In this work, we propose an unsupervised module that combines wavelet-packet transform and k-means++ clustering to extract frequency features and classify patches of medical images. This module produces region labels for each patch-image, bypassing heavy computation and methodological labelling. Our WeCREST model, powered by this module, outperforms CycleGAN in terms of SSIM and PSNR, partly outperforms the supervised pix2pix, but underperforms compared to the state-of-the-art weakly supervised WeCREST. This improvement of the original WeCREST provides new insights into wavelet-based feature extraction and unsupervised region-style classification for medical images.
In this work, we propose a deep-UV LED-based imaging modality for generating AF images on human prostate paraffin sections. The achieved lateral resolution is around 1 µm. Moreover, a virtual staining framework is used to style transfer the AF images into clinical standard H&E images with an MS-SSIM > 0.65 between the virtually stained images and their corresponding ground truth images. The high similarity between the two domains is recognized by medical professionals, illustrating the high accuracy of the virtual staining algorithm and the clinical importance of this work for the post-operative histopathology workflow.
High-resolution and label-free histological imaging modalities provide cell nuclear contrast that is analogous to standard hematoxylin and eosin (H&E) histological staining. Recently, a rapid and slide-free imaging technique, termed computational high-throughput autofluorescence microscopy by pattern illumination (CHAMP), has been developed to image thick and unprocessed tissues with subcellular resolution. Here, we propose a fast and low-cost light-emitting diode (LED) based CHAMP imaging system assisted by deep learning. The low-resolution widefield LED images can be translated into high-resolution LED-CHAMP images that highly resemble Laser-CHAMP images by enhanced super-resolution generative adversarial networks (ESRGAN). Moreover, LED-CHAMP images can be further translated into virtual H&E-stained images comparable to standard H&E histology by virtual staining models. The versatility of LEDCHAMP is experimentally demonstrated using mouse brain thin slices and thick sections, which takes only five minutes for imaging tissue surface area with 10 × 10 mm2. The promising LED-CHAMP workflow enables fast, low-cost, and comparable image quality for intraoperative assessment.
In this work, a multiwavelength autofluorescence virtual instant stain (MAVIS) workflow is proposed to provide a multiple virtual staining solution to facilitate clinical diagnosis. Multiple ultraviolet excitation and visible emission wavelengths are used to highlight different biomolecules while a weakly supervised algorithm provides a robust and accurate virtual staining with adjacent tissue slices. The result of MAVIS with three histochemical stains on human tissue slices achieves a multi-scale structural similarity index measure > 0.6, demonstrating the potential of multiple virtual staining as a rapid and low-cost alternative to the current histological workflow.
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