A Weakly Supervised Deep Generative Model for Complex Image Restoration
and Style Transformation
Abstract
The datasets for transforming autofluorescence images to the
histochemically stained images were acquired from a human breast biopsy
tissue and a human liver cancer tissue. Tissues of breast cancer and
liver cancer were extracted surgically or through tissue biopsy. The
tissues were formalin-fixed and paraffin-embedded (FFPE). Thin tissue
slices, with a thickness of 4 µm, were sectioned and placed on a quartz
slide. The tissue slices were deparaffined prior to imaging. The
autofluorescence images were acquired from a wide-field inverted
microscope equipped with a 10X/0.3 numerical aperture (NA) objective
lens (Plan Fluorite, Olympus Corp.), an infinity-corrected tube lens
(TTL-180-A, Thorlabs Inc.), and a monochrome scientific complementary
metal-oxide-semiconductor camera (pco.panda 4.2, PCO. Inc.). A deep
ultraviolet light-emitting diode of 265 nm (M265L4, Thorlabs Inc.) was
used as an excitation light source because of its high absorption in
cell nuclei [28], consequently providing high nuclear contrast
without labels [29]. After acquiring the autofluorescence image, the
same slide was stained with H&E and its bright-field images were
captured using a whole-slide scanner equipped with a 20X/0.75 NA
objective lens (NanoZoomer-SQ, Hamamatsu Photonics K.K). All human
experiments were carried out in conformity with a clinical research
ethics review approved by the Institutional Review Board of the Chinese
University of Hong Kong/ New Territories East Cluster (reference number:
2021.597).