ArticleJournal of pathology informatics2013
Eliminating tissue-fold artifacts in histopathological whole-slide images for improved image-based prediction of cancer grade.
Article in Journal of pathology informatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Mitigating Domain Shift and Dataset Bias in Deep Learning Models for Cancer Diagnostics.Journal of imaging informatics in medicine · 2026Review
- Deep Learning-Assisted Quality Control of Histology Teaching Slides: Detection and Localization of Tissue Fold Artifacts in H&E-Stained Images.Bioengineering (Basel, Switzerland) · 2026Article
- OncoTrace-TOO: Interpretable Machine Learning Framework for Cancer Tissue-of-Origin Identification Using Transcriptomic Signatures.Cancer reports (Hoboken, N.J.) · 2025Article
- Fast TILs-A pipeline for efficient TILs estimation in non-small cell Lung cancer.Journal of pathology informatics · 2025Article
- Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review.Journal of pathology informatics · 2024Review
- Equipping computational pathology systems with artifact processing pipelines: a showcase for computation and performance trade-offs.BMC medical informatics and decision making · 2024Article
- [Artificial intelligence for pathology-how, where, and why?]Pathologie (Heidelberg, Germany) · 2024Article
- Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance.Diagnostics (Basel, Switzerland) · 2023Review
- An automatic entropy method to efficiently mask histology whole-slide images.Scientific reports · 2023Article
- The pathological risk score: A new deep learning-based signature for predicting survival in cervical cancer.Cancer medicine · 2023Article
- Developmental Deconvolution for Classification of Cancer Origin.Cancer discovery · 2022Article
- The need for measurement science in digital pathology.Journal of pathology informatics · 2022Article
- Computational pathology in ovarian cancer.Frontiers in oncology · 2022Review
- What is new in computer vision and artificial intelligence in medical image analysis applications.Quantitative imaging in medicine and surgery · 2021Review
- Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association.Applied immunohistochemistry & molecular morphology : AIMM · 2021Article
- An Interactive Pipeline for Quantitative Histopathological Analysis of Spatially Defined Drug Effects in Tumors.Journal of pathology informatics · 2021Article
- Overcoming an Annotation Hurdle: Digitizing Pen Annotations from Whole Slide Images.Journal of pathology informatics · 2021Article
- Decidual Vasculopathy Identification in Whole Slide Images Using Multiresolution Hierarchical Convolutional Neural Networks.The American journal of pathology · 2020Article
- Enhancing the Value of Histopathological Assessment of Allograft Biopsy Monitoring.Transplantation · 2019Review
- Machine Learning Methods for Histopathological Image Analysis.Computational and structural biotechnology journal · 2018Review
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3 authors.
Funding
Abstract
backgroundAnalysis of tissue biopsy whole-slide images (WSIs) depends on effective detection and elimination of image artifacts. We present a novel method to detect tissue-fold artifacts in histopathological WSIs. We also study the effect of tissue folds on image features and prediction models. MATERIALS AND
methodsWe use WSIs of samples from two cancer endpoints - kidney clear cell carcinoma (KiCa) and ovarian serous adenocarcinoma (OvCa) - publicly available from The Cancer Genome Atlas. We detect tissue folds in low-resolution WSIs using color properties and two adaptive connectivity-based thresholds. We optimize and validate our tissue-fold detection method using 105 manually annotated WSIs from both cancer endpoints. In addition to detecting tissue folds, we extract 461 image features from the high-resolution WSIs for all samples. We use the rank-sum test to find image features that are statistically different among features extracted from the same set of WSIs with and without folds. We then use features that are affected by tissue folds to develop models for predicting cancer grades.
resultsWhen compared to the ground truth, our method detects tissue folds in KiCa with 0.50 adjusted Rand index (ARI), 0.77 average true rate (ATR), 0.55 true positive rate (TPR), and 0.98 true negative rate (TNR); and in OvCa with 0.40 ARI, 0.73 ATR, 0.47 TPR, and 0.98 TNR. Compared to two other methods, our method is more accurate in terms of ARI and ATR. We found that 53 and 30 image features were significantly affected by the presence of tissue-fold artifacts (detected using our method) in OvCa and KiCa, respectively. After eliminating tissue folds, the performance of cancer-grade prediction models improved by 5% and 1% in OvCa and KiCa, respectively.
conclusionThe proposed connectivity-based method is more effective in detecting tissue folds compared to other methods. Reducing tissue-fold artifacts will increase the performance of cancer-grade prediction models.
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