Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks

Recently, the cycle-consistent generative adversarial networks (CycleGAN) has been widely used for synthesis of multi-domain medical images. The domain-specific nonlinear deformations captured by CycleGAN make the synthesized images difficult to be used for some applications, for example, generating pseudo-CT for PET-MR attenuation correction. This paper presents a deformation-invariant CycleGAN (DicycleGAN) method using deformable convolutional layers and new cycle-consistency losses. Its robustness dealing with data that suffer from domain-specific nonlinear deformations has been evaluated through comparison experiments performed on a multi-sequence brain MR dataset and a multi-modality abdominal dataset. Our method has displayed its ability to generate synthesized data that is aligned with the source while maintaining a proper quality of signal compared to CycleGAN-generated data. The proposed model also obtained comparable performance with CycleGAN when data from the source and target domains are alignable through simple affine transformations

Polymers grafted to porous membranes

We study a single flexible chain molecule grafted to a membrane which has pores of size slightly larger than the monomer size. On both sides of the membrane there is the same solvent. When this solvent is good, i.e. when the polymer is described by a self avoiding walk, it can fairly easily penetrate the membrane, so that the average number of membrane crossings tends, for chain length $N\to\infty$, to a positive constant. The average numbers of monomers on either side of the membrane diverges in this limit, although their ratio becomes infinite. For a poor solvent, in contrast, the entire polymer is located, for large $N$, on one side of the membrane. For good and for theta solvents (ideal polymers) we find scaling laws, whose exponents can in the latter case be easily understood from the behaviour of random walks.Comment: 4 pages, 6 figure

Depth-Supervised NeRF for Multi-View RGB-D Operating Room Images

Neural Radiance Fields (NeRF) is a powerful novel technology for the reconstruction of 3D scenes from a set of images captured by static cameras. Renders of these reconstructions could play a role in virtual presence in the operating room (OR), e.g. for training purposes. In contrast to existing systems for virtual presence, NeRF can provide real instead of simulated surgeries. This work shows how NeRF can be used for view synthesis in the OR. A depth-supervised NeRF (DS-NeRF) is trained with three or five synchronised cameras that capture the surgical field in knee replacement surgery videos from the 4D-OR dataset. The algorithm is trained and evaluated for images in five distinct phases before and during the surgery. With qualitative analysis, we inspect views synthesised by a virtual camera that moves in 180 degrees around the surgical field. Additionally, we quantitatively inspect view synthesis from an unseen camera position in terms of PSNR, SSIM and LPIPS for the colour channels and in terms of MAE and error percentage for the estimated depth. DS-NeRF generates geometrically consistent views, also from interpolated camera positions. Views are generated from an unseen camera pose with an average PSNR of 17.8 and a depth estimation error of 2.10%. However, due to artefacts and missing of fine details, the synthesised views do not look photo-realistic. Our results show the potential of NeRF for view synthesis in the OR. Recent developments, such as NeRF for video synthesis and training speedups, require further exploration to reveal its full potential.Comment: 12 pages, 4 figures, submitted to the 14th International Conference on Information Processing in Computer-Assisted Intervention

De bijdrage van toevoeging van cacao aan tabak aan rookverslaving

In this report the effect of these compounds on the addiction to cigarette smoking was assessed, using currently available information in the literature on psychoactive compounds of cocoa. The investigated psychoactive cocoa compounds were theobromine, caffeine, serotonin, histamine, tryptophan, tryptamine, tyramine, phenylethylamine, octopamine and anandamide. The general conclusion is that the level of these compounds in added cocoa in cigarettes is not sufficient to increase the addiction to cigarette smoking.In dit rapport wordt de mogelijke bijdrage van cacao aan rookverslaving beschreven. Cacao wordt aan tabak toegevoegd om de smaak te verbeteren. Daarnaast bevat cacao tal van psychoactieve stoffen die mogelijk bijdragen aan rookverslaving Dit literatuuronderzoek beschrijft de blootstelling, farmacologie, farmacokinetiek, toxicologie, interacties en verslavende eigenschappen van de tien meest bekende stoffen in cacao. De onderzochte stoffen zijn theobromine, caffeine, serotonine, histamine, tryptofaan, tryptamine, tyramine, fenylethylamine, octopamine en anandamide. Deze stoffen komen ook via dranken en voedsel het lichaam binnen of worden door het lichaam zelf aangemaakt. Dit rapport laat zien dat de aan roken gerelateerde blootstelling aan de psychoactieve stoffen uit cacao gering is ten opzichte van de inname via voeding en dranken en/of de lichaamseigen productie van deze stoffen. Een systemisch effect lijkt derhalve onwaarschijnlijk ook al omdat lichaamseigen stoffen snel worden afgebroken. Daarnaast kunnen deze stoffen, omdat ze geinhaleerd worden, een direct effect op de luchtwegen hebben. Daarmee zou de opname van nicotine beinvloed kunnen worden. De nicotine opname zou bijvoorbeeld kunnen toenemen via luchtwegverwijding door theobromine en cafe6ne of kunnen afnemen door luchtwegvernauwing door histamine. Dit rapport laat zien dat de aan roken gerelateerde blootstelling aan deze stoffen waarschijnlijk te gering is voor een direct effect op de luchtwegen. Verder dient te worden opgemerkt dat de hoeveelheid tryptamine, tyramine en fenylethylamine die via cacao wordt toegevoegd verwaarloosbaar is ten opzichte van de hoeveelheid die in tabak zelf aanwezig is. Tot slot is aandacht besteed aan de verbrandingsproducten van cacao. Amine verbindingen als serotonin, tryptofaan, tyramine, tryptamine en fenylethylamine vormen tijdens het roken stoffen die het enzym mono amine oxidase (MAO) remmen. MAO-remmers hebben een anti-depressieve werking en kunnen op die manier bijdragen aan rookverslaving. De conclusie van dit literatuur onderzoek is dat de afzonderlijke psychoactieve stoffen in tabak als gevolg van toevoeging van cacao niet direct bijdragen aan rookverslaving. De verbrandingsproducten van cacao doen dit, via remming van het enzym mono amine oxidase, mogelijk wel. Ook de smaak van cacao wordt geassocieerd met verslaving. De literatuur biedt geen inzicht in het effect op gezondheid en verslaving van het inhaleren van de combinatie van de 10 onderzochte stoffen uit cacao

Detector-Agnostic Phase-Space Distributions

The representation of quantum states via phase-space functions constitutes an intuitive technique to characterize light. However, the reconstruction of such distributions is challenging as it demands specific types of detectors and detailed models thereof to account for their particular properties and imperfections. To overcome these obstacles, we derive and implement a measurement scheme that enables a reconstruction of phase-space distributions for arbitrary states whose functionality does not depend on the knowledge of the detectors, thus defining the notion of detector-agnostic phase-space distributions. Our theory presents a generalization of well-known phase-space quasiprobability distributions, such as the Wigner function. We implement our measurement protocol, using state-of-the-art transition-edge sensors without performing a detector characterization. Based on our approach, we reveal the characteristic features of heralded single- and two-photon states in phase space and certify their nonclassicality with high statistical significance