Focus Quality Assessment of High-Throughput Whole Slide Imaging in Digital Pathology

ArtiDiffuser: A unified framework for artifact restoration and synthesis for histology images via counterfactual diffusion model

Artifacts in histology images pose challenges for accurate diagnosis with deep learning models, often leading to misinterpretations. Existing artifact restoration methods primarily rely on Generative Adversarial Networks (GANs), which approach the problem as image-to-image translation. However, those approaches are prone to mode collapse and can unexpectedly alter morphological features or staining styles. To address the issue, we propose ArtiDiffuser, a counterfactual diffusion model tailored to restore only artifact-distorted regions while preserving the integrity of the rest of the image. Additionally, we show an innovative perspective by addressing the misdiagnosis stemming from artifacts via artifact synthesis as data augmentation, and thereby leverage ArtiDiffuser to unify the artifact synthesis and the restoration capabilities. This synergy significantly surpasses the performance of conventional methods which separately handle artifact restoration or synthesis. We propose a Swin-Transformer denoising network backbone to capture both local and global attention, further enhanced with a class-guided Mixture of Experts (MoE) to process features related to specific artifact categories. Moreover, it utilizes adaptable class-specific tokens for enhanced feature discrimination and a mask-weighted loss function to specifically target and correct artifact-affected regions, thus addressing issues of data imbalance. In downstream applications, ArtiDiffuser employs a consistency regularization strategy that assures the model's predictive accuracy is maintained across original and artifact-augmented images. We also contribute the first comprehensive histology dataset, comprising 723 annotated patches across various artifact categories, to facilitate further research. Evaluations on four distinct datasets for both restoration and synthesis demonstrate ArtiDiffuser's effectiveness compared to GAN-based approaches, used for either pre-processing or augmentation. The code is available at https://github.com/wagnchogn/ArtiDiffuser.

GrandQC: A comprehensive solution to quality control problem in digital pathology

Histological slides contain numerous artifacts that can significantly deteriorate the performance of image analysis algorithms. Here we develop the GrandQC tool for tissue and multi-class artifact segmentation. GrandQC allows for high-precision tissue segmentation (Dice score 0.957) and segmentation of tissue without artifacts (Dice score 0.919-0.938 dependent on magnification). Slides from 19 international pathology departments digitized with the most common scanning systems and from The Cancer Genome Atlas dataset were used to establish a QC benchmark, analyzing inter-institutional, intra-institutional, temporal, and inter-scanner slide quality variations. GrandQC improves the performance of downstream image analysis algorithms. We open-source the GrandQC tool, our large manually annotated test dataset, and all QC masks for the entire TCGA cohort to address the problem of QC in digital/computational pathology. GrandQC can be used as a tool to monitor sample preparation and scanning quality in pathology departments and help to track and eliminate major artifact sources.

Image Quality Assessment and Reliability Analysis of Artificial Intelligence-Based Tumor Classification of Stimulated Raman Histology of Tumor Biobank Samples

BACKGROUND

Stimulated Raman histology (SRH) is a label-free optical imaging method for rapid intraoperative analysis of fresh tissue samples. Analysis of SRH images using Convolutional Neural Networks (CNN) has shown promising results for predicting the main histopathological classes of neurooncological tumors. Due to the relatively low number of rare tumor representations in CNN training datasets, a valid prediction of rarer entities remains limited. To develop new reliable analysis tools, larger datasets and greater tumor variety are crucial. One way to accomplish this is through research biobanks storing frozen tumor tissue samples. However, there is currently no data available regarding the pertinency of previously frozen tissue samples for SRH analysis. The aim of this study was to assess image quality and perform a comparative reliability analysis of artificial intelligence-based tumor classification using SRH in fresh and frozen tissue samples.

METHODS

In a monocentric prospective study, tissue samples from 25 patients undergoing brain tumor resection were obtained. SRH was acquired in fresh and defrosted samples of the same specimen after varying storage durations at -80 °C. Image quality was rated by an experienced neuropathologist, and prediction of histopathological diagnosis was performed using two established CNNs.

RESULTS

The image quality of SRH in fresh and defrosted tissue samples was high, with a mean image quality score of 1.96 (range 1-5) for both groups. CNN analysis showed high internal consistency for histo-(Cα 0.95) and molecular (Cα 0.83) pathological tumor classification. The results were confirmed using a dataset with samples from the local tumor biobank (Cα 0.91 and 0.53).

CONCLUSIONS

Our results showed that SRH appears comparably reliable in fresh and frozen tissue samples, enabling the integration of tumor biobank specimens to potentially improve the diagnostic range and reliability of CNN prediction tools.

Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview

The capability to gather heterogeneous data, alongside the increasing power of artificial intelligence to examine it, leading a revolution in harnessing multimodal data in the life sciences. However, most approaches are limited to unimodal data, leaving integrated approaches across modalities relatively underdeveloped in computational pathology. Pathogenomics, as an invasive method to integrate advanced molecular diagnostics from genomic data, morphological information from histopathological imaging, and codified clinical data enable the discovery of new multimodal cancer biomarkers to propel the field of precision oncology in the coming decade. In this perspective, we offer our opinions on synthesizing complementary modalities of data with emerging multimodal artificial intelligence methods in pathogenomics. It includes correlation between the pathological and genomic profile of cancer, fusion of histology, and genomics profile of cancer. We also present challenges, opportunities, and avenues for future work.

Artifact Detection and Restoration in Histology Images With Stain-Style and Structural Preservation

The artifacts in histology images may encumber the accurate interpretation of medical information and cause misdiagnosis. Accordingly, prepending manual quality control of artifacts considerably decreases the degree of automation. To close this gap, we propose a methodical pre-processing framework to detect and restore artifacts, which minimizes their impact on downstream AI diagnostic tasks. First, the artifact recognition network AR-Classifier first differentiates common artifacts from normal tissues, e.g., tissue folds, marking dye, tattoo pigment, spot, and out-of-focus, and also catalogs artifact patches by their restorability. Then, the succeeding artifact restoration network AR-CycleGAN performs de-artifact processing where stain styles and tissue structures can be maximally retained. We construct a benchmark for performance evaluation, curated from both clinically collected WSIs and public datasets of colorectal and breast cancer. The functional structures are compared with state-of-the-art methods, and also comprehensively evaluated by multiple metrics across multiple tasks, including artifact classification, artifact restoration, downstream diagnostic tasks of tumor classification and nuclei segmentation. The proposed system allows full automation of deep learning based histology image analysis without human intervention. Moreover, the structure-independent characteristic enables its processing with various artifact subtypes. The source code and data in this research are available at https://github.com/yunboer/AR-classifier-and-AR-CycleGAN.

Blind Image Quality Assessment for Pathological Microscopic Image Under Screen and Immersion Scenarios

The high-quality pathological microscopic images are essential for physicians or pathologists to make a correct diagnosis. Image quality assessment (IQA) can quantify the visual distortion degree of images and guide the imaging system to improve image quality, thus raising the quality of pathological microscopic images. Current IQA methods are not ideal for pathological microscopy images due to their specificity. In this paper, we present deep learning-based blind image quality assessment model with saliency block and patch block for pathological microscopic images. The saliency block and patch block can handle the local and global distortions, respectively. To better capture the area of interest of pathologists when viewing pathological images, the saliency block is fine-tuned by eye movement data of pathologists. The patch block can capture lots of global information strongly related to image quality via the interaction between different image patches from different positions. The performance of the developed model is validated by the home-made Pathological Microscopic Image Quality Database under Screen and Immersion Scenarios (PMIQD-SIS) and cross-validated by the five public datasets. The results of ablation experiments demonstrate the contribution of the added blocks. The dataset and the corresponding code are publicly available at: https://github.com/mikugyf/PMIQD-SIS.

Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance

The pathology is decisive for disease diagnosis but relies heavily on experienced pathologists. In recent years, there has been growing interest in the use of artificial intelligence in pathology (AIP) to enhance diagnostic accuracy and efficiency. However, the impressive performance of deep learning-based AIP in laboratory settings often proves challenging to replicate in clinical practice. As the data preparation is important for AIP, the paper has reviewed AIP-related studies in the PubMed database published from January 2017 to February 2022, and 118 studies were included. An in-depth analysis of data preparation methods is conducted, encompassing the acquisition of pathological tissue slides, data cleaning, screening, and subsequent digitization. Expert review, image annotation, dataset division for model training and validation are also discussed. Furthermore, we delve into the reasons behind the challenges in reproducing the high performance of AIP in clinical settings and present effective strategies to enhance AIP's clinical performance. The robustness of AIP depends on a randomized collection of representative disease slides, incorporating rigorous quality control and screening, correction of digital discrepancies, reasonable annotation, and sufficient data volume. Digital pathology is fundamental in clinical-grade AIP, and the techniques of data standardization and weakly supervised learning methods based on whole slide image (WSI) are effective ways to overcome obstacles of performance reproduction. The key to performance reproducibility lies in having representative data, an adequate amount of labeling, and ensuring consistency across multiple centers. Digital pathology for clinical diagnosis, data standardization and the technique of WSI-based weakly supervised learning will hopefully build clinical-grade AIP.

Using symmetric illumination and color camera to achieve high throughput Fourier ptychographic microscopy

This study aims to develop a high throughput Fourier ptychographic microscopy (FPM) technique based on symmetric illumination and a color detector, which is able to accelerate image acquisition by up to 12 times. As an emerging technology, the efficiency of FPM is limited by its data acquisition process, especially for color microscope image reconstruction. To overcome this, we built an FPM prototype equipped with a color camera and a 4×/0.13 NA objective lens. During the image acquisition, two symmetric LEDs illuminate the sample simultaneously using white light, which doubles the light intensity and reduces the total captured raw patterns by half. A standard USAF 1951 resolution target was used to measure the system's modulation transfer function (MTF) curve, and the H&E-stained ovarian cancer samples were then imaged to assess the feature qualities depicted on the reconstructed images. The results showed that the measured MTF curves of red, green, and blue channels are generally comparable to the corresponding curves generated by conventional FPM, while symmetric illumination FPM preserves more tissue details, which is superior to the results captured by conventional 20×/0.4 NA objective lens. This investigation initially verified the feasibility of symmetric illumination based color FPM.

sPhaseStation: a whole slide quantitative phase imaging system based on dual-view transport of intensity phase microscopy

Whole slide imaging scans a microscope slide into a high-resolution digital image, and it paves the way from pathology to digital diagnostics. However, most of them rely on bright-field and fluorescence imaging with sample labels. In this work, we designed sPhaseStation, which is a dual-view transport of intensity phase microscopy-based whole slide quantitative phase imaging system for label-free samples. sPhaseStation relies on a compact microscopic system with two imaging recorders that can capture both under and over-focus images. Combined with the field of view (FoV) scan, a series of these defocus images in different FoVs can be captured and stitched into two FoV-extended under and over-focus ones, which are used for phase retrieval via solving the transport of intensity equation. Using a 10× micro-objective, sPhaseStation reaches the spatial resolution of 2.19 µm and obtains the phase with high accuracy. Additionally, it acquires a whole slide image of a 3m m×3m m region in 2 min. The reported sPhaseStation could be a prototype of the whole slide quantitative phase imaging device, which may provide a new perspective for digital pathology.

Next-Generation Pathology Using Multiplexed Immunohistochemistry: Mapping Tissue Architecture at Single-Cell Level

Single-cell omics aim at charting the different types and properties of all cells in the human body in health and disease. Over the past years, myriads of cellular phenotypes have been defined by methods that mostly required cells to be dissociated and removed from their original microenvironment, thus destroying valuable information about their location and interactions. Growing insights, however, are showing that such information is crucial to understand complex disease states. For decades, pathologists have interpreted cells in the context of their tissue using low-plex antibody- and morphology-based methods. Novel technologies for multiplexed immunohistochemistry are now rendering it possible to perform extended single-cell expression profiling using dozens of protein markers in the spatial context of a single tissue section. The combination of these novel technologies with extended data analysis tools allows us now to study cell-cell interactions, define cellular sociology, and describe detailed aberrations in tissue architecture, as such gaining much deeper insights in disease states. In this review, we provide a comprehensive overview of the available technologies for multiplexed immunohistochemistry, their advantages and challenges. We also provide the principles on how to interpret high-dimensional data in a spatial context. Similar to the fact that no one can just “read” a genome, pathological assessments are in dire need of extended digital data repositories to bring diagnostics and tissue interpretation to the next level.

Integrated Analytical System for Clinical Single-Cell Analysis

High-dimensional analyses of cancers can potentially be used to better define cancer subtypes, analyze the complex tumor microenvironment, and perform cancer cell pathway analyses for drug trials. Unfortunately, integrated systems that allow such analyses in serial fine needle aspirates within a day or at point-of-care currently do not exist. To achieve this, an integrated immunofluorescence single-cell analyzer (i2SCAN) for deep profiling of directly harvested cells is developed. By combining a novel cellular imaging system, highly cyclable bioorthogonal FAST antibody panels, and integrated computational analysis, it is shown that same-day analysis is possible in thousands of harvested cells. It is demonstrated that the i2SCAN approach allows comprehensive analysis of breast cancer samples obtained by fine needle aspiration or core tissues. The method is a rapid, robust, and low-cost solution to high-dimensional analysis of scant clinical specimens.

Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications

With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational bottlenecks requiring new computational histopathology tools. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology applications. Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis of human cancers. In this paper, we provide a comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis. Specifically, we first describe recent literature that uses deep learning for color normalization, which is one essential research direction for H&E-stained histopathological image analysis. Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation. We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images. Finally, online resources and open research problems on pathological image analysis are also provided in this review for the convenience of researchers who are interested in this exciting field.

An algorithm selection methodology for automated focusing in optical microscopy

Autofocus systems are essential in optical microscopy. These systems typically sweep the sample through the focal range and apply an algorithm to determine the contrast value of each image, where the highest value indicates the optimal focus position. As the optimal algorithm may vary according to the images' content, we evaluate the 15 most used algorithms in the field using 150 stacks of images from four different kinds of tissue. We use four measuring criteria and two types of analysis and propose a general methodology to apply to select the best fitting algorithm for any given application. In this paper, we present the results of this evaluation and a detailed discussion of different features: the threshold used for the algorithms, the criteria parameters, the analysis used, the bit depth of the images, their magnification, and the type of tissue, reaching the conclusion that some of these parameters are more relevant to the study than others, and the implementation of the proposed methodology can lead to a fast and reliable autofocus system capable of performing an analysis and selection of algorithms with no supervision required.

Best Practice Recommendations for the Implementation of a Digital Pathology Workflow in the Anatomic Pathology Laboratory by the European Society of Digital and Integrative Pathology (ESDIP)

The interest in implementing digital pathology (DP) workflows to obtain whole slide image (WSI) files for diagnostic purposes has increased in the last few years. The increasing performance of technical components and the Food and Drug Administration (FDA) approval of systems for primary diagnosis led to increased interest in applying DP workflows. However, despite this revolutionary transition, real world data suggest that a fully digital approach to the histological workflow has been implemented in only a minority of pathology laboratories. The objective of this study is to facilitate the implementation of DP workflows in pathology laboratories, helping those involved in this process of transformation to identify: (a) the scope and the boundaries of the DP transformation; (b) how to introduce automation to reduce errors; (c) how to introduce appropriate quality control to guarantee the safety of the process and (d) the hardware and software needed to implement DP systems inside the pathology laboratory. The European Society of Digital and Integrative Pathology (ESDIP) provided consensus-based recommendations developed through discussion among members of the Scientific Committee. The recommendations are thus based on the expertise of the panel members and on the agreement obtained after virtual meetings. Prior to publication, the recommendations were reviewed by members of the ESDIP Board. The recommendations comprehensively cover every step of the implementation of the digital workflow in the anatomic pathology department, emphasizing the importance of interoperability, automation and tracking of the entire process before the introduction of a scanning facility. Compared to the available national and international guidelines, the present document represents a practical, handy reference for the correct implementation of the digital workflow in Europe.

High throughput slanted scanning whole slide imaging system for digital pathology

In whole slide imaging (WSI), normally only a one layer imaging of the slide is performed. Autofocus at multiple positions is usually required. But defocus blur still exists due to tissue folding or specimen thickness. Repeated Z-stack scan be applied here, which, however, is too time consuming. Here, a high throughput slanted scanning WSI system is reported. In this system, the slide surface was slanted 1° relative to the focal plane. Thus, the focal plane spanned multiple layers of the sample. By moving the slide, multi-layer image data of the sample can be acquired simultaneously at a time frame comparable to conventional 1-layer imaging. With image fusion, defocus blur can be avoided. High quality and fast imaging of both cytological and histological slide specimens was demonstrated without applying aberration correction. The system can be a highly efficient way for the application of WSI in digital pathology.

Assessment of a computerized quantitative quality control tool for whole slide images of kidney biopsies

Inconsistencies in the preparation of histology slides and whole-slide images (WSIs) may lead to challenges with subsequent image analysis and machine learning approaches for interrogating the WSI. These variabilities are especially pronounced in multicenter cohorts, where batch effects (i.e. systematic technical artifacts unrelated to biological variability) may introduce biases to machine learning algorithms. To date, manual quality control (QC) has been the de facto standard for dataset curation, but remains highly subjective and is too laborious in light of the increasing scale of tissue slide digitization efforts. This study aimed to evaluate a computer-aided QC pipeline for facilitating a reproducible QC process of WSI datasets. An open source tool, HistoQC, was employed to identify image artifacts and compute quantitative metrics describing visual attributes of WSIs to the Nephrotic Syndrome Study Network (NEPTUNE) digital pathology repository. A comparison in inter-reader concordance between HistoQC aided and unaided curation was performed to quantify improvements in curation reproducibility. HistoQC metrics were additionally employed to quantify the presence of batch effects within NEPTUNE WSIs. Of the 1814 WSIs (458 H&E, 470 PAS, 438 silver, 448 trichrome) from n = 512 cases considered in this study, approximately 9% (163) were identified as unsuitable for subsequent computational analysis. The concordance in the identification of these WSIs among computational pathologists rose from moderate (Gwet's AC1 range 0.43 to 0.59 across stains) to excellent (Gwet's AC1 range 0.79 to 0.93 across stains) agreement when aided by HistoQC. Furthermore, statistically significant batch effects (p < 0.001) in the NEPTUNE WSI dataset were discovered. Taken together, our findings strongly suggest that quantitative QC is a necessary step in the curation of digital pathology cohorts. © 2020 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Quality control stress test for deep learning-based diagnostic model in digital pathology

Digital pathology provides a possibility for computational analysis of histological slides and automatization of routine pathological tasks. Histological slides are very heterogeneous concerning staining, sections' thickness, and artifacts arising during tissue processing, cutting, staining, and digitization. In this study, we digitally reproduce major types of artifacts. Using six datasets from four different institutions digitized by different scanner systems, we systematically explore artifacts' influence on the accuracy of the pre-trained, validated, deep learning-based model for prostate cancer detection in histological slides. We provide evidence that any histological artifact dependent on severity can lead to a substantial loss in model performance. Strategies for the prevention of diagnostic model accuracy losses in the context of artifacts are warranted. Stress-testing of diagnostic models using synthetically generated artifacts might be an essential step during clinical validation of deep learning-based algorithms.

Autofocusing technologies for whole slide imaging and automated microscopy

Whole slide imaging (WSI) has moved digital pathology closer to diagnostic practice in recent years. Due to the inherent tissue topography variability, accurate autofocusing remains a critical challenge for WSI and automated microscopy systems. The traditional focus map surveying method is limited in its ability to acquire a high degree of focus points while still maintaining high throughput. Real-time approaches decouple image acquisition from focusing, thus allowing for rapid scanning while maintaining continuous accurate focus. This work reviews the traditional focus map approach and discusses the choice of focus measure for focal plane determination. It also discusses various real-time autofocusing approaches including reflective-based triangulation, confocal pinhole detection, low-coherence interferometry, tilted sensor approach, independent dual sensor scanning, beam splitter array, phase detection, dual-LED illumination and deep-learning approaches. The technical concepts, merits and limitations of these methods are explained and compared to those of a traditional WSI system. This review may provide new insights for the development of high-throughput automated microscopy imaging systems that can be made broadly available and utilizable without loss of capacity.

A Regulatory Science Initiative to Harmonize and Standardize Digital Pathology and Machine Learning Processes to Speed up Clinical Innovation to Patients

Unlocking the full potential of pathology data by gaining computational access to histological pixel data and metadata (digital pathology) is one of the key promises of computational pathology. Despite scientific progress and several regulatory approvals for primary diagnosis using whole-slide imaging, true clinical adoption at scale is slower than anticipated. In the U.S., advances in digital pathology are often siloed pursuits by individual stakeholders, and to our knowledge, there has not been a systematic approach to advance the field through a regulatory science initiative. The Alliance for Digital Pathology (the Alliance) is a recently established, volunteer, collaborative, regulatory science initiative to standardize digital pathology processes to speed up innovation to patients. The purpose is: (1) to account for the patient perspective by including patient advocacy; (2) to investigate and develop methods and tools for the evaluation of effectiveness, safety, and quality to specify risks and benefits in the precompetitive phase; (3) to help strategize the sequence of clinically meaningful deliverables; (4) to encourage and streamline the development of ground-truth data sets for machine learning model development and validation; and (5) to clarify regulatory pathways by investigating relevant regulatory science questions. The Alliance accepts participation from all stakeholders, and we solicit clinically relevant proposals that will benefit the field at large. The initiative will dissolve once a clinical, interoperable, modularized, integrated solution (from tissue acquisition to diagnostic algorithm) has been implemented. In times of rapidly evolving discoveries, scientific input from subject-matter experts is one essential element to inform regulatory guidance and decision-making. The Alliance aims to establish and promote synergistic regulatory science efforts that will leverage diverse inputs to move digital pathology forward and ultimately improve patient care.