ArticleScientific reports2016
3D texture analysis for classification of second harmonic generation images of human ovarian cancer.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Artificial Second Harmonic Generation Image Feature Tuning with Generative Models and SeFa Semantic Analysis.JPhys photonics · 2026Article
- Deep Learning Framework for Early Detection of Pancreatic Cancer Using Multi-modal Medical Imaging Analysis.Journal of imaging informatics in medicine · 2026Article
- Label-free multiphoton microscopy and machine learning for recognition of hepatocellular carcinoma.Scientific reports · 2026Article
- Generalized 3D quasi-phase-matching model of image contrast in second harmonic generation microscopy of fibrillar collagen architectures.JPhys photonics · 2026Article
- Intraoperative biopsy imaging of lung cancer risk.Communications medicine · 2026Article
- Learning-enhanced 3D fiber orientation mapping in thick cardiac tissues.Biomedical optics express · 2025Article
- Quantitative and qualitative metrics of tumor stroma in predicting ovarian cancer outcomes and expansion of its study with AI-based tools.Molecular therapy. Oncology · 2025Review
- Collagen fiber density observed in metastatic ovarian cancer promotes tumor cell adhesion.Acta biomaterialia · 2025Article
- Mechanisms on How Matricellular Microenvironments Sustain Idiopathic Pulmonary Fibrosis.International journal of molecular sciences · 2025Review
- Prospects and challenges of deep learning in gynecologic malignancies.Frontiers in oncology · 2025Review
- Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy.Light, science & applications · 2024Review
- Tumor-associated fibrosis: a unique mechanism promoting ovarian cancer metastasis and peritoneal dissemination.Cancer metastasis reviews · 2024Review
- The importance of 3D fibre architecture in cancer and implications for biomaterial model design.Nature reviews. Cancer · 2024Review
- Preoperative Molecular Subtype Classification Prediction of Ovarian Cancer Based on Multi-Parametric Magnetic Resonance Imaging Multi-Sequence Feature Fusion Network.Bioengineering (Basel, Switzerland) · 2024Article
- A Perspective Review: Analyzing Collagen Alterations in Ovarian Cancer by High-Resolution Optical Microscopy.Cancers · 2024Review
- Imaging and quantification of the tumor microenvironment of triple negative breast cancer using TPEF and scanning laser optical tomography.Biomedical optics express · 2023Article
- A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women.Diagnostics (Basel, Switzerland) · 2023Article
- Label-free multiphoton microscopy enables histopathological assessment of colorectal liver metastases and supports automated classification of neoplastic tissue.Scientific reports · 2023Article
- Assessing the roles of collagen fiber morphology and matrix stiffness on ovarian cancer cell migration dynamics using multiphoton fabricated orthogonal image-based models.Acta biomaterialia · 2022Article
- Machine learning-based rapid diagnosis of human borderline ovarian cancer on second-harmonic generation images.Biomedical optics express · 2021Article
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9 authors.
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Abstract
Remodeling of the collagen architecture in the extracellular matrix (ECM) has been implicated in ovarian cancer. To quantify these alterations we implemented a form of 3D texture analysis to delineate the fibrillar morphology observed in 3D Second Harmonic Generation (SHG) microscopy image data of normal (1) and high risk (2) ovarian stroma, benign ovarian tumors (3), low grade (4) and high grade (5) serous tumors, and endometrioid tumors (6). We developed a tailored set of 3D filters which extract textural features in the 3D image sets to build (or learn) statistical models of each tissue class. By applying k-nearest neighbor classification using these learned models, we achieved 83-91% accuracies for the six classes. The 3D method outperformed the analogous 2D classification on the same tissues, where we suggest this is due the increased information content. This classification based on ECM structural changes will complement conventional classification based on genetic profiles and can serve as an additional biomarker. Moreover, the texture analysis algorithm is quite general, as it does not rely on single morphological metrics such as fiber alignment, length, and width but their combined convolution with a customizable basis set.
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