ArticleIEEE journal of biomedical and health informatics2025
3D-Organoid-SwinNet: High-Content Profiling of 3D Organoids.
Article in IEEE journal of biomedical and health informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight.Bioactive materials · 2027Review
- Review
- Robust analysis of the tumor spectrum in a preclinical model of breast cancer reveals stable subtypes with distinct growth patterns.Computer methods and programs in biomedicine · 2026Article
- Research progress of artificial intelligence in high-throughput drug screening.Frontiers in pharmacology · 2026Review
- LOGSAGE: LOG-BASED SALIENCY FOR GUIDED ENCODING IN ROBUST NUCLEI SEGMENTATION OF IMMUNOFLUORESCENCE HISTOLOGY IMAGES.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- COUPLED SWIN TRANSFORMERS AND MULTI-APERTURES NETWORK(CSTA-NET) IMPROVES MEDICAL IMAGE SEGMENTATION.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- Multi-Aperture Transformers for 3D (MAT3D) Segmentation of Clinical and Microscopic Images.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer VisionArticle
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Authors and funding
4 authors.
Funding
Abstract
Profiling of Patient-Derived organoids is necessary for drug screening and precision medicine. This step requires accurate segmentation of three-dimensional cellular structures followed by protein readouts. While fully Convolutional Neural Networks are widely used in medical image segmentation, they struggle to capture long-range dependencies necessary for accurate segmentation. On the other hand, transformer models have shown promise in capturing long-range dependencies and self-similarities. Motivated by this, we present 3D-Organoid-SwinNet, a unique segmentation model explicitly designed for organoid semantic segmentation. We evaluated the performance of our technique using an Organoid dataset from four breast cancer subtypes. We demonstrated consistent top-tier performance in both the validation and testing phases, achieving a Dice score of 94.91 while reducing the number of parameters to 21 million. Our findings indicate that the proposed model offers a foundation for transformer-based models designed for high-content profiling of organoid models.
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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.