Analyzing Whole Slide Images (WSIs) is an important part of modern digital pathology because it allows researchers to extract many features from tissue samples. However, traditional methods using tools like CellProfiler can be slow and involve several steps: dividing the WSI into smaller patches, extracting features from each patch, and then combining those features back into the full image.��

To make this process faster and simpler, we developed PySpatial, a high-speed toolkit designed specifically for analyzing WSIs. PySpatial improves the usual process by working directly on selected regions of the image, which reduces unnecessary steps. It uses special techniques like rtree-based spatial indexing and matrix-based computation to quickly find and process these regions.��

We tested PySpatial on two datasets—one involving Perivascular Epithelioid Cell (PEC) tumors and another from the Kidney Precision Medicine Project (KPMP)—and found major improvements in performance. For small and scattered features in the PEC data, PySpatial was almost 10 times faster than CellProfiler. For larger structures like glomeruli and arteries in the KPMP data, it was twice as fast.��

These results show that PySpatial can speed up large-scale WSI analysis while keeping accuracy high, making it a useful tool for advancing digital pathology.��

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Segmenting large, detailed images of tissue samples in computational pathology is very challenging because tissues have complex structures at different scales. For example, in kidney pathology, you have larger regions like the cortex and medulla, as well as smaller structures like glomeruli, tubules, blood vessels, and various cell types.��

To tackle this, we developed a new method called Hierarchical Adaptive Taxonomy Segmentation (HATs). HATs is designed to accurately identify and label these different kidney structures in large panoramic images by using a detailed understanding of kidney anatomy. The key features of HATs include:��

1. A special technique that uses the spatial relationships between 15 different tissue types to create a flexible model that can be applied to different scales, from large regions down to individual cells.��

1. A simplified way of representing these tissue structures in a matrix format, making it easier to process the whole panoramic image.��

1. Integration of a new AI model (EfficientSAM) to help extract features from the images without needing manual inputs, making it more adaptable and efficient.��

Our tests showed that HATs effectively combines clinical knowledge and imaging techniques to accurately segment over 15 types of tissue structures. The code for HATs is available at .��

Fig. 1.����

Knowledge transformation from kidney anatomy to a hierarchical taxonomy tree. This figure demonstrates the transformation of intricate clinical anatomical relationships within the kidney into a hierarchical taxonomy tree. (a) Pathologists examine histopathology in accordance with kidney anatomy. (b) This study revisits kidney anatomy using a hierarchical semantic taxonomy for panoramic segmentation, covering 15 classes across regions, units, and cells. The tree incorporates spatial relationships into a semi-supervised learning paradigm and uses hierarchical scale information as prior knowledge to weigh the relationship between classes.��

New molecular imaging methods can capture detailed genetic and protein information directly from tissues, allowing scientists to study diseases while keeping the original structure of the tissue intact. By combining this molecular data with traditional tissue images, researchers can learn more about how different parts of tissues are affected by diseases. However, making sense of all this complex data, especially when comparing many samples, is challenging.��

To help with this, we created MILWRM, a Python tool that can quickly find and label different areas within tissue samples. MILWRM analyzes images and groups similar parts of the tissue together, making it easier to identify specific regions.��

We tested MILWRM on various tissue samples, including human colon polyps, lymph nodes, mouse kidneys, and mouse brain slices. The tool was able to distinguish different types of polyps and identify unique areas in the brain based on their molecular characteristics. MILWRM helps researchers understand the structure and molecular features of tissues, making it a valuable tool for studying diseases.��

Fig. 1: The workflow of the MILWRM pipeline.��

MILWRM begins with constructing a tissue labeler object from all the sample slides that undergo data preprocessing, serialization, and subsampling to create a randomly subsampled dataset used for k-means model construction. This subsampled data is used to find an optimal number of tissue domains, and k-selection using the adjusted inertia method. Finally, a k-means model is constructed, and each pixel is assigned a TD. Each TD has a distinct domain profile describing its molecular features. MILWRM also provides quality control metrics such as confidence scores (created with BioRender.com).��

This study addresses a key challenge in multiplex immunofluorescence (MxIF) imaging, a technique used in biomedical research to provide detailed insights into cell structures and spatial organization. While MxIF imaging, such as using DAPI staining to identify cell nuclei and CD20 staining for cell membranes, is invaluable for understanding cell composition, it suffers from saturation artifacts. These artifacts occur when certain areas of the image become overly bright, making it difficult to analyze individual cells accurately. Existing methods for correcting these saturation issues, like gamma correction, often fall short because they assume uniform saturation, which is rarely the case in practice.

The authors propose a novel solution using a hybrid generative adversarial network (GAN) called HD-mixGAN, which combines two different types of neural networks (CycleGAN and Pix2pixHD) to correct saturation artifacts. This approach takes advantage of both small datasets where paired (before and after) images are available and larger datasets that only have unpaired images of over-saturated regions. By generating synthetic data from the unpaired datasets using a CycleGAN and combining it with real data, the model effectively learns to correct saturation artifacts, improving the overall image quality.

The method was tested in a task to detect cell nuclei, where it significantly outperformed traditional methods, improving the accuracy (F1 score) by 6%. This approach represents the first focused effort to address saturation issues in multi-round MxIF imaging, providing a data-driven solution that enhances the accuracy of single-cell analysis. The study also makes its code and implementation freely available, facilitating further research and applications in this area.

Eosinophilic Esophagitis (EoE) is a chronic, immune/antigen-mediated esophageal disease characterized by symptoms related to esophageal dysfunction and histological evidence of eosinophil-dominant inflammation. Due to the complex microscopic representation of EoE in imaging, current manual identification methods are labor-intensive and prone to inaccuracies.

This study introduces an open-source toolkit, named Open-EoE, designed for end-to-end whole slide image (WSI) level eosinophil (Eos) detection with a single line of command via Docker. The toolkit supports three state-of-the-art deep learning-based object detection models and optimizes performance through an ensemble learning strategy, enhancing precision and reliability.

Experimental results demonstrate that Open-EoE can efficiently detect Eos on a testing set of 289 WSIs. At the widely accepted diagnostic threshold of ≥15 Eos per high power field (HPF) for EoE, Open-EoE achieved an accuracy of 91%, showing good consistency with pathologist evaluations. This suggests a promising avenue for integrating machine learning methodologies into the diagnostic process for EoE.

Precise identification of multiple cell classes in high-resolution Giga-pixel whole slide imaging (WSI) is essential for various clinical applications. Building an AI model for this purpose usually requires pixel-level annotations, which are time-consuming, require domain expertise (e.g., pathologists), and are prone to errors, particularly when differentiating intricate cell types (e.g., podocytes and mesangial cells) through visual inspection alone.

A recent study found that lay annotators could sometimes outperform domain experts in labeling tasks when provided with additional immunofluorescence (IF) images for reference, a method termed molecular-empowered learning. However, the manual delineation required for these annotations remains a resource-intensive task.

This paper explores bypassing pixel-level delineation by using the recent segment anything model (SAM) with weak box annotations in a zero-shot learning approach. SAM’s capability to generate pixel-level annotations from box annotations is leveraged to train a segmentation model. The findings indicate that SAM-assisted molecular-empowered learning (SAM-L) reduces the annotation effort required from lay annotators by necessitating only weak box annotations, without compromising annotation accuracy or the performance of the deep learning-based segmentation.

This research represents a significant advancement in making the annotation process for training pathological image segmentation more accessible, relying solely on non-expert annotators.

Crohn’s disease (CD) is a chronic, relapsing inflammatory condition that affects various segments of the gastrointestinal tract. The activity of CD is determined through histological findings, particularly by examining the density of neutrophils in Hematoxylin and Eosin (H&E) stained images. However, a deeper understanding of morphometry and local cell arrangements beyond simple cell counting and tissue morphology is needed.

To address this, researchers characterized six distinct cell types from H&E images and developed a novel method to analyze the local spatial signature of each cell. This method involves creating a 10-cell neighborhood matrix, which represents the arrangement of neighboring cells around each individual cell. By utilizing t-SNE for non-linear spatial projection, the study presented these arrangements in scatter-plot and Kernel Density Estimation contour-plot formats. The analysis examined patterns of differences in the cellular environment, focusing on the odds ratio of spatial patterns between active CD and control groups, using data collected from two research institutes.

The findings revealed heterogeneous nearest-neighbor patterns, indicating distinct tendencies of cell clustering, especially in the rectum region. These variations underscore the influence of data heterogeneity on cell spatial arrangements in CD patients. Additionally, the observed spatial distribution disparities between the two research sites highlight the importance of collaborative efforts among healthcare organizations to ensure comprehensive analysis.

All tools used in the research analysis pipeline are available at .

When dealing with giga-pixel digital pathology in whole-slide imaging (WSI), a significant portion of data records is relevant during each analysis operation. The computational bottleneck often lies in the input-output (I/O) system, especially during patch-level processing, which imposes a substantial I/O load on the computer system. However, this data management process can be further optimized by parallelizing it, as patch-level image processes are typically independent across different patches.

This paper discusses efforts to address this data access challenge by implementing the Adaptable IO System version 2 (ADIOS2). The focus is on constructing and releasing a digital pathology-centric pipeline using ADIOS2, which streamlines data management across WSIs. Additionally, strategies have been developed to reduce data retrieval times.

The performance evaluation covers two key scenarios: (1) a CPU-based image analysis scenario (“CPU scenario”) and (2) a GPU-based deep learning framework scenario (“GPU scenario”). The findings reveal significant outcomes. In the CPU scenario, ADIOS2 achieves an impressive two-fold speed-up compared to the brute-force approach. In the GPU scenario, its performance is on par with the state-of-the-art GPU I/O acceleration framework, NVIDIA Magnum IO GPU Direct Storage (GDS).

This study represents one of the initial instances of utilizing ADIOS2 in the field of digital pathology. The source code for this implementation has been made publicly available at .

Understanding how cells communicate, co-locate, and interrelate is essential for grasping human physiology. Hematoxylin and eosin (H&E) staining is widely used in clinical studies and research. The Colon Nucleus Identification and Classification (CoNIC) Challenge recently advanced artificial intelligence to label six cell types on H&E stained colon tissues. However, this only covers a small fraction of potential cell classifications, missing various epithelial (progenitor, endocrine, goblet), lymphocyte (B cells, helper T cells, cytotoxic T cells), and connective tissue (fibroblasts, stromal) subtypes.

To address this limitation, the study proposes using inter-modality learning to label previously unclassifiable cell types on virtual H&E images. The researchers utilized multiplexed immunofluorescence (MxIF) histology imaging to identify 14 cell type subclasses. By performing style transfer, they synthesized virtual H&E images from MxIF and transferred the detailed labels from MxIF to these virtual H&E images. They then assessed the effectiveness of this learning approach.

The results demonstrated that helper T cells and progenitor cells could be identified with positive predictive values of 0.34±0.15 (prevalence 0.03±0.01) and 0.47±0.1 (prevalence 0.07±0.02) respectively on virtual H&E images. This method represents a promising step toward automating cell type annotation in digital pathology, significantly enhancing the capability to classify diverse cell types beyond current limitations.