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

Robust multiresolution and multistain background segmentation in whole slide images

Background segmentation is an important step in analysis of histopathological images. It allows one to remove irrelevant regions and focus on the tissue of interest. However, background segmentation is challenging due to the variability of stain colors and intensity levels across different images, modalities, and magnification levels. In this paper, we present a learning-based model for histopathology background segmentation based on convolutional neural networks. We compare two multiresolution approaches to deal with the variability of magnification in histopathology images: (i) model that uses upscaling of smaller patches of the image, and (ii) model simultaneously trained on multiple resolution levels. Our model is characterized by solid performance both in multiresolution and multistain dyes (H &E and IHC), achieving good performance on publicly available dataset. The quantitative scores are, in terms of the Dice score, close to 94.71. The qualitative analysis presents strong performance on previously unseen cases from different distributions and various dyes. We freely release the model, weights, and ground-truth annotations to promote the open science and reproducible research

Unsupervised method for intra-patient registration of brain magnetic resonance images based on objective function weighting by inverse consistency ::contribution to the BraTS-Reg challenge

Registration of brain scans with pathologies is difficult, yet important research area. The importance of this task motivated researchers to organize the BraTS-Reg challenge, jointly with IEEE ISBI 2022 and MICCAI 2022 conferences. The organizers introduced the task of aligning pre-operative to follow-up magnetic resonance images of glioma. The main difficulties are connected with the missing data leading to large, nonrigid, and noninvertible deformations. In this work, we describe our contributions to both the editions of the BraTS-Reg challenge. The proposed method is based on combined deep learning and instance optimization approaches. First, the instance optimization enriches the state-of-the-art LapIRN method to improve the generalizability and fine-details preservation. Second, an additional objective function weighting is introduced, based on the inverse consistency. The proposed method is fully unsupervised and exhibits high registration quality and robustness. The quantitative results on the external validation set are: (i) IEEE ISBI 2022 edition: 1.85, and 0.86, (ii) MICCAI 2022 edition: 1.71, and 0.86, in terms of the mean of median absolute error and robustness respectively. Future work could transfer the inverse consistency-based weighting directly into the deep network training