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Thin section histology is limited in providing 3D structural information, particularly of the intricate morphology of the vasculature. Availability of high spatial resolution imaging for thick samples, would overcome the restriction dictated by low light penetration. Our study aimed at optimizing the procedure for efficient and affordable tissue clearing, along with an appropriate immunofluorescence labeling that will be applicable for high resolution imaging of blood and lymphatic vessels. The new procedure, termed whole organ blood and lymphatic vessels imaging (WOBLI), is based on two previously reported methods, CLARITY and ScaleA2. We used this procedure for the analysis of isolated whole ovary, uterus, lung and liver. These organs were subjected to passive clearing, following fixation, immunolabeling and embedding in hydrogel. Cleared specimens were immersed in ScaleA2 solution until transparency was achieved and imaged using light sheet microscopy. We demonstrate that WOBLI allows detailed analysis and generation of structural information of the lymphatic and blood vasculature from thick slices and more importantly, from whole organs. We conclude that WOBLI offers the advantages of morphology and fluorescence preservation with efficient clearing. Furthermore, WOBLI provides a robust, cost-effective method for generation of transparent specimens, allowing high resolution, 3D-imaging of blood and lymphatic vessels networks.

Segmentation of anatomical structures and particularly abdominal organs is a fundamental problem for quantitative image analysis in preclinical research. This paper presents a novel approach for whole body segmentation of small animals in a multimodal setting of MR, CT and optical imaging. The algorithm integrates multiple imaging sequences into a machine learning framework, which generates supervoxels by an efficient hierarchical agglomerative strategy and utilizes multiple SVM-kNN classifiers each constrained by a heatmap prior region to compose the segmentation. We demonstrate results showing segmentation of mice images into several structures including the heart, lungs, liver, kidneys, stomach, vena cava, bladder, tumor, and skeleton structures. Experimental validation on a large set of mice and organs, indicated that our system outperforms alternative state of the art approaches. The system proposed can be generalized to various tissues and imaging modalities to produce automatic atlas-free segmentation, thereby enabling a wide range of applications in preclinical studies of small animal imaging.