检索结果-内蒙古大学图书馆

Multi-Channel Feature Extraction for Virtual Histological Staining of Photon Absorption Remote Sensing images

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Boktor, Marian Tweel, James E.D. Ecclestone, Benjamin R. Ye, Jennifer Ai Fieguth, Paul Reza, Parsin Haji PhotoMedicine Labs University of Waterloo 200 University Ave W WaterlooONN2L 3G1 Canada Vision and Image Processing Lab University of Waterloo 200 University Ave W WaterlooONN2L 3G1 Canada IllumiSonics Inc. 22 King Street South Suite 300 WaterlooONN2J 1N8 Canada

Accurate and fast histological staining is crucial in histopathology, impacting diagnostic precision and reliability. Traditional staining methods are time-consuming and subjective, causing delays in diagnosis. Digital pathology plays a vital role in advancing and optimizing histology processes to improve efficiency and reduce turnaround times. This study introduces a novel deep learning-based framework for virtual histological staining using photon absorption remote sensing (PARS) images. By extracting features from PARS time-resolved signals using a variant of the K-means method, valuable multi-modal information is captured. The proposed multi-channel cycleGAN (MC-GAN) model expands on the traditional cycleGAN framework, allowing the inclusion of additional features. Experimental results reveal that specific combinations of features outperform the conventional channels by improving the labeling of tissue structures prior to model training. Applied to human skin and mouse brain tissue, the results underscore the significance of choosing the optimal combination of features, as it reveals a substantial visual and quantitative concurrence between the virtually stained and the gold standard chemically stained hematoxylin and eosin (H&E) images, surpassing the performance of other feature combinations. Accurate virtual staining is valuable for reliable diagnostic information, aiding pathologists in disease classification, grading, and treatment planning. This study aims to advance label-free histological imaging and opens doors for intraoperative microscopy applications. © 2023, CC BY.

关键词： Grading

Automated Whole Slide Imaging for label-Free Histology using Photon Absorption Remote Sensing Microscopy

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Tweel, James E.D. Ecclestone, Benjamin R. Boktor, Marian Dinakaran, Deepak Mackey, John R. Reza, Parsin Haji PhotoMedicine Labs University of Waterloo 200 University Ave W WaterlooONN2L 3G1 Canada illumiSonics Inc. 22 King Street South Suite 300 WaterlooONN2J 1N8 Canada Vision and Image Processing Lab University of Waterloo 200 University Ave W WaterlooONN2L 3G1 Canada

The field of histology relies heavily on antiquated tissue processing and staining techniques that limit the efficiency of pathologic diagnoses of cancer and other diseases. Current staining and advanced labeling methods are often destructive and mutually incompatible, requiring new tissue sections for each stain. This prolongs the diagnostic process and depletes valuable biopsy samples. In this study, we present an alternative label-free histology platform using the first transmission-mode Photon Absorption Remote Sensing microscope. Optimized for automated whole slide scanning of unstained tissue samples, the system provides slide images at magnifications up to 40x that are fully compatible with existing digital pathology tools. The scans capture high quality and high-resolution images with subcellular diagnostic detail. After imaging, samples remain suitable for histochemical, immunohistochemical, and other staining techniques. Scattering and absorption (radiative and non-radiative) contrasts are shown in whole slide images of malignant human breast and skin tissues samples. Clinically relevant features are highlighted, and close correspondence and analogous contrast is demonstrated with one-to-one gold standard H&E stained images. Our previously reported pix2pix virtual staining model is applied to an entire whole slide image, showcasing the potential of this approach in whole slide label-free H&E emulation. This work is a critical advance for integrating label-free optical methods into standard histopathology workflows, both enhancing diagnostic efficiency, and broadening the number of stains that can be applied while preserving valuable tissue samples. © 2023, CC BY.

关键词： Histology

Virtual Histology with Photon Absorption Remote Sensing using a Cycle-Consistent Generative Adversarial Network with Weakly Registered Pairs

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Tweel, James E.D. Ecclestone, Benjamin R. Boktor, Marian Simmons, James Alexander Tummon Fieguth, Paul Reza, Parsin Haji PhotoMedicine Labs Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada IllumiSonics Inc. 22 King Street South Suite 300 WaterlooONN2J 1N8 Canada Vision and Image Processing Lab Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Modern histopathology relies on the microscopic examination of thin tissue sections stained with histochemical techniques, typically using brightfield or fluorescence microscopy. However, the staining of samples can permanently alter their chemistry and structure, meaning an individual tissue section must be prepared for each desired staining contrast. This not only consumes valuable tissue samples but also introduces delays in essential diagnostic timelines. In this work, virtual histochemical staining is developed using label-free photon absorption remote sensing (PARS) microscopy. We present a method that generates virtually stained histology images that are indistinguishable from the gold standard hematoxylin and eosin (H&E) staining. First, PARS label-free ultraviolet absorption images are captured directly within unstained tissue specimens. The radiative and non-radiative absorption images are then preprocessed, and virtually stained through the presented pathway. The preprocessing pipeline features a self-supervised Noise2Void denoising convolutional neural network (CNN) as well as a novel algorithm for pixel-level mechanical scanning error correction. These developments significantly enhance the recovery of sub-micron tissue structures, such as nucleoli location and chromatin distribution. Finally, we used a cycle-consistent generative adversarial network CycleGAN architecture to virtually stain the preprocessed PARS data. Virtual staining is applied to thin unstained sections of malignant human skin and breast tissue samples. Clinically relevant details are revealed, with comparable contrast and quality to gold standard H&E-stained images. This work represents a crucial step to deploying label-free microscopy as an alternative to standard histopathology techniques. © 2023, CC BY.

关键词： Fluorescence microscopy

AEGIS: A real-time multimodal augmented reality computer vision based system to assist facial expression recognition for individuals with autism spectrum disorder

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Hou Lee, James Ren Mr. Wong, Alexander University of Waterloo Canada Vision and Image Processing Lab

FactorizeNet: Progressive depth factorization for efficient network architecture exploration under quantization constraints

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Yun, Stone Wong, Alexander University of Waterloo Vision and Image Processing Lab Canada University of Waterloo Waterloo Artificial Intelligence Institute Canada

Depth factorization and quantization have emerged as two of the principal strategies for designing efficient deep convolutional neural network (CNN) architectures tailored for low-power inference on the edge. However, there is still little detailed understanding of how different depth factorization choices affect the final, trained distributions of each layer in a CNN, particularly in the situation of quantized weights and activations. In this study, we introduce a progressive depth factorization strategy for efficient CNN architecture exploration under quantization constraints. By algorithmically increasing the granularity of depth factorization in a progressive manner, the proposed strategy enables a fine-grained, low-level analysis of layer-wise distributions. Thus enabling the gain of in-depth, layer-level insights on efficiency-accuracy tradeoffs under fixed-precision quantization. Such a progressive depth factorization strategy also enables efficient identification of the optimal depth-factorized macroarchitecture design (which we will refer to here as FactorizeNet) based on the desired efficiency-accuracy requirements. © 2020, CC-BY.

关键词： Factorization

Time-domain feature extraction for target-specificity in Photoacoustic Remote Sensing Microscopy

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Pellegrino, Nicholas Ecclestone, Benjamin R. Fieguth, Paul Reza, Parsin Haji PhotoMedicine Labs University of Waterloo 200 University Ave W WaterlooONN2L 3G1 Canada Vision and Image Processing Lab University of Waterloo 200 University Ave W WaterlooONN2L 3G1 Canada IllumiSonics Inc. 22 King Street South Suite 300 WaterlooONN2J 1N8 Canada

Photoacoustic Remote Sensing (PARS) microscopy is an emerging label-free optical absorption imaging modality. PARS operates by capturing nanosecond-scale optical perturbations generated by photoacoustic pressures. These time-domain (TD) modulations are usually projected by amplitude to determine absorption magnitude. However, significant information on the target's material properties is contained within the TD signals. This work proposes a novel clustering method to learn TD features which relate to underlying biomolecule characteristics. This technique identifies features related to constituent biomolecules, enabling single-acquisition virtual tissue labelling. Colorized visualizations of tissue are produced, highlighting specific tissue components. This is demonstrated on freshly resected murine brain tissue, clearly discerning structures including myelinated and unmyelinated neurons (white and gray matter) and nuclear structures. © 2022, CC BY.

关键词： Biomolecules

30-3: A Moving Camera and Synthetic Calibration Target Solution for Non-Planar Scene Estimation and Projector Calibration

学校读者我要写书评

暂无评论

SID Symposium Digest of Technical Papers 2022年第1期53卷

作者： Katherine Arnold Paul Fieguth Mark Lamm Vision and Image Processing Lab University of Waterloo Waterloo ON Canada Christie Digital Systems Canada Inc. Kitchener ON Canada

The automatic calibration of projector systems improves the possible environments available to this technology which relies on exact scene calibration. This paper explores the challenge of projector calibration and non-planar scene estimation. This formulation assumes no prior information on the moving camera or fixed projector. Limited scene understanding provides the basis for constructing synthetic calibration targets to perform a geometric recovery through bundle adjustment. Synthetic data are generated from ground-truth camera and projector parameters to explore the method's performance over varying properties of the synthetic calibration targets.

关键词： Projector-camera calibration non-planar scene moving camera bundle adjustment auto-calibration self-calibration projection mapping projector calibration

FRESCO: Fast Radiometric Egocentric Screen Compensation

学校读者我要写书评

暂无评论

FRESCO: Fast Radiometric Egocentric Screen Compensation

IEEE/CVF Conference on Computer vision and Pattern Recognition Workshops

作者： Matthew Post Paul Fieguth Mohamed A. Naiel Zohreh Azimifar Mark Lamm Vision and Image Processing Lab University of Waterloo Shiraz University Christie Digital Systems Canada Inc

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Existing radiometric compensation methods for projector-camera systems have been shown to produce compensated colours which are inconsistent to a human viewer. In this paper, a novel radiometric compensation method for projector-camera systems and textured surfaces is introduced based on the human visual system (HVS) colour response. The proposed method can extend established compensation methods to produce colours which are human-perceived to be correct (egocentric modelling). As a result, this method performs radiometric compensation which is not only consistent and precise, but also produces images which are visually accurate to an external colour reference. This method is shown to produce generally the lowest average radiometric compensation error when compared to compensation performed using only the response of a camera, demonstrated through quantitative analysis of compensated colours, and supported by qualitative results.

关键词： compensation method Radiation compensated Color Error compensation Human Cameras QUANTITATIVE ANALYSIS Inconsistent

RUNet: A Robust UNet Architecture for image Super-Resolution

学校读者我要写书评

暂无评论

RUNet: A Robust UNet Architecture for Image Super-Resolution

IEEE Computer Society Conference on Computer vision and Pattern Recognition Workshops (CVPRW)

作者： Xiaodan Hu Mohamed A. Naiel Alexander Wong Mark Lamm Paul Fieguth Vision and Image Processing Lab University of Waterloo Waterloo Canada Christie Digital Systems Canada Inc. Kitchener Canada

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Single image super-resolution (SISR) is a challenging ill-posed problem which aims to restore or infer a high-resolution image from a low-resolution one. Powerful deep learning-based techniques have achieved state-of-the-art performance in SISR; however, they can underperform when handling images with non-stationary degradations, such as for the application of projector resolution enhancement. In this paper, a new UNet architecture that is able to learn the relationship between a set of degraded low-resolution images and their corresponding original high-resolution images is proposed. We propose employing a degradation model on training images in a non-stationary way, allowing the construction of a robust UNet (RUNet) for image super-resolution (SR). Experimental results show that the proposed RUNet improves the visual quality of the obtained super-resolution images while maintaining a low reconstruction error.

关键词： Degradation Spatial resolution Training Interpolation Robustness Visualization