MI-SegNet: Mutual Information-Based US Segmentation for Unseen Domain Generalization

Generalization capabilities of learning-based medical image segmentation across domains are currently limited by the performance degradation caused by the domain shift, particularly for ultrasound (US) imaging. The quality of US images heavily relies on carefully tuned acoustic parameters, which vary across sonographers, machines, and settings. To improve the generalizability on US images across domains, we propose MI-SegNet, a novel mutual information (MI) based framework to explicitly disentangle the anatomical and domain feature representations; therefore, robust domain-independent segmentation can be expected. Two encoders are employed to extract the relevant features for the disentanglement. The segmentation only uses the anatomical feature map for its prediction. In order to force the encoders to learn meaningful feature representations a cross-reconstruction method is used during training. Transformations, specific to either domain or anatomy are applied to guide the encoders in their respective feature extraction task. Additionally, any MI present in both feature maps is punished to further promote separate feature spaces. We validate the generalizability of the proposed domain-independent segmentation approach on several datasets with varying parameters and machines. Furthermore, we demonstrate the effectiveness of the proposed MI-SegNet serving as a pre-trained model by comparing it with state-of-the-art networks.Comment: Accepted by MICCAI 202

Adipocyte-derived extracellular vesicles increase insulin secretion through transport of insulinotropic protein cargo

Adipocyte-derived extracellular vesicles (AdEVs) are membranous nanoparticles that convey communication from adipose tissue to other organs. Here, to delineate their role as messengers with glucoregulatory nature, we paired fluorescence AdEV-tracing and SILAC-labeling with (phospho)proteomics, and revealed that AdEVs transfer functional insulinotropic protein cargo into pancreatic β-cells. Upon transfer, AdEV proteins were subjects for phosphorylation, augmented insulinotropic GPCR/cAMP/PKA signaling by increasing total protein abundances and phosphosite dynamics, and ultimately enhanced 1st-phase glucose-stimulated insulin secretion (GSIS) in murine islets. Notably, insulinotropic effects were restricted to AdEVs isolated from obese and insulin resistant, but not lean mice, which was consistent with differential protein loads and AdEV luminal morphologies. Likewise, in vivo pre-treatment with AdEVs from obese but not lean mice amplified insulin secretion and glucose tolerance in mice. This data suggests that secreted AdEVs can inform pancreatic β-cells about insulin resistance in adipose tissue in order to amplify GSIS in times of increased insulin demand

Dermal features derived from optoacoustic tomograms via machine learning correlate10 microangiopathy phenotypes with diabetes stage

<p>Skin microangiopathy has been associated with diabetes. Here we show that skin-microangiopathy phenotypes in humans can be correlated with diabetes stage via morphophysiological cutaneous features extracted from raster-scan optoacoustic mesoscopy (RSOM) images of skin on the leg. We obtained 199 RSOM images from 115 participants (40 healthy and 75 with diabetes), and used machine learning to segment skin layers and microvasculature to identify clinically explainable features pertaining to different depths and scales of detail that provided the highest predictive power. Features in the dermal layer at the scale of detail of 0.1–1 mm (such as the number of junction-to-junction branches) were highly sensitive to diabetes stage. A 'microangiopathy score' compiling the 32 most-relevant features predicted the presence of diabetes with an area under the receiver operating characteristic curve of 0.84. The analysis of morphophysiological cutaneous features via RSOM may allow for the discovery of diabetes biomarkers in the skin and for the monitoring of diabetes status.</p&gt

Dermal features derived from optoacoustic tomograms via machine learning correlate microangiopathy phenotypes with diabetes stage

<p>Skin microangiopathy has been associated with diabetes. Here we show that skin-microangiopathy phenotypes in humans can be correlated with diabetes stage via morphophysiological cutaneous features extracted from raster-scan optoacoustic mesoscopy (RSOM) images of skin on the leg. We obtained 199 RSOM images from 115 participants (40 healthy and 75 with diabetes), and used machine learning to segment skin layers and microvasculature to identify clinically explainable features pertaining to different depths and scales of detail that provided the highest predictive power. Features in the dermal layer at the scale of detail of 0.1–1 mm (such as the number of junction-to-junction branches) were highly sensitive to diabetes stage. A 'microangiopathy score' compiling the 32 most-relevant features predicted the presence of diabetes with an area under the receiver operating characteristic curve of 0.84. The analysis of morphophysiological cutaneous features via RSOM may allow for the discovery of diabetes biomarkers in the skin and for the monitoring of diabetes status.</p&gt

Multimodal optoacoustic and multiphoton microscopy of human carotid atheroma

Carotid artery atherosclerosis is a main cause of stroke. Understanding atherosclerosis biology is critical in the development of targeted prevention and treatment strategies. Consequently, there is demand for advanced tools investigating atheroma pathology. We consider hybrid optoacoustic and multiphoton microscopy for the integrated and complementary interrogation of plaque tissue constituents and their mutual interactions. Herein, we visualize human carotid plaque using a hybrid multimodal imaging system that combines optical resolution optoacoustic (photoacoustic) microscopy, second and third harmonic generation microscopy, and two-photon excitation fluorescence microscopy. Our data suggest more comprehensive insights in the pathophysiology of atheroma formation and destabilization, by enabling congruent visualization of structural and biological features critical for the atherosclerotic process and its acute complications, such as red blood cells and collagen

VesNet-RL: Simulation-based Reinforcement Learning for Real-World US Probe Navigation

Ultrasound (US) is one of the most common medical imaging modalities since it is radiation-free, low-cost, and real-time. In freehand US examinations, sonographers often navigate a US probe to visualize standard examination planes with rich diagnostic information. However, reproducibility and stability of the resulting images often suffer from intra- and inter-operator variation. Reinforcement learning (RL), as an interaction-based learning method, has demonstrated its effectiveness in visual navigating tasks; however, RL is limited in terms of generalization. To address this challenge, we propose a simulation-based RL framework for real-world navigation of US probes towards the standard longitudinal views of vessels. A UNet is used to provide binary masks from US images; thereby, the RL agent trained on simulated binary vessel images can be applied in real scenarios without further training. To accurately characterize actual states, a multi-modality state representation structure is introduced to facilitate the understanding of environments. Moreover, considering the characteristics of vessels, a novel standard view recognition approach based on the minimum bounding rectangle is proposed to terminate the searching process. To evaluate the effectiveness of the proposed method, the trained policy is validated virtually on 3D volumes of a volunteer's in-vivo carotid artery, and physically on custom-designed gel phantoms using robotic US. The results demonstrate that proposed approach can effectively and accurately navigate the probe towards the longitudinal view of vessels.Comment: Directly accepted by IEEE RAL after the first round of review. Video: https://www.youtube.com/watch?v=bzCO07Hquj8 Codes: https://github.com/yuan-12138/VesNet-R

Multicompartmental non-invasive sensing of postprandial lipemia in humans with multispectral optoacoustic tomography

OBJECTIVE Postprandial lipid profiling (PLP), a risk indicator of cardiometabolic disease, is based on frequent blood sampling over several hours after a meal, an approach that is invasive and inconvenient. Non-invasive PLP may offer an alternative for disseminated human monitoring. Herein, we investigate the use of clinical multispectral optoacoustic tomography (MSOT) for non-invasive, label-free PLP via direct lipid-sensing in human vasculature and soft tissues. METHODS Four (n = 4) subjects (3 females and 1 male, age: 28 ± 7 years) were enrolled in the current pilot study. We longitudinally measured the lipid signals in arteries, veins, skeletal muscles, and adipose tissues of all participants at 30-min intervals for 6 h after the oral consumption of a high-fat meal. RESULTS Optoacoustic lipid-signal analysis showed on average a 63.4% intra-arterial increase at ~ 4 h postprandially, an 83.9% intra-venous increase at ~ 3 h, a 120.8% intra-muscular increase at ~ 3 h, and a 32.8% subcutaneous fat increase at ~ 4 h. CONCLUSION MSOT provides the potential to study lipid metabolism that could lead to novel diagnostics and prevention strategies by label-free, non-invasive detection of tissue biomarkers implicated in cardiometabolic diseases

A dual Ucp1 reporter mouse model for imaging and quantitation of brown and brite fat recruitment

Objectives: Brown adipose tissue (BAT) dissipates nutritional energy as heat through uncoupling protein 1 (UCP1). The discovery of functional BAT in healthy adult humans has promoted the search for pharmacological interventions to recruit and activate brown fat as a treatment of obesity and diabetes type II. These efforts require in vivo models to compare the efficacy of novel compounds in a relevant physiological context. Methods: We generated a knock-in mouse line expressing firefly luciferase and near-infrared red florescent protein (iRFP713) driven by the regulatory elements of the endogenous Ucp1 gene. Results: Our detailed characterization revealed that firefly luciferase activity faithfully reports endogenous Ucp1 gene expression in response to physiological and pharmacological stimuli. The iRFP713 fluorescence signal was detected in the interscapular BAT region of cold-exposed reporter mice in an allele-dosage dependent manner. Using this reporter mouse model, we detected a higher browning capacity in female peri-ovarian white adipose tissue compared to male epididymal WAT, which we further corroborated by molecular and morphological features. In situ imaging detected a strong luciferase activity signal in a previously unappreciated adipose tissue depot adjunct to the femoral muscle, now adopted as femoral brown adipose tissue. In addition, screening cultured adipocytes by bioluminescence imaging identified the selective Salt-Inducible Kinase inhibitor, HG-9-91-01, to increase Ucp1 gene expression and mitochondrial respiration in brown and brite adipocytes. Conclusions: In our mouse model, firefly luciferase activity serves as a bona fide reporter for dynamic regulation of Ucp1. In addition, by means of iRFP713 we are able to monitor Ucp1 expression in a non-invasive fashion. Keywords: BAT, WAT, Firefly luciferase, iRFP713, UCP1, Thermogenesis, Brownin

Multispectral optoacoustic tomography of lipid and hemoglobin contrast in human carotid atherosclerosis

Several imaging techniques aim at identifying features of carotid plaque instability but come with limitations, such as the use of contrast agents, long examination times and poor portability. Multispectral optoacoustic tomography (MSOT) employs light and sound to resolve lipid and hemoglobin content, both features associated with plaque instability, in a label-free, fast and highly portable way. Herein, 5 patients with carotid atherosclerosis, 5 healthy volunteers and 2 excised plaques, were scanned with handheld MSOT. Spectral unmixing allowed visualization of lipid and hemoglobin content within three ROIs: whole arterial cross-section, plaque and arterial lumen. Calculation of the fat-blood-ratio (FBR) value within the ROIs enabled the differentiation between patients and healthy volunteers (P = 0.001) and between plaque and lumen in patients (P = 0.04). Our results introduce MSOT as a tool for molecular imaging of human carotid atherosclerosis and open new possibilities for research and clinical assessment of carotid plaques

Skeletal muscle optoacoustics reveals patterns of circulatory function and oxygen metabolism during exercise

Imaging skeletal muscle function and metabolism, as reported by local hemodynamics and oxygen kinetics, can elucidate muscle performance, severity of an underlying disease or outcome of a treatment. Herein, we used multispectral optoacoustic tomography (MSOT) to image hemodynamics and oxygen kinetics within muscle during exercise. Four healthy volunteers underwent three different hand-grip exercise challenges (60s isometric, 120s intermittent isometric and 60s isotonic). During isometric contraction, MSOT showed a decrease of HbO2, Hb and total blood volume (TBV), followed by a prominent increase after the end of contraction. Corresponding hemodynamic behaviors were recorded during the intermittent isometric and isotonic exercises. A more detailed analysis of MSOT readouts revealed insights into arteriovenous oxygen differences and muscle oxygen consumption during all exercise schemes. These results demonstrate an excellent capability of visualizing both circulatory function and oxygen metabolism within skeletal muscle under exercise, with great potential implications for muscle research, including relevant disease diagnostics