A Process Model for Direct Correlation between Computed Tomography and Histopathology

Abstract

Rationale and Objectives Multimodal imaging techniques for capturing normal and diseased human anatomy and physiology are being developed to benefit patient clinical care, research, and education. In the past, the incorporation of histopathology into these multimodal datasets has been complicated by the large differences in image quality, content, and spatial association. Materials and Methods We have developed a novel system, the large-scale image microtome array (LIMA), to bridge the gap between nonstructurally destructive and destructive imaging such that reliable registration between radiological data and histopathology can be achieved. Registration algorithms have been designed to align the multimodal datasets, which include computed tomography, computed micro-tomography, LIMA, and histopathology data to a common coordinate system. Results The resulting volumetric dataset provides an abundance of valuable information relating to the tissue sample including density, anatomical structure, color, texture, and cellular information in three dimensions. An image processing pipeline has been established to register all the multimodal data to a common coordinate system. Conclusion In this study, we have chosen to use human lung cancer nodules as an example; however, the flexibility of the image acquisition and subsequent processing algorithms makes it applicable to any soft organ tissue. A novel process model has been established to generate cross registered multimodal datasets for the investigation of human lung cancer nodule content and associated image-based representation.

An Automated Segmentation Approach for Highlighting the Histological Complexity of Human Lung Cancer

“…Of these 11 lobectomy specimens all were found to contain cancerous nodules, seven were adenocarcinomas, three squamous cell carcinomas, and one neuroendocrine carcinoma. The excised lobes were processed as previously described by Sieren et al 21 In brief, each lobectomy specimen was cannulated through the main airway and inflation-fixed using a modified Heitzmen technique. Following fixation the nodules were isolated from the surrounding lobe via gross dissection.…”

“…Due to the unique imaging and sectioning process of the LIMA system coupled with the fixation process, the LIMA datasets contain no significant deformation of the tissue structure and can be used as a reliable basis to correct for the distorted histology data. Each digitized histology section image was non-rigidly registered via a landmark driven thin plate spline algorithm to the corresponding LIMA image 21. Thus any disruption to the structure caused by histological processing was corrected, restoring the 3D relationship between individual histology sections 21…”

“…An extensive process model for the collection, manipulation, and image acquisition of volumetric multi-modality datasets of lung nodules has been developed 21. A challenge with the designed process model was the time-consuming and subjective task of requiring expert pathologists to manually segment the tissue types of interest in the histology datasets.…”

An alternative approach to histopathological validation of PET imaging for radiation therapy image-guidance: A proof of concept

“…Different tissue mounting jigs can be built for different tumour sizes using readily available materials. While there are institutions with highly specialised infrastructure for 3D tissue reconstruction [24], the method presented in this study meets the demands of the validation goal: to obtain a 3D intratumoural distribution of a PET tracer that can be used to generate sPET images precisely registered to histopathological data. The 3D distribution of FDG uptake, as obtained in this study, demonstrated good spatial correlation between neighbouring slices, even though a non-negligible interslice distance was utilised.…”

Comprehensive Approach to Coregistration of Autoradiography and Microscopy Images Acquired from a Set of Sequential Tissue Sections

“…Specifically, for each image pair, the total number of marker dots visible around the tissue was randomly split into a registration landmark set and a measurement landmark set. The rigid transformation was based on the optimal alignment of the registration set, which always contained only 4 points; the rest of the landmarks served as a measurement point set (22). To evaluate the rigid registration error, the displacements between the weighted centers of mass of corresponding points in the measurement point set were recorded after registration, for all the used sets of images (8 tumor models).…”