TIAToolbox: An End-to-End Toolbox for Advanced Tissue Image Analytics

Pocock, Johnathan; Graham, Simon; Vu, Quoc Dang; Jahanifar, Mostafa; Deshpande, Srijay; Hadjigeorghiou, Giorgos; Shephard, Adam; Bashir, Raja Muhammad Saad; Bilal, Mohsin; Lu, Wenqi; Epstein, D. B. A.; Minhas, Fayyaz ul Amir Afsar; Rajpoot, Nasir M.; Raza, Shan E Ahmed

doi:10.1101/2021.12.23.474029

Cited by 9 publications

(6 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There needs to be a global consensus on the clinical 'gold standard' MSI testing and its improved sensitivity and specificity over the currently known values of sensitivity and specificity, which are below 100% [72]. Recent developments in making the prediction models available as open-source software after rigorous testing are encouraging [73,74]. The open-source TIAToolBox library [73] has made models for MSI [55] and HER2 [75] predictions available for testing by the community.…”

Section: Discussion and Future Of Ai In Immunotherapymentioning

confidence: 99%

“…Recent developments in making the prediction models available as open-source software after rigorous testing are encouraging [73,74]. The open-source TIAToolBox library [73] has made models for MSI [55] and HER2 [75] predictions available for testing by the community. HEAL [74] is another end-to-end tool for histopathology analysis that contains the code for the prediction of MSI and EGFR status.…”

Section: Discussion and Future Of Ai In Immunotherapymentioning

confidence: 99%

See 1 more Smart Citation

Role of AI and digital pathology for colorectal immuno-oncology

Bilal

Nimir²,

Snead³

et al. 2022

Br J Cancer

Self Cite

View full text Add to dashboard Cite

Immunotherapy deals with therapeutic interventions to arrest the progression of tumours using the immune system. These include checkpoint inhibitors, T-cell manipulation, cytokines, oncolytic viruses and tumour vaccines. In this paper, we present a survey of the latest developments on immunotherapy in colorectal cancer (CRC) and the role of artificial intelligence (AI) in this context. Among these, microsatellite instability (MSI) is perhaps the most popular IO biomarker globally. We first discuss the MSI status of tumours, its implications for patient management, and its relationship to immune response. In recent years, several aspiring studies have used AI to predict the MSI status of patients from digital whole-slide images (WSIs) of routine diagnostic slides. We present a survey of AI literature on the prediction of MSI and tumour mutation burden from digitised WSIs of haematoxylin and eosin-stained diagnostic slides. We discuss AI approaches in detail and elaborate their contributions, limitations and key takeaways to drive future research. We further expand this survey to other IO-related biomarkers like immune cell infiltrates and alternate data modalities like immunohistochemistry and gene expression. Finally, we underline possible future directions in immunotherapy for CRC and promise of AI to accelerate this exploration for patient benefits.

show abstract

Section: Discussion and Future Of Ai In Immunotherapymentioning

confidence: 99%

Section: Discussion and Future Of Ai In Immunotherapymentioning

confidence: 99%

Role of AI and digital pathology for colorectal immuno-oncology

Bilal

Nimir²,

Snead³

et al. 2022

Br J Cancer

Self Cite

View full text Add to dashboard Cite

show abstract

“…Several computational toolboxes currently allow for training predictive models on whole slide images (WSIs) stained using hematoxylin and eosin (H&E) [26][27][28][29]. We compared the results of our approach against CLAM [26], a publicly available pipeline for WSI classification.…”

Section: Comparison To Existing Classifiersmentioning

confidence: 99%

Predicting the HER2 status in oesophageal cancer from tissue microarrays using convolutional neural networks

et al. 2023

View full text Add to dashboard Cite

Background Fast and accurate diagnostics are key for personalised medicine. Particularly in cancer, precise diagnosis is a prerequisite for targeted therapies, which can prolong lives. In this work, we focus on the automatic identification of gastroesophageal adenocarcinoma (GEA) patients that qualify for a personalised therapy targeting epidermal growth factor receptor 2 (HER2). We present a deep-learning method for scoring microscopy images of GEA for the presence of HER2 overexpression. Methods Our method is based on convolutional neural networks (CNNs) trained on a rich dataset of 1602 patient samples and tested on an independent set of 307 patient samples. We additionally verified the CNN’s generalisation capabilities with an independent dataset with 653 samples from a separate clinical centre. We incorporated an attention mechanism in the network architecture to identify the tissue regions, which are important for the prediction outcome. Our solution allows for direct automated detection of HER2 in immunohistochemistry-stained tissue slides without the need for manual assessment and additional costly in situ hybridisation (ISH) tests. Results We show accuracy of 0.94, precision of 0.97, and recall of 0.95. Importantly, our approach offers accurate predictions in cases that pathologists cannot resolve and that require additional ISH testing. We confirmed our findings in an independent dataset collected in a different clinical centre. The attention-based CNN exploits morphological information in microscopy images and is superior to a predictive model based on the staining intensity only. Conclusions We demonstrate that our approach not only automates an important diagnostic process for GEA patients but also paves the way for the discovery of new morphological features that were previously unknown for GEA pathology.

show abstract

“…Similar to YOLOv5, The same image augmentation techniques are utilized during the training of NuClick plus an extra stain augmentation technique. Stain augmentation is done using TIAToolbox 33 where nucleus patches are decomposed into their constructing H and E components, then those components are randomly altered and assembled back into an RGB image using the image stain matrix.…”

Section: Instance Segmentation From Detectionmentioning

confidence: 99%

Accurate segmentation of nuclear instances using a double-stage neural network

Jahanifar

Graham

et al. 2023

Medical Imaging 2023: Digital and Computational Pathology

View full text Add to dashboard Cite

Automatic nuclear instance segmentation is a crucial task in computational pathology as this information is required for extracting cell-based features in down-stream analysis. However, instance segmentation is a challenging task due to the nature of histology images which show large variations and irregularities in nuclei appearances and arrangements. Various deep learning-based methods have tried to tackle these challenges, however, most of these methods fail to segment the nuclei instances in crowded scenes accurately, or they are not fast enough for practical usage. In this paper, we propose a double-stage neural network for nuclear instance segmentation which leverages the power of an interactive model, NuClick, for accurate instance segmentation by replacing the user input with a nuclei detection module, YOLOv5. We optimized the proposed method to be lightweight and fast and show that it can achieve promising results when tested on the largest publicly available nuclei dataset.

show abstract

TIAToolbox: An End-to-End Toolbox for Advanced Tissue Image Analytics

Cited by 9 publications

References 46 publications

Role of AI and digital pathology for colorectal immuno-oncology

Role of AI and digital pathology for colorectal immuno-oncology

Predicting the HER2 status in oesophageal cancer from tissue microarrays using convolutional neural networks

Accurate segmentation of nuclear instances using a double-stage neural network

Contact Info

Product

Resources

About