Category Archives: Tools

Digital Histology, Infrastructure / IT, Tools

A performance comparison of using Openslide open and read WSIs via AWS S3 for S3FS and Goofys

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Background

These days, developers In digital pathology frequently use OpenSlide to read whole slide images (WSIs). Since WSIs are typically large, transferring and sharing them can be challenging. Cloud storage provides a scalable, durable, and secure solution for storing and sharing WSIs remotely. By mounting cloud storage as a local file system, existing tools can access WSIs without additional modifications.

AWS S3 (Simple Storage Service) and MinIO (S3 compliant, onsite and open-source) are a widely used, robust, and highly scalable cloud storage service. While Mountpoint for AWS S3 is a promising option for treating cloud storage as a local file system, several open-source projects also allow users to mount an S3 bucket as a local file system. Among the most well-known are s3fs and Goofys. In this post, we’ll compare the performance of these two popular open-source solutions.

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Digital Histology, Python, Tools

Serialization and storage of GeoJson in Digital Pathology

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GeoJSON, a widely used format based on JSON (JavaScript Object Notation), is specifically designed for encoding a variety of geographic data structures. This versatile format excels in representing simple geographical features, such as points, lines, and polygons, along with their non-spatial attributes. In the realm of digital pathology, GeoJSON has emerged as a common format for storing annotations, enabling precise documentation of regions of interest, cellular structures, and other critical details within pathology images. The popularity of GeoJSON in this field is bolstered by its broad support across numerous tools (e.g., Qupath) and thus facilitates seamless integration and analysis in digital pathology workflows.

Despite its widespread adoption, there are several open questions regarding the efficient use of GeoJSON that can significantly impact performance. One key concern is the best method for storing GeoJSON in a compressed format to minimize storage requirements while preserving the integrity of the data. Efficient compression techniques are crucial, especially when dealing with large-scale pathology datasets.

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Python, Tools

Data Exploration Of Features For Outcome Association In Digital Pathology

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Introduction

In the field of digital pathology, a frequent approach for the creation of image-based biomarkers involves extracting features from scanned pathology slides. These features, which are often related to the morphology or spatial distribution of various tissue or cell types, provide valuable insights into the underlying biology of diseases. In cancer research, it is particularly important to examine how these features correlate with clinical outcomes such as overall survival (OS), progression-free survival (PFS), or other binary outcomes (e.g., response to a specific treatment).

Here we release python code that can be executed in a notebook to facilitate this process. It accepts a pandas DataFrame and generates a one-page summary PDF file, facilitating the analysis of individual features and their potential correlation with clinical outcomes.

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