions and auditory brainstem response are ineffective as they only provide an estimate of concentration and are susceptible to non-linear sensitivity effects. Imaging modalities such as MRI with infused gadolinium contrast agent are limited due to the high spatial resolution requirement for pharmacokinetic analysis, especially in mice with cochlear length in the micron scale. We develop an intracochlear pharmacokinetic model using micro-computed tomography imaging of the cochlea during in vivo infusion of a contrast agent at the basal end of scala tympani through a cochleostomy. This approach requires accurately segmenting the main cochlear compartments: scala tympani (ST), scala media (SM) and scala vestibuli (SV). Each scan was segmented using 1) atlas-based deformable registration, and 2) V-Net, a encoder-decoder style convolutional neural network. The segmentation of these cochlear regions enable concentrations to be extracted along the length of each scala. These spatio-temporal concentration profiles are used to learn a concentration dependent diffusion coefficient, and transport parameters between the major scalae and to clearance. The pharmacokinetic model results are comparable to the current state of the art model, and can simulate concentrations for cases involving different infusion molecules and drug delivery protocols. While our model shows promising results, to extend the approach to larger animals and to generate accurate further experimental data, computational constraints, and time requirements of previous segmentation methods need to be mitigated. To this end, we extended the V-Net architecture with inclusion of spatial attention. Moreover, to enable segmentation in hardware restricted environments, we designed a 3D segmentation network using Capsule Networks that can provide improved segmentation performance along with 90% reduction in trainable parameters. Finally, to demonstrate the effectiveness of these networks, we test them on multiple public datasets. They are also tested on the cochlea dataset and pharmacokinetic model simulations will be validated against existing results

Computed Tomography of Chemiluminescence: A 3D Time Resolved Sensor for Turbulent Combustion

Time resolved 3D measurements of turbulent flames are required to further understanding of combustion and support advanced simulation techniques (LES). Computed Tomography of Chemiluminescence (CTC) allows a flame’s 3D chemiluminescence profile to be obtained by inverting a series of integral measurements. CTC provides the instantaneous 3D flame structure, and can also measure: excited species concentrations, equivalence ratio, heat release rate, and possibly strain rate. High resolutions require simultaneous measurements from many view points, and the cost of multiple sensors has traditionally limited spatial resolutions. However, recent improvements in commodity cameras makes a high resolution CTC sensor possible and is investigated in this work. Using realistic LES Phantoms (known fields), the CT algorithm (ART) is shown to produce low error reconstructions even from limited noisy datasets. Error from selfabsorption is also tested using LES Phantoms and a modification to ART that successfully corrects this error is presented. A proof-of-concept experiment using 48 non-simultaneous views is performed and successfully resolves a Matrix Burner flame to 0.01% of the domain width (D). ART is also extended to 3D (without stacking) to allow 3D camera locations and optical effects to be considered. An optical integral geometry (weighted double-cone) is presented that corrects for limited depth-of-field, and (even with poorly estimated camera parameters) reconstructs the Matrix Burner as well as the standard geometry. CTC is implemented using five PicSight P32M cameras and mirrors to provide 10 simultaneous views. Measurements of the Matrix Burner and a Turbulent Opposed Jet achieve exposure times as low as 62 μs, with even shorter exposures possible. With only 10 views the spatial resolution of the reconstructions is low. However, a cosine Phantom study shows that 20–40 viewing angles are necessary to achieve high resolutions (0.01– 0.04D). With 40 P32M cameras costing £40000, future CTC implementations can achieve high spatial and temporal resolutions