Guided interactive image segmentation using machine learning and color based data set clustering

We present a novel approach that combines machine learning based interactive image segmentation using supervoxels with a clustering method for the automated identification of similarly colored images in large data sets which enables a guided reuse of classifiers. Our approach solves the problem of significant color variability prevalent and often unavoidable in biological and medical images which typically leads to deteriorated segmentation and quantification accuracy thereby greatly reducing the necessary training effort. This increase in efficiency facilitates the quantification of much larger numbers of images thereby enabling interactive image analysis for recent new technological advances in high-throughput imaging. The presented methods are applicable for almost any image type and represent a useful tool for image analysis tasks in general

Guided interactive image segmentation using machine learning and color-based image set clustering

International audienceOver the last decades, image processing and analysis has become one of the key technologies in systems biology and medicine. The quantification of anatomical structures and dynamic processes in living systems is essential for understanding the complex underlying mechanisms and allows, i.a., the construction of spatio-temporal models that illuminate the interplay between architecture and function. Recently, deep learning significantly improved the performance of traditional image analysis in cases where imaging techniques provide large amounts of data. However, if only few images are available or qualified annotations are expensive to produce, the applicability of deep learning is still limited.We present a novel approach that combines machine learning based interactive image segmentation using supervoxels with a clustering method for the automated identification of similarly colored images in large image sets which enables a guided reuse of interactively trained classifiers. Our approach solves the problem of deteriorated segmentation and quantification accuracy when reusing trained classifiers which is due to significant color variability prevalent and often unavoidable in biological and medical images. This increase in efficiency improves the suitability of interactive segmentation for larger image sets, enabling efficient quantification or the rapid generation of training data for deep learning with minimal effort. The presented methods are applicable for almost any image type and represent a useful tool for image analysis tasks in general.The provided free software TiQuant makes the presented methods easily and readily usable and can be downloaded at tiquant.hoehme.com

Intravital Dynamic and Correlative Imaging of Mouse Livers Reveals Diffusion-Dominated Canalicular and Flow-Augmented Ductular Bile Flux

Background and Aims Small‐molecule flux in tissue microdomains is essential for organ function, but knowledge of this process is scant due to the lack of suitable methods. We developed two independent techniques that allow the quantification of advection (flow) and diffusion in individual bile canaliculi and in interlobular bile ducts of intact livers in living mice, namely fluorescence loss after photoactivation and intravital arbitrary region image correlation spectroscopy. Approach and Results The results challenge the prevailing “mechano‐osmotic” theory of canalicular bile flow. After active transport across hepatocyte membranes, bile acids are transported in the canaliculi primarily by diffusion. Only in the interlobular ducts is diffusion augmented by regulatable advection. Photoactivation of fluorescein bis‐(5‐carboxymethoxy‐2‐nitrobenzyl)‐ether in entire lobules demonstrated the establishment of diffusive gradients in the bile canalicular network and the sink function of interlobular ducts. In contrast to the bile canalicular network, vectorial transport was detected and quantified in the mesh of interlobular bile ducts. Conclusions The liver consists of a diffusion‐dominated canalicular domain, where hepatocytes secrete small molecules and generate a concentration gradient and a flow‐augmented ductular domain, where regulated water influx creates unidirectional advection that augments the diffusive flux

The influence of sleep on subjective well-being: An experience sampling study

Previous research has associated sleep with subjective well-being (SWB), but less is known about the underlying within-person processes. In the current study, we investigated how self-reported and actigraphy-measured sleep parameters (sleep onset latency, sleep duration, sleep satisfaction, social jetlag, and sleep efficiency) influence SWB (positive affect [PA], negative affect [NA], and life satisfaction [LS]) at the within- and between-person levels. Multilevel analyses of data from 109 university students who completed a 2-week experience sampling study revealed that higher within-person sleep satisfaction was a significant predictor of all three components of next day's SWB (ps < .005). Higher between-person sleep satisfaction was also related to higher levels of PA and LS (ps < .005), whereas shorter self-reported between-person sleep onset latency was associated with higher PA and LS, and lower NA (ps < .05). However, longer actigraphy-measured within-person sleep onset latency was associated with higher next day's LS (p = .028). When including within- and between-person sleep parameters into the same models predicting SWB, only within- and between-person sleep satisfaction remained a significant predictor of all components of SWB. Additionally, we found an effect of higher self-reported within-person sleep onset latency on PA and of shorter self-reported within-person sleep duration on LS (ps < .05). Our results indicate that the evaluative component of sleep-sleep satisfaction-is most consistently linked with SWB. Thus, sleep interventions that are successful in not only altering sleep patterns but also enhancing sleep satisfaction may stand a better chance at improving students' SWB. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Interruption of bile acid uptake by hepatocytes after acetaminophen overdose ameliorates hepatotoxicity.

Background & aimsAcetaminophen (APAP) overdose remains a frequent cause of acute liver failure, which is generally accompanied by increased levels of serum bile acids (BAs). However, the pathophysiological role of BAs remains elusive. Herein, we investigated the role of BAs in APAP-induced hepatotoxicity.MethodsWe performed intravital imaging to investigate BA transport in mice, quantified endogenous BA concentrations in the serum of mice and patients with APAP overdose, analyzed liver tissue and bile by mass spectrometry and MALDI-mass spectrometry imaging, assessed the integrity of the blood-bile barrier and the role of oxidative stress by immunostaining of tight junction proteins and intravital imaging of fluorescent markers, identified the intracellular cytotoxic concentrations of BAs, and performed interventions to block BA uptake from blood into hepatocytes.ResultsPrior to the onset of cell death, APAP overdose causes massive oxidative stress in the pericentral lobular zone, which coincided with a breach of the blood-bile barrier. Consequently, BAs leak from the bile canaliculi into the sinusoidal blood, which is then followed by their uptake into hepatocytes via the basolateral membrane, their secretion into canaliculi and repeated cycling. This, what we termed 'futile cycling' of BAs, led to increased intracellular BA concentrations that were high enough to cause hepatocyte death. Importantly, however, the interruption of BA re-uptake by pharmacological NTCP blockage using Myrcludex B and Oatp knockout strongly reduced APAP-induced hepatotoxicity.ConclusionsAPAP overdose induces a breach of the blood-bile barrier which leads to futile BA cycling that causes hepatocyte death. Prevention of BA cycling may represent a therapeutic option after APAP intoxication.Lay summaryOnly one drug, N-acetylcysteine, is approved for the treatment of acetaminophen overdose and it is only effective when given within ∼8 hours after ingestion. We identified a mechanism by which acetaminophen overdose causes an increase in bile acid concentrations (to above toxic thresholds) in hepatocytes. Blocking this mechanism prevented acetaminophen-induced hepatotoxicity in mice and evidence from patients suggests that this therapy may be effective for longer periods after ingestion compared to N-acetylcysteine

NASH limits anti-tumour surveillance in immunotherapy-treated HCC.

Hepatocellular carcinoma (HCC) can have viral or non-viral causes1-5. Non-alcoholic steatohepatitis (NASH) is an important driver of HCC. Immunotherapy has been approved for treating HCC, but biomarker-based stratification of patients for optimal response to therapy is an unmet need6,7. Here we report the progressive accumulation of exhausted, unconventionally activated CD8+PD1+ T cells in NASH-affected livers. In preclinical models of NASH-induced HCC, therapeutic immunotherapy targeted at programmed death-1 (PD1) expanded activated CD8+PD1+ T cells within tumours but did not lead to tumour regression, which indicates that tumour immune surveillance was impaired. When given prophylactically, anti-PD1 treatment led to an increase in the incidence of NASH-HCC and in the number and size of tumour nodules, which correlated with increased hepatic CD8+PD1+CXCR6+, TOX+, and TNF+ T cells. The increase in HCC triggered by anti-PD1 treatment was prevented by depletion of CD8+ T cells or TNF neutralization, suggesting that CD8+ T cells help to induce NASH-HCC, rather than invigorating or executing immune surveillance. We found similar phenotypic and functional profiles in hepatic CD8+PD1+ T cells from humans with NAFLD or NASH. A meta-analysis of three randomized phase III clinical trials that tested inhibitors of PDL1 (programmed death-ligand 1) or PD1 in more than 1,600 patients with advanced HCC revealed that immune therapy did not improve survival in patients with non-viral HCC. In two additional cohorts, patients with NASH-driven HCC who received anti-PD1 or anti-PDL1 treatment showed reduced overall survival compared to patients with other aetiologies. Collectively, these data show that non-viral HCC, and particularly NASH-HCC, might be less responsive to immunotherapy, probably owing to NASH-related aberrant T cell activation causing tissue damage that leads to impaired immune surveillance. Our data provide a rationale for stratification of patients with HCC according to underlying aetiology in studies of immunotherapy as a primary or adjuvant treatment

Creation of Three-dimensional Liver Tissue Models from Experimental Images for Systems Medicine

PubMed-listedInternational audienceIn this chapter, we illustrate how three-dimensional liver tissue models can be created from experimental image modalities by utilizing a well-established processing chain of experiments, microscopic imaging, image processing, image analysis and model construction. We describe how key features of liver tissue architecture are quantified and translated into model parameterizations, and show how a systematic iteration of experiments and model simulations often leads to a better understanding of biological phenomena in systems biology and systems medicine

TiQuant: software for tissue analysis, quantification and surface reconstruction

International audienceABSTRACTMotivation: TiQuant is a modular software tool for efficient quantificationof biological tissues based on volume data obtained by biomedicalimage modalities. It includes a number of versatile imageand volume processing chains tailored to the analysis of differenttissue types which have been experimentally verified. TiQuant implementsa novel method for the reconstruction of three-dimensionalsurfaces of biological systems, data that often cannot be obtainedexperimentally but which is of utmost importance for tissue modellingin systems biology.Availability: TiQuant is freely available for non-commercial use atmsysbio.com/tiquant. Windows, OSX and Linux are supported.Contact: [email protected] information: Supplementary data are available atBioinformatics online