ACROBAT -- a multi-stain breast cancer histological whole-slide-image data set from routine diagnostics for computational pathology

The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is an essential part of the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly adopted into diagnostic guidelines and routine workflows to manually assess status and scoring of several established biomarkers, including ER, PGR, HER2 and KI67. However, this is a task that can also be facilitated by computational pathology image analysis methods. The research in computational pathology has recently made numerous substantial advances, often based on publicly available whole slide image (WSI) data sets. However, the field is still considerably limited by the sparsity of public data sets. In particular, there are no large, high quality publicly available data sets with WSIs of matching IHC and H&E-stained tissue sections. Here, we publish the currently largest publicly available data set of WSIs of tissue sections from surgical resection specimens from female primary breast cancer patients with matched WSIs of corresponding H&E and IHC-stained tissue, consisting of 4,212 WSIs from 1,153 patients. The primary purpose of the data set was to facilitate the ACROBAT WSI registration challenge, aiming at accurately aligning H&E and IHC images. For research in the area of image registration, automatic quantitative feedback on registration algorithm performance remains available through the ACROBAT challenge website, based on more than 37,000 manually annotated landmark pairs from 13 annotators. Beyond registration, this data set has the potential to enable many different avenues of computational pathology research, including stain-guided learning, virtual staining, unsupervised pre-training, artefact detection and stain-independent models

The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results establish the current state-of-the-art in WSI registration and guide researchers in selecting and developing methods

Reconstruction and Analysis of Tissue Types in 3D

This project derives from a collection of image data that analyzes mouse aorta by locating plaque (atherosclerosis), using a mouse as a model organism. With the implementation of artificial intelligence (AI), which is a field that uses rationality through statistics and computer algorithms that produce automated decision-making systems to create usable outcomes, we examine large image datasets of bilaterally dissected frozen mouse aorta with multiple stains. All DigitalSlide and part of the FR1 and FR19 image sets were stained with Hematoxylin and Eosin (H&E). Anti-mouse Mac-3 antibody and FR-β Immunohistochemistry to detect different macrophages were used in the remaining FR1 and FR19 image sets. Curation was performed to remove extraneous tissue to allow alignment of image coordinates through image registration using VALIS registration software. The goal is to create a pipeline of tools to produce an automated workflow that enables reconstruction of histological tissue into 3D to create a natural spatial context. The dataset consists of about three hundred high resolution whole slide images (WSI), where processing requires a significantly efficient computing capacity. CSC Puhti, a high-performance computing environment was used to curate and register the images. VALIS software was implemented to provide image stacks with coordinate spatial alignment to each other. With QuPath, manual annotations of the images were made, creating labeled data that enabled the use of supervised machine learning models such as decision trees, k nearest neighbor, and artificial neural network (ANN) to train an object classifier. An automated dataframe calculator created with Python was made to calculate the Target Registered Error (TRE) that measured the distance between the manual annotation points on sequential images to average the error between points. Results show that after adjustments with the training and test data, the Random Trees algorithm offers the most compatible classifier in identifying plaque in the resulting image stacks during segmentation for images with low TRE. Image registration with VALIS hones adjacent images to align to each other from within the middle of the stack, however preventing obscured image results. The combination of all of these procedures together are applicable to find pixel-wise similarity to generate reconstruction of tissue into a 3D volume