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We have developed a digital histology imaging system that has the potential to improve the accuracy of surgical margin assessment in the treatment of breast cancer by providing finer sampling and 3D visualization. The system is capable of producing a 3D representation of histopathology from an entire lumpectomy specimen. We acquire digital photomicrographs of a stack of large (120 x 170 mm) histology slides cut serially through the entire specimen. The images are then registered and displayed in 2D and 3D. This approach dramatically improves sampling and can improve visualization of tissue structures compared to current, small-format histology. The system consists of a brightfield microscope, adapted with a freeze-frame digital video camera and a large, motorized translation stage. The image of each slide is acquired as a mosaic of adjacent tiles, each tile representing one field-of-view of the microscope, and the mosaic is assembled into a seamless composite image. The assembly is done by a program developed to build image sets at six different levels within a multiresolution pyramid. A database-linked viewing program has been created to efficiently register and display the animated stack of images, which occupies about 80 GB of disk space per lumpectomy at full resolution, on a high-resolution (3840 x 2400 pixels) colour monitor. The scanning or tiling approach to digitization is inherently susceptible to two artefacts which disrupt the composite image, and which impose more stringent requirements on system performance. Although non-uniform illumination across any one isolated tile may not be discernible, the eye readily detects this non-uniformity when the entire assembly of tiles is viewed. The pattern is caused by deficiencies in optical alignment, spectrum of the light source, or camera corrections. The imaging task requires that features as small as 3.2 μm in extent be seamlessly preserved. However, inadequate accuracy in positioning of the translation stage produces visible discontinuities between adjacent features. Both of these effects can distract the viewer from the perception of diagnostically important features. Here we describe the system design and discuss methods for the correction of these artefacts. In addition, we outline our approach to rendering the processing and display of these large images computationally feasible.
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