Whole Slide Imaging Systems Research Articles

Development of a multi-scanner facility for data acquisition for digital pathology artificial intelligence.

Whole slide imaging (WSI) of pathology glass slides using high-resolution scanners has enabled the large-scale application of artificial intelligence (AI) in pathology, to support the detection and diagnosis of disease, potentially increasing efficiency and accuracy in tissue diagnosis. Despite the promise of AI, it has limitations. 'Brittleness' or sensitivity to variation in inputs necessitates that large amounts of data are used for training. AI is often trained on data from different scanners but not usually by replicating the same slide across scanners. The utilisation of multiple WSI instruments to produce digital replicas of the same slides will make more comprehensive datasets and may improve the robustness and generalisability of AI algorithms as well as reduce the overall data requirements of AI training. To this end, the National Pathology Imaging Cooperative (NPIC) has built the AI FORGE (Facilitating Opportunities for Robust Generalisable data Emulation), a unique multi-scanner facility embedded in a clinical site in the NHS to (1) compare scanner performance, (2) replicate digital pathology image datasets across WSI systems, and (3) support the evaluation of clinical AI algorithms. The NPIC AI FORGE currently comprises 15 scanners from nine manufacturers. It can generate approximately 4,000 WSI images per day (approximately 7 TB of image data). This paper describes the process followed to plan and build such a facility. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review.

Digital pathology (DP) has begun to play a key role in the evaluation of liver specimens. Recent studies have shown that a workflow that combines DP and artificial intelligence (AI) applied to histopathology has potential value in supporting the diagnosis, treatment evaluation, and prognosis prediction of liver diseases. Here, we provide a systematic review of the use of this workflow in the field of hepatology. Based on the PRISMA 2020 criteria, a search of the PubMed, SCOPUS, and Embase electronic databases was conducted, applying inclusion/exclusion filters. The articles were evaluated by two independent reviewers, who extracted the specifications and objectives of each study, the AI tools used, and the results obtained. From the 266 initial records identified, 25 eligible studies were selected, mainly conducted on human liver tissues. Most of the studies were performed using whole-slide imaging systems for imaging acquisition and applying different machine learning and deep learning methods for image pre-processing, segmentation, feature extractions, and classification. Of note, most of the studies selected demonstrated good performance as classifiers of liver histological images compared to pathologist annotations. Promising results to date bode well for the not-too-distant inclusion of these techniques in clinical practice.

Minimum resolution requirements of digital pathology images for accurate classification.

Digitization of pathology has been proposed as an essential mitigation strategy for the severe staffing crisis facing most pathology departments. Despite its benefits, several barriers have prevented widespread adoption of digital workflows, including cost and pathologist reluctance due to subjective image quality concerns. In this work, we quantitatively determine the minimum image quality requirements for binary classification of histopathology images of breast tissue in terms of spatial and sampling resolution. We train an ensemble of deep learning classifier models on publicly available datasets to obtain a baseline accuracy and computationally degrade these images according to our derived theoretical model to identify the minimum resolution necessary for acceptable diagnostic accuracy. Our results show that images can be degraded significantly below the resolution of most commercial whole-slide imaging systems while maintaining reasonable accuracy, demonstrating that macroscopic features are sufficient for binary classification of stained breast tissue. A rapid low-cost imaging system capable of identifying healthy tissue not requiring human assessment could serve as a triage system for reducing caseloads and alleviating the significant strain on the current workforce.

Validation of whole slide imaging for frozen section diagnosis of lymph node metastasis: A retrospective study from a tertiary care hospital in Thailand

BackgroundThe use of whole slide imaging (WSI) for frozen section (FS) diagnosis is helpful, particularly in the context of pathologist shortages. However, there is minimal data on such usage in resource-limited settings. This study aims to validate the use of WSI for FS diagnosis of lymph node metastasis using a low-cost virtual microscope scanner with consumer-grade laptops at a tertiary care hospital in Thailand. MethodsFS slides were retrieved for which the clinical query was to evaluate lymph node metastasis. They were digitized by a virtual microscope scanner (MoticEasyScan, Hong Kong) using up to 40× optical magnification. Three observers with different pathology experience levels diagnosed each slide, reviewing glass slides (GS) followed by digital slides (DS) after two weeks of a wash out period. WSI and GS diagnoses were compared. The time used for scanning and diagnosis of each slide was recorded. Results295 FS slides were retrieved and digitized. The first-time successful scanning rate was 93.6 %. The mean scanning time was 2 min per slide. Both intraobserver agreement and interobserver agreement of WSI and GS diagnoses were high (Cohen's K; kappa value >0.84). The time used for DS diagnosis decreased as the observer's experience with WSI increased. ConclusionsDespite varying pathological experiences, observers using WSI provided accurate FS diagnoses of lymph node metastasis. The time required for DS diagnoses decreased with additional observer's experience with WSI. Therefore, a WSI system containing low-cost scanners and consumer-grade laptops could be used for FS services in hospital laboratories lacking pathologists.

Learning to autofocus in whole slide imaging via physics-guided deep cascade networks.

Whole slide imaging (WSI), is an essential technology for digital pathology, the performance of which is primarily affected by the autofocusing process. Conventional autofocusing methods either are time-consuming or require additional hardware and thus are not compatible with the current WSI systems. In this paper, we propose an effective learning-based method for autofocusing in WSI, which can realize accurate autofocusing at high speed as well as without any optical hardware modifications. Our method is inspired by an observation that sample images captured by WSI have distinctive characteristics with respect to positive / negative defocus offsets, due to the asymmetry effect of optical aberrations. Based on this physical knowledge, we develop novel deep cascade networks to enhance autofocusing quality. Specifically, to handle the effect of optical aberrations, a binary classification network is tailored to distinguish sample images with positive / negative defocus. As such, samples within the same category share similar characteristics. It facilitates the followed refocusing network, which is designed to learn the mapping between the defocus image and defocus distance. Experimental results demonstrate that our method achieves superior autofocusing performance to other related methods.

Whole-Slide Imaging: Updates and Applications in Papillary Thyroid Carcinoma.

Whole-slide imaging (WSI) has wide spectrum of application in histopathology, especially in the study of cancer including papillary thyroid carcinoma. The main applications of WSI system include research, teaching, and assessment and recently pathology practices. The other major advantages of WSI over histological sections on glass slides are easier storage and sharing of information as well as adaptation of use in artificial intelligence. The applications of WSI depend on factors such as volume of services requiring WSI, physical factors (computer server, bandwidth limitation of networks, storages requirements for data), adaption of the WSI images with the laboratory workflow, personnel (IT expert, pathologist, technicians) adaptation to the WSI workflow, validation studies, ethics, and cost efficiency of the application(s).

Deep learning-enabled whole slide imaging (DeepWSI): oil-immersion quality using dry objectives, longer depth of field, higher system throughput, and better functionality.

Whole slide imaging (WSI) has moved the traditional manual slide inspection process to the era of digital pathology. A typical WSI system translates the sample to different positions and captures images using a high numerical aperture (NA) objective lens. Performing oil-immersion microscopy is a major obstacle for WSI as it requires careful liquid handling during the scanning process. Switching between dry objective and oil-immersion lens is often impossible as it disrupts the acquisition process. For a high-NA objective lens, the sub-micron depth of field also poses a challenge to acquiring in-focus images of samples with uneven topography. Additionally, it implies a small field of view for each tile, thus limiting the system throughput and resulting in a long acquisition time. Here we report a deep learning-enabled WSI platform, termed DeepWSI, to substantially improve the system performance and imaging throughput. With this platform, we show that images captured with a regular dry objective lens can be transformed into images comparable to that of a 1.4-NA oil immersion lens. Blurred images with defocus distance from -5 µm to +5 µm can be virtually refocused to the in-focus plane post measurement. We demonstrate an equivalent data throughput of >2 gigapixels per second, the highest among existing WSI systems. Using the same deep neural network, we also report a high-resolution virtual staining strategy and demonstrate it for Fourier ptychographic WSI. The DeepWSI platform may provide a turnkey solution for developing high-performance diagnostic tools for digital pathology.

Low-cost whole slide imaging system with single-shot autofocusing based on color-multiplexed illumination and deep learning.

Recent research on whole slide imaging (WSI) has greatly promoted the development of digital pathology. However, accurate autofocusing is still the main challenge for WSI acquisition and automated digital microscope. To address this problem, this paper describes a low cost WSI system and proposes a fast, robust autofocusing method based on deep learning. We use a programmable LED array for sample illumination. Before the brightfield image acquisition, we turn on a red and a green LED, and capture a color-multiplexed image, which is fed into a neural network for defocus distance estimation. After the focus tracking process, we employ a low-cost DIY adaptor to digitally adjust the photographic lens instead of the mechanical stage to perform axial position adjustment, and acquire the in-focus image under brightfield illumination. To ensure the calculation speed and image quality, we build a network model based on a 'light weight' backbone network architecture-MobileNetV3. Since the color-multiplexed coherent illuminated images contain abundant information about the defocus orientation, the proposed method enables high performance of autofocusing. Experimental results show that the proposed method can accurately predict the defocus distance of various types of samples and has good generalization ability for new types of samples. In the case of using GPU, the processing time for autofocusing is less than 0.1 second for each field of view, indicating that our method can further speed up the acquisition of whole slide images.

Tissue Multiplex Analyte Detection in Anatomic Pathology - Pathways to Clinical Implementation.

Background: Multiplex tissue analysis has revolutionized our understanding of the tumor microenvironment (TME) with implications for biomarker development and diagnostic testing. Multiplex labeling is used for specific clinical situations, but there remain barriers to expanded use in anatomic pathology practice.Methods: We review immunohistochemistry (IHC) and related assays used to localize molecules in tissues, with reference to United States regulatory and practice landscapes. We review multiplex methods and strategies used in clinical diagnosis and in research, particularly in immuno-oncology. Within the framework of assay design and testing phases, we examine the suitability of multiplex immunofluorescence (mIF) for clinical diagnostic workflows, considering its advantages and challenges to implementation.Results: Multiplex labeling is poised to radically transform pathologic diagnosis because it can answer questions about tissue-level biology and single-cell phenotypes that cannot be addressed with traditional IHC biomarker panels. Widespread implementation will require improved detection chemistry, illustrated by InSituPlex technology (Ultivue, Inc., Cambridge, MA) that allows coregistration of hematoxylin and eosin (H&E) and mIF images, greater standardization and interoperability of workflow and data pipelines to facilitate consistent interpretation by pathologists, and integration of multichannel images into digital pathology whole slide imaging (WSI) systems, including interpretation aided by artificial intelligence (AI). Adoption will also be facilitated by evidence that justifies incorporation into clinical practice, an ability to navigate regulatory pathways, and adequate health care budgets and reimbursement. We expand the brightfield WSI system “pixel pathway” concept to multiplex workflows, suggesting that adoption might be accelerated by data standardization centered on cell phenotypes defined by coexpression of multiple molecules.Conclusion: Multiplex labeling has the potential to complement next generation sequencing in cancer diagnosis by allowing pathologists to visualize and understand every cell in a tissue biopsy slide. Until mIF reagents, digital pathology systems including fluorescence scanners, and data pipelines are standardized, we propose that diagnostic labs will play a crucial role in driving adoption of multiplex tissue diagnostics by using retrospective data from tissue collections as a foundation for laboratory-developed test (LDT) implementation and use in prospective trials as companion diagnostics (CDx).

Brightfield, fluorescence, and phase-contrast whole slide imaging via dual-LED autofocusing.

Whole slide imaging (WSI) systems convert the conventional biological samples into digital images. Existing commercial WSI systems usually require an expensive high-performance motorized stage to implement the precise mechanical control, and the cost is prohibitive for most individual pathologists. In this work, we report a low-cost WSI system using the off-the-shelf components, including a computer numerical control (CNC) router, a photographic lens, a programmable LED array, a fluorescence filter cube, and a surface-mount LED. To perform real-time single-frame autofocusing, we exploited two elements of a programmable LED array to illuminate the sample from two different incident angles. The captured image would contain two copies of the sample with a certain separation determined by the defocus distance of the sample. Then the defocus distance can be recovered by identifying the translational shift of the two copies. The reported WSI system can reach a resolution of ∼0.7 µm. The time to determine the optimal focusing position for each tile is only 0.02 s, which is about an 83% improvement compared to our previous work. We quantified the focusing performance on 1890 different tissue tiles. The mean focusing error is ∼0.34 µm, which is well below the ± 0.7 µm depth of field range of our WSI system. The reported WSI system can handle both the semitransparent and the transparent sample, enabling us to demonstrate the implementation of brightfield, fluorescence, and phase-contrast WSI. An automatic digital distortion correction strategy is also developed to avoid the stitching errors. The reported prototype has an affordable cost and can make it broadly available and utilizable for individual pathologists as well as can promote the development of digital pathology.

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.

Validation of a Portable Whole-Slide Imaging System for Frozen Section Diagnosis

Background: Frozen section (FS) diagnosis is one of the promising applications of digital pathology (DP). However, the implementation of an appropriate and economically viable DP solution for FS in routine practice is challenging. The objective of this study was to establish the non-inferiority of whole-slide imaging (WSI) versus optical microscopy (OM) for FS diagnosis using a low cost and portable DP system. Materials and Methods: A validation study to investigate the technical performance and diagnostic accuracy of WSI versus OM for FS diagnosis was performed using 60 FS cases[120 slides i.e, 60 hematoxylin and eosin (H & E) and 60 toluidine blue (TOLB)]. The diagnostic concordance, inter- and intra-observer agreement for FS diagnosis by WSI versus OM were recorded. Results: The first time successful scanning rate was 89.1% (107/120). Mean scanning time per slide for H and E and TOLB slide was 1:47 min (range; 0:22–3: 21 min) and 1:46 min (range; 0:21–3: 20 min), respectively. Mean storage space per slide for H and E and TOLB slide was 0.83 GB (range: 0.12–1.73 GB) and 0.71 GB (range: 0.11–1.66 GB), respectively. Considering major discrepancies, the overall diagnostic concordance for OM and WSI, when compared with the reference standard, was 95.42% and 95.83%, respectively. There was almost perfect intra as well as inter-observer agreement (k ≥ 0.8) among 4 pathologists between WSI and OM for FS diagnosis. Mean turnaround time (TAT) of 14:58 min was observed using WSI for FS diagnosis, which was within the College of American Pathologists recommended range for FS reporting. The image quality was average to best quality in most of the cases. Conclusion: WSI was noninferior to OM for FS diagnosis across various specimen types. This portable WSI system can be safely adopted for routine FS diagnosis and provides an economically viable alternative to high-end scanners.

Rapid Whole Slide Imaging via Dual-Shot Deep Autofocusing

Whole slide imaging (WSI) is an emerging technology for digital pathology. The accuracy and speed of autofocusing are critical for the performance of the WSI system. This paper introduces a novel technique of deep autofocusing for WSI. Instead of mechanically adjusting the focal distance on a tile-by-tile basis, we develop a deep convolutional neural network for tile-wise autofocusing to generate in-focus images from tentative possibly defocused images. This deep autofocusing network (DAFNet) works with only two images taken at different focal distances; in contrast, traditional methods need to take, for each tile of the target ultra high-resolution pathology image, a stack of as many as 21 shots with varying focal distances. The novel architecture design of DAFNet facilitates the fusion of complementary information of the two input images of different focal distances. The proposed off-line reconstruction strategy allows high throughput scanning of sample slides done without compromising image quality, because DAFNet can rectify errors in focal distance and bring the scanned tiles back into focus by learnt non-linear dual-input blur-to-sharp mapping. Experimental results demonstrate the refocusing capability of the DAFNet method.

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.

Computational imaging modalities for multi-focal whole-slide imaging systems.

Whole-slide imaging systems can generate full-color image data of tissue slides efficiently, which are needed for digital pathology applications. This paper focuses on a scanner architecture that is based on a multi-line image sensor that is tilted with respect to the optical axis, such that every line of the sensor scans the tissue slide at a different focus level. This scanner platform is designed for imaging with continuous autofocus and inherent color registration at a throughput of the order of 400 MPx/s. Here, single-scan multi-focal whole-slide imaging, enabled by this platform, is explored. In particular, two computational imaging modalities based on multi-focal image data are studied. First, 3D imaging of thick absorption stained slides (∼60µm) is demonstrated in combination with deconvolution to ameliorate the inherently weak contrast in thick-tissue imaging. Second, quantitative phase tomography is demonstrated on unstained tissue slides and on fluorescently stained slides, revealing morphological features complementary to features made visible with conventional absorption or fluorescence stains. For both computational approaches simplified algorithms are proposed, targeted for straightforward parallel processing implementation at ∼GPx/s throughputs.

Whole Slide Imaging (WSI) in Pathology: Current Perspectives and Future Directions.

Whole slide imaging (WSI), ever since its first introduction about two decades ago, has been validated for a number of applications in the field of pathology. The recent approval of US FDA to a WSI system for use in primary surgical pathology diagnosis has opened avenues for wider acceptance and application of this technology in routine practice. The ongoing technological advances in digital scanners, image visualization methods, and the integration of artificial intelligence-derived algorithms with these systems provide opportunities of its newer applications. Its benefits are innumerable such as ease of access through internet, avoidance of physical storage space, and no risk of deterioration of staining quality or breakage of slides to name a few. Various barriers such as the high cost, technical glitches, and professional hesitation to adopt a new technology have hindered its use in pathology. This review article summarizes the technical aspects of WSI, its applications in diagnostic pathology, training, and research along with future perspectives. It highlights the benefits, limitations, and challenges delaying the use of this technology in routine practice. The review is targeted at students, residents, and budding pathologists to better acquaint them with the key aspects of state-of-the-art technology and enable them to implement WSI judiciously.

Reproducible Color Gamut of Hematoxylin and Eosin Stained Images in Standard Color Spaces

A whole-slide imaging (WSI) system is a digital color imaging system used in digital pathology with the potential to substitute the conventional light microscope. A WSI system digitalizes a glass slide by converting the optical image to digital data with a scanner and then converting the digital data back to the optical image with a display. During the digital-to-optical or optical-to-digital conversion, a color space is required to define the mapping between the digital domain and the optical domain so that the numerical data of each color pixel can be interpreted meaningfully. Unfortunately, many current WSI products do not specify the designated color space clearly, which leaves the user using the universally default color space, sRGB. sRGB is a legacy color space that has a limited color gamut, which is known to be unable to reproduce all color shades present in histology slides. In this work, experiments were conducted to quantitatively investigate the limitation of the sRGB color space used in WSI systems. Eight hematoxylin and eosin (H and E)-stained tissue samples, including human bladder, brain, breast, colon, kidney, liver, lung, and uterus, were measured with a multispectral imaging system to obtain the true colors at the pixel level. The measured color truth of each pixel was converted into the standard CIELAB color space to test whether it was within the color gamut of the sRGB color space. Experiment results show that all the eight images have a portion of pixels outside the sRGB color gamut. In the worst-case scenario, the bladder sample, about 35% of the image exceeded the sRGB color gamut. The results suggest that the sRGB color space is inadequate for WSI scanners to encode H and E-stained whole-slide images, and an sRGB display may have insufficient color gamut for displaying H and E-stained histology images.

OpenWSI: a low-cost, high-throughput whole slide imaging system via single-frame autofocusing and open-source hardware

Recent advancements in whole slide imaging (WSI) have moved pathology closer to digital practice. Existing systems require precise mechanical control, and the cost is prohibitive for most individual pathologists. Here we report a low-cost and high-throughput WSI system termed OpenWSI. The reported system is built using off-the-shelf components, including a programmable LED array, a photographic lens, and a low-cost computer numerical control (CNC) router. Different from conventional WSI platforms, our system performs real-time single-frame autofocusing using color-multiplexed illumination. For axial positioning control, we perform coarse adjustment using the CNC router and precise adjustment using the ultrasonic motor ring in the photographic lens. By using a 20X objective lens, we show that the OpenWSI system has a resolution of ∼ 0.7 µ m . It can acquire whole slide images of a 225 m m 2 region in ∼ 2 m i n s with throughput comparable to existing high-end platforms. The reported system offers a turnkey solution to transform the high-end WSI platforms into one that can be made broadly available and utilizable without loss of capacity.

Abstract 1149: Systems pathology by Optra 30 color multiplex immunofluorescence automated whole slide imaging system integrated with image analysis

Abstract Background A paradigm shift from single marker immunohistochemistry (IHC) to multiplexing in molecular histopathology has helped better understand the cellular composition and heterogeneity of sub population of cells in tissues. Presently, one needs to manually exchange filters after each multiplex scan for capturing all fluorophores per staining round. This is very cumbersome and inefficient, as this process requires rescanning the same slide multiple times. Moreover, visual assessment and quantification of biomarkers is limited by inter and intra-observer variability. We hereby present an automated multispectral whole-slide imaging system integrated with image analysis for detection of upto 30-color multiplex assay with spectral unmixing and cell segmentation functionality. Material and Methods Multiplex IHC whole slide imaging Formalin Fixed Paraffin Embedded rat brain; indirect IHC stained sections with 10 antibodies using photostable fluorophores were obtained. The staining was based on antigenic integrity and antibody binding affinity. Optra's multispectral fluorescent whole slide scanning process was optimized to image sensitive antigens in early cycles and robust antigens in latter cycles. The fully automated fluorescence scanning system imaged weakly expressing biomarkers in the visible range (480-620 nm), highly expressing targets in the violet/blue (405-440 nm), far-red (650-680 nm) and NIR (700-800 nm) range across the entire usable light spectrum (350-800 nm). ResultsWe could simultaneously detect up to 30 biomarkers within a single tissue section, using 12 staining-imaging-stripping cycles, with 10 antibodies screened per cycle. Software measures enabled separation of spectrally overlapping markers in the multiplex assay. A computer-controlled X-Y-Z stage used autofocusing at 20X/40X magnification. Integrated image analysis solution performed comprehensive evaluation of antigens of interest with accurate quantification of the target proteins based on parameters including but not limited to hue value/width, intensity threshold scoring, morphological characterization, pixel-count thresholding and color saturation. Conclusion This proposed solution is efficient compared to the current imaging methods to tackle the aforementioned image acquisition and image analysis challenges associated with multiplexing assays. Note: This abstract was not presented at the meeting. Citation Format: Abhi Gholap, Anagha P. Jadhav, Suraj Somwanshi, Gurunath Kamble, Isha Doshi, Jiaoti Huang. Systems pathology by Optra 30 color multiplex immunofluorescence automated whole slide imaging system integrated with image analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1149.

Rapid and robust whole slide imaging based on LED-array illumination and color-multiplexed single-shot autofocusing.

Digital pathology is experiencing an exponential period of growth catalyzed by advancements in imaging hardware and progresses in machine learning. The use of whole slide imaging (WSI) for digital pathology has recently been cleared for primary diagnosis in the US. The demand for using frozen section procedure for rapid identification of cancerous tissue during surgery is another driving force for the development of WSI. A conventional WSI system scans the tissue slide to different positions and acquires the digital images. In a typical implementation, a focus map is created prior to the scanning process, leading to significant overhead time and a necessity for high positional accuracy of the mechanical system. The resulting cost of WSI system is often prohibitive for frozen section procedure during surgery. We report a novel WSI scheme based on a programmable LED array for sample illumination. In between two regular brightfield image acquisitions, we acquire one additional image by turning on a red and a green LED for color multiplexed illumination. We then identify the translational shift of the red- and green-channel images by maximizing the image mutual information or cross-correlation. The resulting translational shift is used for dynamic focus correction in the scanning process. Since we track the differential focus during adjacent acquisitions, there is no positional repeatability requirement in our scheme. We demonstrate a prototype WSI platform with a mean focusing error of ~0.3 microns. Different from previous implementations, this prototype platform requires no focus map surveying, no secondary camera or additional optics, and allows for continuous sample motion in the focus tracking process. A programmable LED array can be used for color-multiplexed single-shot autofocusing in WSI. The reported scheme may enable the development of cost-effective WSI platforms without positional repeatability requirement. It may also provide a turnkey solution for other high-content microscopy applications.