Liver whole slide image analysis for 3D vessel reconstruction

The emergence of digital pathology has enabled numerous quantitative analyses of histopathology structures. However, most pathology image analyses are limited to two-dimensional datasets, resulting in substantial information loss and incomplete interpretation. To address this, we have developed a complete framework for three-dimensional whole slide image analysis and demonstrated its efficacy on 3D vessel structure analysis with liver tissue sections. The proposed workflow includes components on image registration, vessel segmentation, vessel cross-section association, object interpolation, and volumetric rendering. For 3D vessel reconstruction, a cost function is formulated based on shape descriptors, spatial similarity and trajectory smoothness by taking into account four vessel association scenarios. An efficient entropy-based Relaxed Integer Programming (eRIP) method is proposed to identify the optimal inter-frame vessel associations. The reconstructed 3D vessels are both quantitatively and qualitatively validated. Evaluation results demonstrate high efficiency and accuracy of the proposed method, suggesting its promise to support further 3D vessel analysis with whole slide images

A framework for 3D vessel analysis using whole slide images of liver tissue sections

Three-dimensional (3D) high resolution microscopic images have high potential for improving the understanding of both normal and disease processes where structural changes or spatial relationship of disease features are significant. In this paper, we develop a complete framework applicable to 3D pathology analytical imaging, with an application to whole slide images of sequential liver slices for 3D vessel structure analysis. The analysis workflow consists of image registration, segmentation, vessel cross-section association, interpolation, and volumetric rendering. To identify biologically-meaningful correspondence across adjacent slides, we formulate a similarity function for four association cases. The optimal solution is then obtained by constrained Integer Programming. We quantitatively and qualitatively compare our vessel reconstruction results with human annotations. Validation results indicate a satisfactory concordance as measured both by region-based and distance-based metrics. These results demonstrate a promising 3D vessel analysis framework for whole slide images of liver tissue sections

A 3D Primary Vessel Reconstruction Framework with Serial Microscopy Images

Three dimensional microscopy images present significant potential to enhance biomedical studies. This paper presents an automated method for quantitative analysis of 3D primary vessel structures with histology whole slide images. With registered microscopy images, we identify primary vessels with an improved variational level set framework at each 2D slide. We propose a Vessel Directed Fitting Energy (VDFE) to provide prior information on vessel wall probability in an energy minimization paradigm. We find the optimal vessel cross-section associations along the image sequence with a two-stage procedure. Vessel mappings are first found between each pair of adjacent slides with a similarity function for four association cases. These bi-slide vessel components are further linked by Bayesian Maximum A Posteriori (MAP) estimation where the posterior probability is modeled as a Markov chain. The efficacy of the proposed method is demonstrated with 54 whole slide microscopy images of sequential sections from a human liver

Serial optical coherence microscopy for label-free volumetric histopathology

The observation of histopathology using optical microscope is an essential procedure for examination of tissue biopsies or surgically excised specimens in biological and clinical laboratories. However, slide-based microscopic pathology is not suitable for visualizing the large-scale tissue and native 3D organ structure due to its sampling limitation and shallow imaging depth. Here, we demonstrate serial optical coherence microscopy (SOCM) technique that offers label-free, high-throughput, and large-volume imaging of ex vivo mouse organs. A 3D histopathology of whole mouse brain and kidney including blood vessel structure is reconstructed by deep tissue optical imaging in serial sectioning techniques. Our results demonstrate that SOCM has unique advantages as it can visualize both native 3D structures and quantitative regional volume without introduction of any contrast agents

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Automated Vascular Smooth Muscle Segmentation, Reconstruction, Classification and Simulation on Whole-Slide Histology

Histology of the microvasculature depicts detailed characteristics relevant to tissue perfusion. One important histologic feature is the smooth muscle component of the microvessel wall, which is responsible for controlling vessel caliber. Abnormalities can cause disease and organ failure, as seen in hypertensive retinopathy, diabetic ischemia, Alzheimer’s disease and improper cardiovascular development. However, assessments of smooth muscle cell content are conventionally performed on selected fields of view on 2D sections, which may lead to measurement bias. We have developed a software platform for automated (1) 3D vascular reconstruction, (2) detection and segmentation of muscularized microvessels, (3) classification of vascular subtypes, and (4) simulation of function through blood flow modeling. Vessels were stained for α-actin using 3,3\u27-Diaminobenzidine, assessing both normal (n=9 mice) and regenerated vasculature (n=5 at day 14, n=4 at day 28). 2D locally adaptive segmentation involved vessel detection, skeletonization, and fragment connection. 3D reconstruction was performed using our novel nucleus landmark-based registration. Arterioles and venules were categorized using supervised machine learning based on texture and morphometry. Simulation of blood flow for the normal and regenerated vasculature was performed at baseline and during demand based on the structural measures obtained from the above tools. Vessel medial area and vessel wall thickness were found to be greater in the normal vasculature as compared to the regenerated vasculature (p\u3c0.001) and a higher density of arterioles was found in the regenerated tissue (p\u3c0.05). Validation showed: a Dice coefficient of 0.88 (compared to manual) for the segmentations, a 3D reconstruction target registration error of 4 μm, and area under the receiver operator curve of 0.89 for vessel classification. We found 89% and 67% decreases in the blood flow through the network for the regenerated vasculature during increased oxygen demand as compared to the normal vasculature, respectively for 14 and 28 days post-ischemia. We developed a software platform for automated vasculature histology analysis involving 3D reconstruction, segmentation, and arteriole vs. venule classification. This advanced the knowledge of conventional histology sampling compared to whole slide analysis, the morphological and density differences in the regenerated vasculature, and the effect of the differences on blood flow and function

A Method for 3D Histopathology Reconstruction Supporting Mouse Microvasculature Analysis.

Structural abnormalities of the microvasculature can impair perfusion and function. Conventional histology provides good spatial resolution with which to evaluate the microvascular structure but affords no 3-dimensional information; this limitation could lead to misinterpretations of the complex microvessel network in health and disease. The objective of this study was to develop and evaluate an accurate, fully automated 3D histology reconstruction method to visualize the arterioles and venules within the mouse hind-limb. Sections of the tibialis anterior muscle from C57BL/J6 mice (both normal and subjected to femoral artery excision) were reconstructed using pairwise rigid and affine registrations of 5 µm-thick, paraffin-embedded serial sections digitized at 0.25 µm/pixel. Low-resolution intensity-based rigid registration was used to initialize the nucleus landmark-based registration, and conventional high-resolution intensity-based registration method. The affine nucleus landmark-based registration was developed in this work and was compared to the conventional affine high-resolution intensity-based registration method. Target registration errors were measured between adjacent tissue sections (pairwise error), as well as with respect to a 3D reference reconstruction (accumulated error, to capture propagation of error through the stack of sections). Accumulated error measures were lower (