CellViT: Vision Transformers for precise cell segmentation and classification

Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications. However, it is a challenging task due to nuclei variances in staining and size, overlapping boundaries, and nuclei clustering. While convolutional neural networks have been extensively used for this task, we explore the potential of Transformer-based networks in combination with large scale pre-training in this domain. Therefore, we introduce a new method for automated instance segmentation of cell nuclei in digitized tissue samples using a deep learning architecture based on Vision Transformer called CellViT. CellViT is trained and evaluated on the PanNuke dataset, which is one of the most challenging nuclei instance segmentation datasets, consisting of nearly 200,000 annotated nuclei into 5 clinically important classes in 19 tissue types. We demonstrate the superiority of large-scale in-domain and out-of-domain pre-trained Vision Transformers by leveraging the recently published Segment Anything Model and a ViT-encoder pre-trained on 104 million histological image patches - achieving state-of-the-art nuclei detection and instance segmentation performance on the PanNuke dataset with a mean panoptic quality of 0.50 and an F1-detection score of 0.83. The code is publicly available at https://github.com/TIO-IKIM/CellViT.

Histology-Based Prediction of Therapy Response to Neoadjuvant Chemotherapy for Esophageal and Esophagogastric Junction Adenocarcinomas Using Deep Learning

PURPOSE

Quantifying treatment response to gastroesophageal junction (GEJ) adenocarcinomas is crucial to provide an optimal therapeutic strategy. Routinely taken tissue samples provide an opportunity to enhance existing positron emission tomography-computed tomography (PET/CT)-based therapy response evaluation. Our objective was to investigate if deep learning (DL) algorithms are capable of predicting the therapy response of patients with GEJ adenocarcinoma to neoadjuvant chemotherapy on the basis of histologic tissue samples.

METHODS

This diagnostic study recruited 67 patients with I-III GEJ adenocarcinoma from the multicentric nonrandomized MEMORI trial including three German university hospitals TUM (University Hospital Rechts der Isar, Munich), LMU (Hospital of the Ludwig-Maximilians-University, Munich), and UME (University Hospital Essen, Essen). All patients underwent baseline PET/CT scans and esophageal biopsy before and 14-21 days after treatment initiation. Treatment response was defined as a ≥35% decrease in SUVmax from baseline. Several DL algorithms were developed to predict PET/CT-based responders and nonresponders to neoadjuvant chemotherapy using digitized histopathologic whole slide images (WSIs).

RESULTS

The resulting models were trained on TUM (n = 25 pretherapy, n = 47 on-therapy) patients and evaluated on our internal validation cohort from LMU and UME (n = 17 pretherapy, n = 15 on-therapy). Compared with multiple architectures, the best pretherapy network achieves an area under the receiver operating characteristic curve (AUROC) of 0.81 (95% CI, 0.61 to 1.00), an area under the precision-recall curve (AUPRC) of 0.82 (95% CI, 0.61 to 1.00), a balanced accuracy of 0.78 (95% CI, 0.60 to 0.94), and a Matthews correlation coefficient (MCC) of 0.55 (95% CI, 0.18 to 0.88). The best on-therapy network achieves an AUROC of 0.84 (95% CI, 0.64 to 1.00), an AUPRC of 0.82 (95% CI, 0.56 to 1.00), a balanced accuracy of 0.80 (95% CI, 0.65 to 1.00), and a MCC of 0.71 (95% CI, 0.38 to 1.00).

CONCLUSION

Our results show that DL algorithms can predict treatment response to neoadjuvant chemotherapy using WSI with high accuracy even before therapy initiation, suggesting the presence of predictive morphologic tissue biomarkers.

Valuing vicinity: Memory attention framework for context-based semantic segmentation in histopathology

The segmentation of histopathological whole slide images into tumourous and non-tumourous types of tissue is a challenging task that requires the consideration of both local and global spatial contexts to classify tumourous regions precisely. The identification of subtypes of tumour tissue complicates the issue as the sharpness of separation decreases and the pathologist's reasoning is even more guided by spatial context. However, the identification of detailed tissue types is crucial for providing personalized cancer therapies. Due to the high resolution of whole slide images, existing semantic segmentation methods, restricted to isolated image sections, are incapable of processing context information beyond. To take a step towards better context comprehension, we propose a patch neighbour attention mechanism to query the neighbouring tissue context from a patch embedding memory bank and infuse context embeddings into bottleneck hidden feature maps. Our memory attention framework (MAF) mimics a pathologist's annotation procedure - zooming out and considering surrounding tissue context. The framework can be integrated into any encoder-decoder segmentation method. We evaluate the MAF on two public breast cancer and liver cancer data sets and an internal kidney cancer data set using famous segmentation models (U-Net, DeeplabV3) and demonstrate the superiority over other context-integrating algorithms - achieving a substantial improvement of up to 17% on Dice score. The code is publicly available at https://github.com/tio-ikim/valuing-vicinity.