Exceeding the limit for microscopic image translation with a deep learning-based unified framework.

Deep learning algorithms have been widely used in microscopic image translation. The corresponding data-driven models can be trained by supervised or unsupervised learning depending on the availability of paired data. However, general cases are where the data are only roughly paired such that supervised learning could be invalid due to data unalignment, and unsupervised learning would be less ideal as the roughly paired information is not utilized.

Lung Cancer Diagnosis on Virtual Histologically Stained Tissue Using Weakly Supervised Learning.

Lung adenocarcinoma (LUAD) is the most common primary lung cancer and accounts for 40% of all lung cancer cases. The current gold standard for lung cancer analysis is based on the pathologists' interpretation of hematoxylin and eosin (H&E)-stained tissue slices viewed under a brightfield microscope or a digital slide scanner. Computational pathology using deep learning has been proposed to detect lung cancer on histology images.

Translational rapid ultraviolet-excited sectioning tomography for whole-organ multicolor imaging with real-time molecular staining.

Rapid multicolor three-dimensional (3D) imaging for centimeter-scale specimens with subcellular resolution remains a challenging but captivating scientific pursuit. Here, we present a fast, cost-effective, and robust multicolor whole-organ 3D imaging method assisted with ultraviolet (UV) surface excitation and vibratomy-assisted sectioning, termed translational rapid ultraviolet-excited sectioning tomography (TRUST). With an inexpensive UV light-emitting diode (UV-LED) and a color camera, TRUST achieves widefield exogenous molecular-specific fluorescence and endogenous content-rich autofluorescence imaging simultaneously while preserving low system complexity and system cost.

Ultraviolet photoacoustic microscopy with tissue clearing for high-contrast histological imaging.

Ultraviolet photoacoustic microscopy (UV-PAM) has been investigated to provide label-free and registration-free volumetric histological images for whole organs, offering new insights into complex biological organs. However, because of the high UV absorption of lipids and pigments in tissue, UV-PAM suffers from low image contrast and shallow image depth, hindering its capability for revealing various microstructures in organs. To improve the UV-PAM imaging contrast and imaging depth, here we propose to implement a state-of-the-art optical clearing technique, CUBIC (clear, unobstructed brain/body imaging cocktails and computational analysis), to wash out the lipids and pigments from tissues.

Deep-learning-assisted microscopy with ultraviolet surface excitation for rapid slide-free histological imaging.

Histopathological examination of tissue sections is the gold standard for disease diagnosis. However, the conventional histopathology workflow requires lengthy and laborious sample preparation to obtain thin tissue slices, causing about a one-week delay to generate an accurate diagnostic report. Recently, microscopy with ultraviolet surface excitation (MUSE), a rapid and slide-free imaging technique, has been developed to image fresh and thick tissues with specific molecular contrast.