With the progression of diseases, modified cell–matrix interactions have major effects not only upon key cellular functions but also upon the structure of extracellular matrix and vasculature, which are two of the most prevalent fiber‐like structures in biological tissues. Unfortunately, quantitative approaches to assessing these structural changes are lacking. Herein, a multiparametric imaging system is established to resolve subtle organizational changes of collagen fibers and vasculature in disease progression. The pixel‐wise, automated waviness (paWav) is developed as a novel biomarker, and a multimodal analysis system combining paWav with orientation and alignment assessments is constructed. Aggregation‐induced emission luminogens (AIEgens) with second near‐infrared excitation or emission are developed for in vivo deep‐penetration vasculature imaging. The organization remodeling of cortical blood vessels in stroke in marmosets is quantitatively characterized using biologically excretable AIE dots that highlight the clinical translation potential, and a distance dependence law in vessel morphological remodeling is identified. Finally, the multiparametric analysis relying completely on collagen fiber signatures successfully differentiates cancerous from normal pancreatic tissues using a predictive classification approach. Collectively, the combined use of these structural changes in fibrillar tissue components may enable a better understanding of cell–matrix interactions in pathogenesis and identification of new potential treatment targets.

Ovarian cancer has the highest mortality rate among all gynecological cancers, containing complicated heterogeneous histotypes, each with different treatment plans and prognoses. The lack of screening test makes new perspectives for the biomarker of ovarian cancer of great significance. As the main component of extracellular matrix, collagen fibers undergo dynamic remodeling caused by neoplastic activity. Second harmonic generation (SHG) enables label-free, non-destructive imaging of collagen fibers with submicron resolution and deep sectioning. In this study, we developed a new metric named local coverage to quantify morphologically localized distribution of collagen fibers and combined it with overall density to characterize 3D SHG images of collagen fibers from normal, benign and malignant human ovarian biopsies. An overall diagnosis accuracy of 96.3% in distinguishing these tissue types made local and overall density signatures a sensitive biomarker of tumor progression. Quantitative, multi-parametric SHG imaging might serve as a potential screening test tool for ovarian cancer.

. Significance Deep-imaging of cerebral vessels and accurate organizational characterization are vital to understanding the relationship between tissue structure and function. Aim We aim at large-depth imaging of the mouse brain vessels based on aggregation-induced emission luminogens (AIEgens), and we create a new algorithm to characterize the spatial orientation adaptively with superior accuracy. Approach Assisted by AIEgens with near-infrared-II excitation, three-photon fluorescence (3PF) images of large-depth cerebral blood vessels are captured. A window optimizing (WO) method is developed for highly accurate, automated 2D/3D orientation determination. The application of this system is demonstrated by establishing the orientational architecture of mouse cerebrovasculature down to the millimeter-level depth. Results The WO method is proved to have significantly higher accuracy in both 2D and 3D cases than the method with a fixed window size. Depth- and diameter-dependent orientation information is acquired based on in vivo 3PF imaging and the WO analysis of cerebral vessel images with a penetration depth of in mice. Conclusions We built an imaging and analysis system for cerebrovasculature that is conducive to applications in neuroscience and clinical fields.

Significance: Rapid diagnosis and analysis of human keloid scar tissues in an automated manner are essential for understanding pathogenesis and formulating treatment solutions.Aim: Our aim is to resolve the features of the extracellular matrix in human keloid scar tissues automatically for accurate diagnosis with the aid of machine learning.Approach: Multiphoton microscopy was utilized to acquire images of collagen and elastin fibers. Morphological features, histogram, and gray-level co-occurrence matrix-based texture features were obtained to produce a total of 28 features. The minimum redundancy maximum relevancy feature selection approach was implemented to rank these features and establish feature subsets, each of which was employed to build a machine learning model through the tree-based pipeline optimization tool (TPOT).Results: The feature importance ranking was obtained, and 28 feature subsets were acquired by incremental feature selection. The subset with the top 23 features was identified as the most accurate. Then stochastic gradient descent classifier optimized by the TPOT was generated with an accuracy of 96.15% in classifying normal, scar, and adjacent tissues. The area under curve of the classification results (scar versus normal and adjacent, normal versus scar and adjacent, and adjacent versus normal and scar) was 1.0, 1.0, and 0.99, respectively. Conclusions:The proposed approach has great potential for future dermatological clinical diagnosis and analysis and holds promise for the development of computer-aided systems to assist dermatologists in diagnosis and treatment.