Super-resolution of biomedical volumes with 2D supervision
Cheng Jiang Alexander Gedeon Yiwei Lyu Eric Landgraf Yufeng Zhang Xinhai Hou Akhil Kondepudi Asadur Chowdury Honglak Lee Todd Hollon
Volumetric biomedical microscopy has the potential to increase the diagnostic
information extracted from clinical tissue specimens and improve the diagnostic
accuracy of both human pathologists and computational pathology models.
Unfortunately, barriers to integrating 3-dimensional (3D) volumetric microscopy
into clinical medicine include long imaging times, poor depth / z-axis
resolution, and an insufficient amount of high-quality volumetric data.
Leveraging the abundance of high-resolution 2D microscopy data, we introduce
masked slice diffusion for super-resolution (MSDSR), which exploits the
inherent equivalence in the data-generating distribution across all spatial
dimensions of biological specimens. This intrinsic characteristic allows for
super-resolution models trained on high-resolution images from one plane (e.g.,
XY) to effectively generalize to others (XZ, YZ), overcoming the traditional
dependency on orientation. We focus on the application of MSDSR to stimulated
Raman histology (SRH), an optical imaging modality for biological specimen
analysis and intraoperative diagnosis, characterized by its rapid acquisition
of high-resolution 2D images but slow and costly optical z-sectioning. To
evaluate MSDSR's efficacy, we introduce a new performance metric, SliceFID, and
demonstrate MSDSR's superior performance over baseline models through extensive
evaluations. Our findings reveal that MSDSR not only significantly enhances the
quality and resolution of 3D volumetric data, but also addresses major
obstacles hindering the broader application of 3D volumetric microscopy in
clinical diagnostics and biomedical research.