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Biological organisms experience constantly changing environments, from sudden changes in physiology brought about by feeding, to the regular rising and setting of the Sun, to ecological changes over evolutionary timescales. Living organisms have evolved to thrive in this changing world but the general principles by which organisms shape and are shaped by time varying environments remain elusive. Our understanding is particularly poor in the intermediate regime with no separation of timescales, where the environment changes on the same timescale as the physiological or evolutionary response. Experiments to systematically characterize the response to dynamic environments are challenging since such environments are inherently high dimensional. This roadmap deals with the unique role played by time varying environments in biological phenomena across scales, from physiology to evolution, seeking to emphasize the commonalities and the challenges faced in this emerging area of research.Image segmentation for human organs is an important task for the diagnosis and treatment of diseases. Current deep learning-based methods are fully supervised and need pixel-level labels. Since the medical images are highly specialized and complex, the work of delineating pixel-level segmentation masks is very time-consuming. Weakly supervised methods are then chosen to lighten the workload, which only needs physicians to determine whether an image contains the organ regions of interest. These weakly supervised methods have a common drawback, in that they do not incorporate prior knowledge that alleviates the lack of pixel-level information for segmentation. In this work, we propose a weakly supervised method based on prior knowledge for the segmentation of human organs. The proposed method was validated on three data sets of human organ segmentation. Experimental results show that the proposed image-level supervised segmentation method outperforms several state-of-the-art methods.In the context of organ shortage for transplantation, new criteria for better organ evaluation should be investigated. Ex-Vivo Lung Perfusion (EVLP) allows extra-corporal lung re-conditioning and evaluation, under controlled parameters of the organ reperfusion and mechanical ventilation. This work reports on the interest of exhaled gas analysis during the EVLP procedure. After a one-hour cold ischemia, the endogenous gas production by an isolated lung of nitric oxide and carbon monoxide is simultaneously monitored in real time. BGB 15025 The exhaled gas is analysed with two very sensitive and selective laser spectrometers developed upon the technique of optical-feedback cavity-enhanced absorption spectroscopy. Exhaled gas concentration measured for an ex-vivo lung is compared to the corresponding production by the whole living pig, measured before euthanasia. On-line measurements of the fraction of nitric oxide in exhaled gas (FENO) in isolated lungs are reported here for the first time, allowing to resolve the respiratory cycles. In this study, performed on 9 animals, FENO by isolated lungs range from 3.3 to 10.6 ppb with a median value of 4.4 ppb. Pairing ex-vivo lung and pig measurements allows to demonstrate a systematic increase of FENO in the ex-vivo lung as compared to the living animal, by a factor of 3 ± 1.2. Measurements of the fraction of carbon monoxide in exhaled gas (FECO) confirm levels recorded during previous studies driven to evaluate FECO as a potential marker of ischemia reperfusion injuries. FECO production by ex-vivo lungs ranges from 0.31 to 2.3 ppm with a median value of 0.8 ppm. As expected, these FECO values are lower than the production by the corresponding whole pig body, by a factor of 6.9 ± 2.7.A method is proposed to model by a generative adversarial network the distribution of particles exiting a patient during Monte Carlo simulation of emission tomography imaging devices. The resulting compact neural network is then able to generate particles exiting the patient, going towards the detectors, avoiding costly particle tracking within the patient. As a proof of concept, the method is evaluated for single photon emission computed tomography (SPECT) imaging and combined with another neural network modeling the detector response function (ARF-nn). A complete rotating SPECT acquisition can be simulated with reduced computation time compared to conventional Monte Carlo simulation. It also allows the user to perform simulations with several imaging systems or parameters, which is useful for imaging system design.

A system for long-length intraoperative imaging is reported based on longitudinal motion of an O-arm gantry featuring a multi-slot collimator. We assess the utility of long-length tomosynthesis and the geometric accuracy of 3D image registration for surgical guidance and evaluation of long spinal constructs.

A multi-slot collimator with tilted apertures was integrated into an O-arm system for long-length imaging. The multi-slot projective geometry leads to slight view disparity in both long-length projection images (referred to as 'line scans') and tomosynthesis 'slot reconstructions' produced using a weighted-backprojection method. The radiation dose for long-length imaging was measured, and the utility of long-length, intraoperative tomosynthesis was evaluated in phantom and cadaver studies. Leveraging the depth resolution provided by parallax views, an algorithm for 3D-2D registration of the patient and surgical devices was adapted for registration with line scans and slot reconstructions. Registrationo achieve median TRE ∼2 mm and<2° from a single scan.

The multi-slot configuration provided intraoperative visualization of long spine segments, facilitating target localization, assessment of global spinal alignment, and evaluation of long surgical constructs. 3D-2D registration to long-length tomosynthesis reconstructions yielded a promising means of guidance and verification with accuracy exceeding that of 3D-2D registration to conventional radiographs.

The multi-slot configuration provided intraoperative visualization of long spine segments, facilitating target localization, assessment of global spinal alignment, and evaluation of long surgical constructs. 3D-2D registration to long-length tomosynthesis reconstructions yielded a promising means of guidance and verification with accuracy exceeding that of 3D-2D registration to conventional radiographs.