ArticleCancers2019
Segmentation and Grade Prediction of Colon Cancer Digital Pathology Images Across Multiple Institutions.
Article in Cancers, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Aligning computational pathology with clinical practice for colorectal cancer.NPJ precision oncology · 2025Review
- Weakly Supervised Gland Segmentation Based on Hierarchical Attention Fusion and Pixel Affinity Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Role of artificial intelligence in digital pathology for gynecological cancers.Computational and structural biotechnology journal · 2024Review
- Colorectal cancer histopathology image analysis: A comparative study of prognostic values of automatically extracted morphometric nuclear features in multispectral and red-blue-green imagery.Histology and histopathology · 2024Article
- Computer-aided diagnosis system for grading brain tumor using histopathology images based on color and texture features.BMC medical imaging · 2024Article
- Optimizing colorectal cancer segmentation with MobileViT-UNet and multi-criteria decision analysis.PeerJ. Computer science · 2024Article
- Computer-aided colorectal cancer diagnosis: AI-driven image segmentation and classification.PeerJ. Computer science · 2024Article
- Segmentation of human functional tissue units in support of a Human Reference Atlas.Communications biology · 2023Article
- Impact of Stain Normalization on Pathologist Assessment of Prostate Cancer: A Comparative Study.Cancers · 2023Article
- Unsupervised segmentation of biomedical hyperspectral image data: tackling high dimensionality with convolutional autoencoders.Biomedical optics express · 2022Article
- Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis Techniques.Sensors (Basel, Switzerland) · 2022Review
- Review
- Colon Cancer Grading Using Infrared Spectroscopic Imaging-Based Deep Learning.Applied spectroscopy · 2022Article
- Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient's Stratification.Frontiers in oncology · 2022Review
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Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Distinguishing benign from malignant disease is a primary challenge for colon histopathologists. Current clinical methods rely on qualitative visual analysis of features such as glandular architecture and size that exist on a continuum from benign to malignant. Consequently, discordance between histopathologists is common. To provide more reliable analysis of colon specimens, we propose an end-to-end computational pathology pipeline that encompasses gland segmentation, cancer detection, and then further breaking down the malignant samples into different cancer grades. We propose a multi-step gland segmentation method, which models tissue components as ellipsoids. For cancer detection/grading, we encode cellular morphology, spatial architectural patterns of glands, and texture by extracting multi-scale features: (i) Gland-based: extracted from individual glands, (ii) local-patch-based: computed from randomly-selected image patches, and (iii) image-based: extracted from images, and employ a hierarchical ensemble-classification method. Using two datasets (Rawalpindi Medical College (RMC),
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