Glandular architecture is currently the basis for the Gleason grading of prostate biopsies. To visualize and computationally analyze glandular features in large 3D pathology datasets, we developed an annotation-free segmentation method for 3D prostate glands that relies upon synthetic 3D immunofluorescence (IF) enabled by generative adversarial networks. By using a fluorescent analog of H and E (cheap and fast stain) as an input, our strategy allows for accurate glandular segmentation that does not rely upon subjective and tedious human annotations or slow and expensive 3D immunolabeling. We aim to demonstrate that this 3D segmentation will enable improved prostate cancer prognostication.
Open-top light-sheet (OTLS) microscopy has recently been developed as a high-throughput, easy-to-use 3D microscopy technique for large specimens [1-2]. The oblique angle of the optical beams relative to the sample plate introduces challenges however, and previous solutions (such as a solid immersion lens) have been limiting [1]. Therefore we have developed a solid immersion meniscus lens (SIMlens), which enables optical beams to transition from air to a higher-index immersion medium without introducing aberrations [3]. A SIMlens is compatible with a turret of air objectives, enabling efficient multi-resolution workflows [4]. We present the first multi-resolution OTLS microscope, enabled by a SIMlens.
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