Multi-focus image fusion using epifluorescence microscopy for robust vascular segmentation

Multi-focus Image Fusion for Confocal Microscopy Using U-Net Regression Map

Characterizing the spatial relationship between blood vessel and lymphatic vascular structures, in the mice dura mater tissue, is useful for modeling fluid flows and changes in dynamics in various disease processes. We propose a new deep learning-based approach to fuse a set of multi-channel single-focus microscopy images within each volumetric z-stack into a single fused image that accurately captures as much of the vascular structures as possible. The red spectral channel captures small blood vessels and the green fluorescence channel images lymphatics structures in the intact dura mater attached to bone. The deep architecture Multi-Channel Fusion U-Net (MCFU-Net) combines multi-slice regression likelihood maps of thin linear structures using max pooling for each channel independently to estimate a slice-based focus selection map. We compare MCFU-Net with a widely used derivative-based multi-scale Hessian fusion method [8]. The multi-scale Hessian-based fusion produces dark-halos, non-homogeneous backgrounds and less detailed anatomical structures. Perception based no-reference image quality assessment metrics PIQUE, NIQE, and BRISQUE confirm the effectiveness of the proposed method.

Complex Non-sinus-associated Pachymeningeal Lymphatic Structures: Interrelationship With Blood Microvasculature

The contribution of cranial dura mater vascular networks, as means for maintaining brain fluid movement and balance, and as the source of significant initiators and/or contributors to neurological disorders, has been overlooked. These networks consist of both blood and lymphatic vessels. The latter were discovered recently and described as sinus-associated structures thus changing the old paradigm that central nervous system lacks lymphatics. In this study, using markers specific to blood and lymphatic endothelia, we demonstrate the existence of the complex non-sinus-associated pachymeningeal lymphatic vasculature. We further show the interrelationship and possible connections between lymphatic vessels and the dural blood circulatory system. Our novel findings reveal the presence of lymphatic-like structures that exist on their own and/or in close proximity to microvessels. Of particular interest are sub-sets of vascular complexes with dual (lymphatic and blood) vessel identity representing a unique microenvironment within the cranial dura. The close association of the systemic blood circulation and meningeal lymphatics achieved in these complexes could facilitate fluid exchange between the two compartments and constitute an alternative route for CSF drainage.

Estrogen-Dependent Changes in Dura Mater Microvasculature Add New Insights to the Pathogenesis of Headache

The pathogenesis of headaches is a matter of ongoing discussion of two major theories describing it either as a vascular phenomenon resulting from vasodilation or primarily as a neurogenic process accompanied by secondary vasodilation associated with sterile neurogenic inflammation. While summarizing current views on neurogenic and vascular origins of headache, this mini review adds new insights regarding how smooth muscle-free microvascular networks, discovered within dura mater connective tissue stroma (previously thought to be “avascular”), may become a site of initial insult generating the background for the development of headache. Deficiencies in estrogen-dependent control of microvascular integrity leading to plasma protein extravasation, potential activation of perivascular and connective tissue stroma nociceptive neurons, and triggering of inflammatory responses are described. Finally, possible avenues for controlling and preventing these pathophysiological changes are discussed.

Confocal Vessel Structure Segmentation with Optimized Feature Bank and Random Forests

In this paper, we consider confocal microscopy based vessel segmentation with optimized features and random forest classification. By utilizing multi-scale vessel-specific features tuned to capture curvilinear structures such as Frobenius norm of the Hessian eigenvalues, Laplacian of Gaussians (LoG), oriented second derivative, line detector and intensity masked with LoG scale map. we obtain better segmentation results in challenging imaging conditions. We obtain binary segmentations using random forest classifier trained on physiologists marked ground-truth. Experimental results on mice dura mater confocal microscopy vessel segmentations indicate that we obtain better results compared to global segmentation approaches.

Multiquadric Spline-Based Interactive Segmentation of Vascular Networks

Commonly used drawing tools for interactive image segmentation and labeling include active contours or boundaries, scribbles, rectangles and other shapes. Thin vessel shapes in images of vascular networks are difficult to segment using automatic or interactive methods. This paper introduces the novel use of a sparse set of user-defined seed points (supervised labels) for precisely, quickly and robustly segmenting complex biomedical images. A multiquadric spline-based binary classifier is proposed as a unique approach for interactive segmentation using as features color values and the location of seed points. Epifluorescence imagery of the dura mater microvasculature are difficult to segment for quantitative applications due to challenging tissue preparation, imaging conditions, and thin, faint structures. Experimental results based on twenty epifluorescence images is used to illustrate the benefits of using a set of seed points to obtain fast and accurate interactive segmentation compared to four interactive and automatic segmentation approaches.

Random Forests for Dura Mater Microvasculature Segmentation Using Epifluorescence Images

Automatic segmentation of microvascular structures is a critical step in quantitatively characterizing vessel remodeling and other physiological changes in the dura mater or other tissues. We developed a supervised random forest (RF) classifier for segmenting thin vessel structures using multiscale features based on Hessian, oriented second derivatives, Laplacian of Gaussian and line features. The latter multiscale line detector feature helps in detecting and connecting faint vessel structures that would otherwise be missed. Experimental results on epifluorescence imagery show that the RF approach produces foreground vessel regions that are almost 20 and 25 percent better than Niblack and Otsu threshold-based segmentations respectively.

MULTISCALE TENSOR ANISOTROPIC FILTERING OF FLUORESCENCE MICROSCOPY FOR DENOISING MICROVASCULATURE

Fluorescence microscopy images are contaminated by noise and improving image quality without blurring vascular structures by filtering is an important step in automatic image analysis. The application of interest here is to automatically extract the structural components of the microvascular system with accuracy from images acquired by fluorescence microscopy. A robust denoising process is necessary in order to extract accurate vascular morphology information. For this purpose, we propose a multiscale tensor with anisotropic diffusion model which progressively and adaptively updates the amount of smoothing while preserving vessel boundaries accurately. Based on a coherency enhancing flow with planar confidence measure and fused 3D structure information, our method integrates multiple scales for microvasculature preservation and noise removal membrane structures. Experimental results on simulated synthetic images and epifluorescence images show the advantage of our improvement over other related diffusion filters. We further show that the proposed multiscale integration approach improves denoising accuracy of different tensor diffusion methods to obtain better microvasculature segmentation.