Artificial intelligence (AI) presents an opportunity in anatomic pathology to provide quantitative objective support to a traditionally subjective discipline, thereby enhancing clinical workflows and enriching diagnostic capabilities. AI requires access to digitized pathology materials, which, at present, are most commonly generated from the glass slide using whole-slide imaging. Models are developed collaboratively or sourced externally, and best practices suggest validation with internal datasets most closely resembling the data expected in practice. Although an array of AI models that provide operational support for pathology practices or improve diagnostic quality and capabilities has been described, most of them can be categorized into one or more discrete types. However, their function in the pathology workflow can vary, as a single algorithm may be appropriate for screening and triage, diagnostic assistance, virtual second opinion, or other uses depending on how it is implemented and validated. Despite the clinical promise of AI, the barriers to adoption have been numerous, to which inclusion of new stakeholders and expansion of reimbursement opportunities may be among the most impactful solutions.
Digital screening and diagnosis from cytology slides can be aided by capturing multiple focal planes. However, using conventional methods, the large file sizes of high-resolution whole-slide images increase linearly with the number of focal planes acquired, leading to significant data storage and bandwidth requirements for the efficient storage and transfer of cytology virtual slides. We investigated whether a sequence of focal planes contained sufficient redundancy to efficiently compress virtual slides across focal planes by applying a commonly available video compression standard, high-efficiency video coding (HEVC). By developing an adaptive algorithm that applied compression to achieve a target image quality, we found that the compression ratio of HEVC exceeded that obtained using JPEG and JPEG2000 compression while maintaining a comparable level of image quality. These results suggest an alternative method for the efficient storage and transfer of whole-slide images that contain multiple focal planes, expanding the utility of this rapidly evolving imaging technology into cytology.
Innovative approaches in tissue imaging in an in vivo setting have included the use of optical coherence tomography (OCT) as a substrate for providing high resolution images at depths approaching 1.5 mm. This technology has offered the possibility of analyzing many tissues that are presently only evaluated using histologic methods after excision or biopsy. Despite the relatively high penetration depths of OCT, it is unclear whether the images acquired approximately 0.5 mm beyond the tissue surface maintain sufficient resolution and signal-to-noise ratio to provide useful information. Furthermore, there are relatively few studies that evaluate whether advanced image processing can be harnessed to improve the effective depth capabilities of OCT in tissue. We tested a tissue phantom designed to mimic the prostate as a model system, and independently modulated its refractive index and transmittance. Using dynamic focusing, and with the aid of an image analysis paradigm designed to improve signal detection in a model of tissue, we tested potential improvements in the ability to resolve structures at increasing penetration depths. We found that co-registered signal averaging and wavelet denoising improved overall image quality. B-spline interpolation made it possible to integrate dynamic focus images in a way that improved the effective penetration depth without significant loss in overall image quality. These results support the notion that image processing can refine OCT images for improved diagnostic capabilities to support in vivo microscopy.
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