This paper proposes a fast whole-organ histological imaging method with real-time staining and mechanical sectioning. Time-consuming and laborious sample processing procedures are not needed. The imaged tissue block will be labeled along with the serial sectioning and optical scanning to improve the overall speed and the uniformity of staining. A super-resolution network (ESRGAN) and an optical-sectioning imaging technique (HiLo microscopy) have been applied to optimize the imaging speed and resolution. The proposed system can realize whole-organ histological imaging within hours to days, depending on the volume of the imaged sample.
Abstract Histopathology based on formalin-fixed and paraffin-embedded tissues remains the gold standard for surgical margin assessment (SMA). However, routine pathological practice is lengthy and laborious, failing to provide immediate feedback to surgeons and pathologists for intraoperative decision-making. In this report, we propose a cost-effective and easy-to-use histological imaging method with speckle illumination microscopy (i.e., HiLo). HiLo can achieve rapid and non-destructive imaging of large and fluorescently-labelled resection tissues at an acquisition speed of 5 cm 2 /min with 1.3-μm lateral resolution and 5.8-μm axial resolution, demonstrating a great potential as an intraoperative SMA tool that can be used by surgeons and pathologists to detect residual tumors at surgical margins. It is experimentally validated that HiLo enables rapid diagnosis of different subtypes of human lung adenocarcinoma and hepatocellular carcinoma, producing images with remarkably recognizable cellular features comparable to the gold-standard histology. This work will facilitate the clinical translations of HiLo microscopy to improve the current standard-of-care.
Virtual histological staining technique with a label-free auto-fluorescence image as an input is a challenging scientific pursuit to visualize complicated biological structures with distinct features. Recently, most of the related methods follow the two-side image translation architecture to get rid of paired data restriction, which is necessary for unprocessed and thick tissue virtual histological staining style transformation. However, the associated cycle consistency loss will inevitably lead to huge calculation consumption and cannot address the problem of incorrect translation among intracellular and extracellular components, which we termed as incorrect staining. In this paper, we propose a novel and computational-efficient one-side image translation framework to transfer unstained auto-fluorescence images into virtual hematoxylin- and eosin-stained counterparts for both thin and thick human samples. To address the incorrect nuclear staining issue, we design a region-classification loss to incorporate partial supervision information. Experimental data on both thin and thick human lung samples are used to demonstrate that our method is computationally efficient while achieving a comparable transformation performance over the two-side framework.
Clinical histopathological analyses usually require hematoxylin-and eosin-(H&E) as regular staining to visualize various tissue types and morphological changes, whereas some special stains are also essential to provide auxiliary information on particular components. However, it is infeasible to simultaneously implement diverse histological staining on the same tissue section. In this paper, we propose a multiple histological staining model that enables arbitrary staining image generation from label-free autofluorescence images. We use AdaIN to fuse styles into the image reconstruction process for source image content preservation. Moreover, direct image match loss is proposed to replace image reconstruction loss. Experimental results on mouse kidney tissue demonstrate the efficiency and advantage of our model compared to the baseline frameworks. Furthermore, we also validated the superior performance of the proposed model using mouse liver and heart tissues, which confirms that our method is generally applicable to multiple organs.
Abstract Ultraviolet (UV) photoacoustic microscopy has attracted lots of attention since it can provide histological images for disease diagnosis without any tissue processing or staining, holding great potential for rapid histopathology in hospitals. However, sometimes, the nuclear contrast in the images is relatively low due to the high UV absorption of various surrounding biomolecules (e.g., heme, myoglobin, lipids, etc.), resulting in low diagnostic accuracy. Here, a label‐free dual‐modality imaging system with ultraviolet photoacoustic and auto‐fluorescence (PAAF) microscopy is proposed, which can obtain photon absorption‐induced PA and AF images simultaneously using only one UV pulsed laser. With the opposite contrast acquired in the PA and AF images and the image fusion technique, this proposed PAAF microscopy enables high‐contrast and high‐sensitivity histological imaging for various tissues, even under a low excitation energy of 0.7 nJ and a high pulse‐to‐pulse energy fluctuation of ≈30%. Mouse brain, kidney, liver, lung, and human lung tissues processed by different clinical protocols have been imaged to demonstrate the versatility of PAAF microscopy, showing its promising applications in surgical pathology.
Surgical margin analysis (SMA), an essential procedure to confirm the complete excision of cancerous tissue in tumor resection surgery, requires intraoperative diagnostic tools to avoid repeated surgeries due to a positive surgical margin. Recently, by taking the advantage of the high intrinsic optical absorption of DNA/RNA at 266 nm wavelength, ultraviolet photoacoustic microscopy (UV-PAM) has been developed to provide high-resolution histological images without labeling, showing great promise as an intraoperative tool for SMA. To enable the development of UV-PAM for SMA, here, a high-speed and open-top UV-PAM system is presented, which can be operated similarly to conventional optical microscopies. The UV-PAM system provides a high lateral resolution of 1.2 µm, and a high imaging speed of 55 kHz A-line rate with one-axis galvanometer mirror scanning. Moreover, to ensure UV-PAM images can be easily interpreted by pathologists without additional training, the original grayscale UV-PAM images are virtually stained by a deep-learning algorithm to mimic the standard hematoxylin- and eosin-stained images, enabling training-free histological analysis. Mouse brain slice imaging is performed to demonstrate the high performance of the open-top UV-PAM system, illustrating its great potential for SMA applications.
Slide-free imaging techniques have shown great promise in improving the histological workflow. For example, computational high-throughput autofluorescence microscopy by pattern illumination (CHAMP) has achieved high resolution with a long depth of field, which, however, requires a costly ultraviolet laser. Here, simply using a low-cost light-emitting diode (LED), we propose a deep learning-assisted framework of enhanced widefield microscopy, termed EW-LED, to generate results similar to CHAMP (the learning target). Comparing EW-LED and CHAMP, EW-LED reduces the cost by 85×, shortening the image acquisition time and computation time by 36× and 17×, respectively. This framework can be applied to other imaging modalities, enhancing widefield images for better virtual histology.
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. Our results show that the UV-PAM imaging contrast and quality can be significantly improved after tissue clearing. With the cleared tissue, multilayers of cell nuclei can also be extracted from time-resolved PA signals. Tissue clearing-enhanced UV-PAM can provide fine details for organ imaging.
'/%3e%3cuse xlink:href='%23a' transform='rotate(144 450 300)'/%3e%3cuse xlink:href='%23a' transform='rotate(216 450 300)'/%3e%3cuse xlink:href='%23a' transform='rotate(288 450 300)'/%3e%3c/svg%3e)
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)