ArticleDiagnostics (Basel, Switzerland)2024
Segmentation and Multi-Timepoint Tracking of 3D Cancer Organoids from Optical Coherence Tomography Images Using Deep Neural Networks.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight.Bioactive materials · 2027Review
- Review
- Bridging In Vitro and Murine Breast Cancer Models: Advanced Imaging Across Multiscale Experimental Platforms.Cancers · 2026Review
- Longitudinal drug response assessment of tumor organoids based on optical attenuation coefficient and multi-dimensional morphological characterization.Biomedical optics express · 2026Article
- Next-Generation In Vitro Pulmonary Platforms for Respiratory Disease Modelling and Therapeutic Development: Current Advances and Future Prospects.Medicina (Kaunas, Lithuania) · 2026Review
- Optical coherence photoacoustic microscopy for 3D cancer model imaging with AI-assisted organoid analysis.Light, science & applications · 2026Article
- Artificial Intelligence in Organoid-Based Disease Modeling: A New Frontier in Precision Medicine.Biomimetics (Basel, Switzerland) · 2025Review
- Temporal reassignment and correspondence evaluation with quality control for time-course imaging of 3D cell culture.Cell reports methods · 2025Article
- Quantitative and longitudinal monitoring of cancer cell invasion in a three-dimensional in vitro model of oral cancer using optical coherence tomography.Scientific reports · 2025Article
- Application and challenges of tumor organoid technology in precision immunotherapy.Frontiers in immunology · 2025Review
- Revolutionising oral organoids with artificial intelligence.Biomaterials translational · 2024Review
- Development of a reliable method for human triple-negative breast cancer organotypic culture: Improving imaging and genomic studies in 3D cultures.Journal of tissue engineeringArticle
Corrections and comments
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Authors and funding
23 authors.
Funding
Abstract
Recent years have ushered in a transformative era in in vitro modeling with the advent of organoids, three-dimensional structures derived from stem cells or patient tumor cells. Still, fully harnessing the potential of organoids requires advanced imaging technologies and analytical tools to quantitatively monitor organoid growth. Optical coherence tomography (OCT) is a promising imaging modality for organoid analysis due to its high-resolution, label-free, non-destructive, and real-time 3D imaging capabilities, but accurately identifying and quantifying organoids in OCT images remain challenging due to various factors. Here, we propose an automatic deep learning-based pipeline with convolutional neural networks that synergistically includes optimized preprocessing steps, the implementation of a state-of-the-art deep learning model, and ad-hoc postprocessing methods, showcasing good generalizability and tracking capabilities over an extended period of 13 days. The proposed tracking algorithm thoroughly documents organoid evolution, utilizing reference volumes, a dual branch analysis, key attribute evaluation, and probability scoring for match identification. The proposed comprehensive approach enables the accurate tracking of organoid growth and morphological changes over time, advancing organoid analysis and serving as a solid foundation for future studies for drug screening and tumor drug sensitivity detection based on organoids.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.