Jessica de Ryk

Large image microscope array for the compilation of multimodality whole organ image databases

Three‐dimensional, structural and functional digital image databases have many applications in education, research, and clinical medicine. However, to date, apart from cryosectioning, there have been no reliable means to obtain whole‐organ, spatially conserving histology. Our aim was to generate a system capable of acquiring high‐resolution images, featuring microscopic detail that could still be spatially correlated to the whole organ. To fulfill these objectives required the construction of a system physically capable of creating very fine whole‐organ sections and collecting high‐magnification and resolution digital images. We therefore designed a large image microscope array (LIMA) to serially section and image entire unembedded organs while maintaining the structural integrity of the tissue. The LIMA consists of several integrated components: a novel large‐blade vibrating microtome, a 1.3 megapixel peltier cooled charge‐coupled device camera, a high‐magnification microscope, and a three axis gantry above the microtome. A custom control program was developed to automate the entire sectioning and automated raster‐scan imaging sequence. The system is capable of sectioning unembedded soft tissue down to a thickness of 40 μm at specimen dimensions of 200 × 300 mm to a total depth of 350 mm. The LIMA system has been tested on fixed lung from sheep and mice, resulting in large high‐quality image data sets, with minimal distinguishable disturbance in the delicate alveolar structures. Anat Rec 290:1377‐1387, 2007.

Dynamic in vivo Alveolar Morphology Using a Novel Laser Scanning Confocal Microscope

Understanding the structure and function of alveoli in vivo is crucial for understanding the normal and diseased lung. In this study we image the alveoli of mice in vivo, using a custom fiber optic catheter based laser scanning confocal microscope. Images obtained using this system are analyzed with an automated software application for alveolar size, wall thickness and alveolar number. Results show that direct dynamic visualization of alveoli and surrounding structures is possible in vivo, with high resolution. Early results indicate a high heterogeneity in alveolar structure in vivo, as opposed to an ordered uniform structure. Using the techniques presented in this study there is great promise for advancing our knowledge of the functional unit of the lung, the alveoli; for alveolar mechanics, cell traffic and 3D structural visualization.

A whole organ serial sectioning and imaging system for correlation of pathology to computer tomography

Randomly selected pathology sections of lung tissue are used to correlate lung pathology with Computer Tomography (CT) images. The randomly selected pathology sections provide physicians with little freedom to thoroughly investigate specific areas of interest as identified via CT images. A Large Image Microscope Array (LIMA) was designed to serially section and image entire organs for direct correlation between lung pathology and CT. The LIMA consists of a novel vibratome, capable of sectioning tissue down to a thickness of 40mm at specimen dimensions of 20cm by 30cm to a total depth of 30cm. A camera and a stereomicroscope, mounted on a XYZ gantry above the vibratome is moved through an automated raster scan to capture the entire surface area of the tissue via many high magnification images. A custom software program was developed to automate all hardware components. The alignment and stitching of the images is achieved though custom C++ code in conjunction with the Insight Segmentation and Registration Toolkit (ITK). The resulting high magnification, high-resolution pathology images are registered with corresponding CT images. Through point-to-point correlation between the two imaging techniques a pathological and CT ground truth may be established.

Three-dimensional histopathology of lung cancer with multimodality image registration

Identifying the three-dimensional content of non-small cell lung cancer tumors is a vital step in the pursuit of understanding cancer growth, development and response to treatment. The majority of non-small cell lung cancer tumors are histologically heterogeneous, and consist of the malignant tumor cells, necrotic tumor cells, fibroblastic stromal tissue, and inflammation. Geometric and tissue density heterogeneity are utilized in computed tomography (CT) representations of lung tumors for distinguishing between malignant and benign nodules. However, the correlation between radiolographical heterogeneity and corresponding histological content has been limited. In this study, a multimodality dataset of human lung cancer is established, enabling the direct comparison between histologically identified tissue content and micro-CT representation. Registration of these two datasets is achieved through the incorporation of a large scale, serial microscopy dataset. This dataset serves as the basis for the rigid and non-rigid registrations required to align the radiological and histological data. The resulting comprehensive, three-dimensional dataset includes radio-density, color and cellular content of a given lung tumor. Using the registered datasets, neural network classification is applied to determine a statistical separation between cancerous and non-cancerous tumor regions in micro-CT.

Three-dimensional visual truth of the normal airway tree for use as a quantitative comparison to micro-CT reconstructions

Mouse models are important for pulmonary research to gain insight into structure and function in normal and diseased states, thereby extending knowledge of human disease conditions. The flexibility of human disease induction into mice, due to their similar genome, along with their short gestation cycle makes mouse models highly suitable as investigative tools. Advancements in non-invasive imaging technology, with the development of micro-computed tomography (μ-CT), have aided representation of disease states in these small pulmonary system models. The generation ofμCT 3D airway reconstructions has to date provided a means to examine structural changes associated with disease. The degree of accuracy ofμCT is uncertain. Consequently, the reliability of quantitative measurements is questionable. We have developed a method of sectioning and imaging the whole mouse lung using the Large Image Microscope Array (LIMA) as the gold standard for comparison. Fixed normal mouse lungs were embedded in agarose and 250μm sections of tissue were removed while the remaining tissue block was imaged with a stereomicroscope. A complete dataset of the mouse lung was acquired in this fashion. Following planar image registration, the airways were manually segmented using an in-house built software program PASS. Amira was then used render the 3D isosurface from the segmentations. The resulting 3D model of the normal mouse airway tree developed from pathology images was then quantitatively assessed and used as the standard to compare the accuracy of structural measurements obtained from μ-CT.

Bright field segmentation tomography (BFST) for use as surface identification in stereomicroscopy

Stereomicroscopy is an important method for use in image acquisition because it provides a 3D image of an object when other microscopic techniques can only provide the image in 2D. One challenge that is being faced with this type of imaging is determining the top surface of a sample that has otherwise indistinguishable surface and planar characteristics. We have developed a system that creates oblique illumination and in conjunction with image processing, the top surface can be viewed. The BFST consists of the Leica MZ12 stereomicroscope with a unique attached lighting source. The lighting source consists of eight light emitting diodes (LEDs) that are separated by 45-degree angles. Each LED in this system illuminates with a 20-degree viewing angle once per cycle with a shadow over the rest of the sample. Subsequently, eight segmented images are taken per cycle. After the images are captured they are stacked through image addition to achieve the full field of view, and the surface is then easily identified. Image processing techniques, such as skeletonization can be used for further enhancement and measurement. With the use of BFST, advances can be made in detecting surface features from metals to tissue samples, such as in the analytical assessment of pulmonary emphysema using the technique of mean linear intercept.

Establishing multi-modality datasets with the incorporation of 3D histopathology for soft tissue classification

The development of multi-modality image analysis has gained increasing popularity over recent years. Multi-modality image databases are being developed to benefit patient clinical care, research and education. The incorporation of histopathology in these multi-modality datasets is complicated by the large differences in image quality, content and spatial association. We have developed a novel system, the large-scale image microtome array (LIMA), to bridge the gap between non-structurally destructive and destructive imaging such that reliable registration and incorporation of three-dimensional (3D) histopathology can be achieved. We have developed registration algorithms to align the micro-CT, LIMA and histopathology data to a common coordinate system. Using this multi-modality image dataset we have developed a classification algorithm to identify on a pixel basis, the tissue types present. The output from the classification processing is a 3D color coded map of tissue distributions. The resulting complete dataset provides an abundance of valuable information relating to the tissue sample including density, anatomical structure, color, texture and cellular information in three dimensions. In this study we have chosen to use normal and diseased lung tissue, however the flexibility of the image acquisition and subsequent processing algorithms makes it applicable to any soft organ tissue.