ArticleCancers2022
StarDist Image Segmentation Improves Circulating Tumor Cell Detection.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.
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Who cites it
53 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Deep Learning-Enhanced Biomarker Interpretation on Cytology Cell Blocks: Foundations and Emerging Opportunities in Spatial Pathobiology.The American journal of pathology · 2026Review
- Segmentation-free analysis of live-cell imaging data reveals how T cell modifications influence cancer cell aggregation dynamics.Scientific reports · 2026Article
- Flow Enabled Target Capture Halbach-based magnetic enrichment increases circulating tumor cell capture from blood in metastatic cancer patients.Molecular oncology · 2026Article
- RetinaDetachNet: Automated Deep Learning Quantification of Photoreceptor Cell Death for Neuroprotection Studies in Experimental Retinal Detachment.Translational vision science & technology · 2026Article
- Fast enrichment and detection of circulating tumor cells from large volumes of whole blood of breast cancer patients utilizing a functionalized bioaffinity CTC filtration membrane.International journal of cancer · 2026Article
- Integrative spatial profiling pipeline for determining TME architectures in archival clinical specimens using CmTSA superplex technology.Cell discovery · 2026Article
- ColonyQuant: automated quantification and morphometric analysis of pluripotent stem cell colonies.BMC bioinformatics · 2026Article
- Integrated histopathology of the human pancreas throughout stages of type 1 diabetes progression.Nature communications · 2026Article
- Spatiotemporal dynamics and regulation of suture morphology and collagen remodeling during trans-sutural distraction osteogenesis.NPJ Regenerative medicine · 2026Article
- Mapping safety in space: the emerging role of spatial transcriptomics in safe drug development.Frontiers in toxicology · 2026Review
- HiGATE: hierarchical graph attention for multi-scale tissue encoder in computational pathology.Frontiers in oncology · 2026Article
- Understanding Channel Complementarity for Multiplex Cell Segmentation.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2026Article
- Spatial omics for profiling the dynamic tumor microenvironment.Clinical & translational immunology · 2026Review
- A complete spatial map of mouse retinal ganglion cells reveals density and gene expression specializations.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Spatial omics: applications and utility in profiling the tumor microenvironment.Cancer metastasis reviews · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
After a CellSearch-processed circulating tumor cell (CTC) sample is imaged, a segmentation algorithm selects nucleic acid positive (DAPI+), cytokeratin-phycoerythrin expressing (CK-PE+) events for further review by an operator. Failures in this segmentation can result in missed CTCs. The CellSearch segmentation algorithm was not designed to handle samples with high cell density, such as diagnostic leukapheresis (DLA) samples. Here, we evaluate deep-learning-based segmentation method StarDist as an alternative to the CellSearch segmentation. CellSearch image archives from 533 whole blood samples and 601 DLA samples were segmented using CellSearch and StarDist and inspected visually. In 442 blood samples from cancer patients, StarDist segmented 99.95% of CTC segmented by CellSearch, produced good outlines for 98.3% of these CTC, and segmented 10% more CTC than CellSearch. Visual inspection of the segmentations of DLA images showed that StarDist continues to perform well when the cell density is very high, whereas CellSearch failed and generated extremely large segmentations (up to 52% of the sample surface). Moreover, in a detailed examination of seven DLA samples, StarDist segmented 20% more CTC than CellSearch. Segmentation is a critical first step for CTC enumeration in dense samples and StarDist segmentation convincingly outperformed CellSearch segmentation.
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