Advances in computational image analysis and machine learning in the field of digital pathology seem poised to make transformational changes in disease diagnosis and prognosis, yet currently 99% of all histopathology slides generated in clinical practice are never digitized. We seek to incorporate digital imaging into the standard glass-slide clinical pathology workflow using relatively inexpensive modifications to traditional clinical microscopes. Here we will introduce PathCAM, a pathology computer-assisted microscope for "ambient" digitization of histology slides during the standard pathology workflow. PathCAM leverages high-frequency spatiotemporal sampling and human-machine interaction to deliver complete and accurate digital records of clinical glass-slide analysis. We will discuss the development of the PathCAM system and technical approach, and demonstrate the unique datasets and capabilities afforded by this hybrid digital-analog pathology imaging and analysis platform.
Diagnoses performed on the basis of histopathological evaluation depend on the premise that information derived from a small number of samples is valid for the entire tissue volume. By insufficiently sampling a biopsy volume the ability of pathologists to draw meaningful inferences from the sample is impeded. This work attempts to apply an information theoretic approach to biopsy sampling rates informed by variation in tissue morphology identified by persistent homology. By quantifying the diagnostic information present in a sample may be possible to prevent under sampling by the clinician by creating a “Nyquist limit" for histopathological sampling given the frequency of morphologically distinct regions in a single biopsy.
Prostate cancer comprises the second most common cancer in men. One of the most powerful and established prognostic indicators of adenocarcinoma of the prostate is the Gleason score, a subjective assessment of the pattern of tumor growth and extent of glandular differentiation in H&E stained histology slides. Despite being the most dominant prostate grading method in use, the Gleason score suffers from high variability between grading pathologists, and due to its 2D nature, fails to effectively capture potentially prognostic information contained in 3D glandular growth patterns. We have previously demonstrated that persistent homology, a subset of topological data analysis (TDA), is effective in generating a quantitative morphological descriptor capable of differentiating Gleason grade in 2D. By capturing glands as loops in 2D, and voids in 3D, persistent homology lends itself naturally to the assessment of 3D glandular growth patterns while maintaining a correspondence to their 2D analogue. Dual-view inverted selective plane illumination microscopy (diSPIM) with a fluorescent H&E analogue was leveraged for volumetric imaging of optically-cleared prostate biopsies. The two orthogonal views of the diSPIM system yielded isotropic resolution in all dimensions, facilitating reconstruction of tissue histology in 3D for quantitative morphological assessment by persistent homology. The use of a nuclei specific hematoxylin analog (DRAQ5), in addition to the isotropic resolution of the system, enabled accurate 3D nuclei segmentation, thereby facilitating application of persistent homology to the corresponding nuclei 3D point clouds. Through TDA a quantitative, reproducible descriptor for 3D prostate cancer morphology will be demonstrated.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.