Understanding metric-related pitfalls in image analysis validation.

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multistage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides a reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Although focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation.

Microvascular Network Remodeling in the Ischemic Mouse Brain Defined by Light Sheet Microscopy.

BACKGROUND: Until now, the analysis of microvascular networks in the reperfused ischemic brain has been limited due to tissue transparency challenges. METHODS: Using light sheet microscopy, we assessed microvascular network remodeling in the striatum from 3 hours to 56 days post-ischemia in 2 mouse models of transient middle cerebral artery occlusion lasting 20 or 40 minutes, resulting in mild ischemic brain injury or brain infarction, respectively. We also examined the effect of a clinically applicable S1P (sphingosine-1-phosphate) analog, FTY720 (fingolimod), on microvascular network remodeling. RESULTS: Over 56 days, we observed progressive microvascular degeneration in the reperfused striatum, that is, the lesion core, which was followed by robust angiogenesis after mild ischemic injury induced by 20-minute middle cerebral artery occlusion. However, more severe ischemic injury elicited by 40-minute middle cerebral artery occlusion resulted in incomplete microvascular remodeling. In both cases, microvascular networks did not return to their preischemic state but displayed a chronically altered pattern characterized by higher branching point density, shorter branches, higher unconnected branch density, and lower tortuosity, indicating enhanced network connectivity. FTY720 effectively increased microvascular length density, branching point density, and volume density in both models, indicating an angiogenic effect of this drug. CONCLUSIONS: Utilizing light sheet microscopy together with automated image analysis, we characterized microvascular remodeling in the ischemic lesion core in unprecedented detail. This technology will significantly advance our understanding of microvascular restorative processes and pave the way for novel treatment developments in the stroke field.

Asymmetric Ion-Pairing Photoredox Catalysis for Stereoselective Cationic Polymerization under Light Control.

By virtue of noninvasive regulations by light, photocontrolled polymerizations have attracted considerable attention for the precision synthesis of macromolecules. However, a cationic polymerization with simultaneous photocontrol and tacticity-regulation remains elusive so far. Herein, we introduce an asymmetric ion-pairing photoredox catalysis strategy that allows for the development of a stereoselective cationic polymerization with concurrent light regulation for the first time. By employing an ion pair catalyst (PC+/*A-) consisting of a photoredox active cation (PC+) and a sterically confined chiral anion (*A-) to deliver the stereochemical control, the cationic polymerization of vinyl ethers can be achieved with photocontrol and high isotactic selectivity (up to 91% m) at a remarkable low catalyst loading (50 ppm).

MMV_Im2Im: an open-source microscopy machine vision toolbox for image-to-image transformation.

Over the past decade, deep learning (DL) research in computer vision has been growing rapidly, with many advances in DL-based image analysis methods for biomedical problems. In this work, we introduce MMV_Im2Im, a new open-source Python package for image-to-image transformation in bioimaging applications. MMV_Im2Im is designed with a generic image-to-image transformation framework that can be used for a wide range of tasks, including semantic segmentation, instance segmentation, image restoration, image generation, and so on. Our implementation takes advantage of state-of-the-art machine learning engineering techniques, allowing researchers to focus on their research without worrying about engineering details. We demonstrate the effectiveness of MMV_Im2Im on more than 10 different biomedical problems, showcasing its general potentials and applicabilities. For computational biomedical researchers, MMV_Im2Im provides a starting point for developing new biomedical image analysis or machine learning algorithms, where they can either reuse the code in this package or fork and extend this package to facilitate the development of new methods. Experimental biomedical researchers can benefit from this work by gaining a comprehensive view of the image-to-image transformation concept through diversified examples and use cases. We hope this work can give the community inspirations on how DL-based image-to-image transformation can be integrated into the assay development process, enabling new biomedical studies that cannot be done only with traditional experimental assays. To help researchers get started, we have provided source code, documentation, and tutorials for MMV_Im2Im at [https://github.com/MMV-Lab/mmv_im2im] under MIT license.

Multisensory Extended Reality Applications Offer Benefits for Volumetric Biomedical Image Analysis in Research and Medicine.

3D data from high-resolution volumetric imaging is a central resource for diagnosis and treatment in modern medicine. While the fast development of AI enhances imaging and analysis, commonly used visualization methods lag far behind. Recent research used extended reality (XR) for perceiving 3D images with visual depth perception and touch but used restrictive haptic devices. While unrestricted touch benefits volumetric data examination, implementing natural haptic interaction with XR is challenging. The research question is whether a multisensory XR application with intuitive haptic interaction adds value and should be pursued. In a study, 24 experts for biomedical images in research and medicine explored 3D medical shapes with 3 applications: a multisensory virtual reality (VR) prototype using haptic gloves, a simple VR prototype using controllers, and a standard PC application. Results of standardized questionnaires showed no significant differences between all application types regarding usability and no significant difference between both VR applications regarding presence. Participants agreed to statements that VR visualizations provide better depth information, using the hands instead of controllers simplifies data exploration, the multisensory VR prototype allows intuitive data exploration, and it is beneficial over traditional data examination methods. While most participants mentioned manual interaction as the best aspect, they also found it the most improvable. We conclude that a multisensory XR application with improved manual interaction adds value for volumetric biomedical data examination. We will proceed with our open-source research project ISH3DE (Intuitive Stereoptic Haptic 3D Data Exploration) to serve medical education, therapeutic decisions, surgery preparations, or research data analysis.

Erratum to 'B4GALNT1 promotes hepatocellular carcinoma stemness and progression via integrin α2ß1-mediated FAK and AKT activation' (JHEP Reports 5 [2023] 100903).

[This corrects the article DOI: 10.1016/j.jhepr.2023.100903.].

Arterial specification precedes microvascular restitution in the peri-infarct cortex that is driven by small microvessels.

Evaluation of microvascular networks was impeded until recently by the need of histological tissue sectioning, which precluded 3D analyses. Using light-sheet microscopy, we investigated microvascular network characteristics in the peri-infarct cortex of mice 3-56 days after transient middle cerebral artery occlusion. In animal subgroups, the sphingosine-1-phosphate analog FTY720 (Fingolimod) was administered starting 24 hours post-ischemia. Light-sheet microscopy revealed a striking pattern of microvascular changes in the peri-infarct cortex, that is, a loss of microvessels, which was most prominent after 7 days and followed by the reappearance of microvessels over 56 days which revealed an increased branching point density and shortened branches. Using a novel AI-based image analysis algorithm we found that the length density of microvessels expressing the arterial specification marker α-smooth muscle actin markedly increased in the peri-infarct cortex already at 7 days post-ischemia. The length and branch density of small microvessels, but not of intermediate or large microvessels increased above pre-ischemic levels within 14-56 days. FTY720 increased the length and branch density of small microvessels. This study demonstrates long-term alterations of microvascular architecture post-ischemia indicative of increased collateralization most notably of small microvessels. Light-sheet microscopy will greatly advance the assessment of microvascular responses to restorative stroke therapies.

Colony context and size-dependent compensation mechanisms give rise to variations in nuclear growth trajectories.

To investigate the fundamental question of how cellular variations arise across spatiotemporal scales in a population of identical healthy cells, we focused on nuclear growth in hiPS cell colonies as a model system. We generated a 3D timelapse dataset of thousands of nuclei over multiple days, and developed open-source tools for image and data analysis and an interactive timelapse viewer for exploring quantitative features of nuclear size and shape. We performed a data-driven analysis of nuclear growth variations across timescales. We found that individual nuclear volume growth trajectories arise from short timescale variations attributable to their spatiotemporal context within the colony. We identified a strikingly time-invariant volume compensation relationship between nuclear growth duration and starting volume across the population. Notably, we discovered that inheritance plays a crucial role in determining these two key nuclear growth features while other growth features are determined by their spatiotemporal context and are not inherited.