High-throughput histopathological image analysis via robust cell segmentation and hashing
Open accessAbstract
Computer-aided diagnosis of histopathological images usually requires to examine all cells for accurate diagnosis. Traditional computational methods may have efficiency issues when performing cell-level analysis. In this paper, we propose a robust and scalable solution to enable such analysis in a real-time fashion. Specifically, a robust segmentation method is developed to delineate cells accurately using Gaussian-based hierarchical voting and repulsive balloon model. A large-scale image retrieval approach is also designed to examine and classify each cell of a testing image by comparing it with a massive database, e.g., half-million cells extracted from the training dataset. We evaluate this proposed framework on a challenging and important clinical use case, i.e., differentiation of two types of lung cancers (the adenocarcinoma and squamous carcinoma), using thousands of lung microscopic tissue images extracted from hundreds of patients. Our method has achieved promising accuracy and running time by searching among half-million cells .
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- Authors
- Zhang, X., Xing, F., Su, H., Yang, L., & Zhang, S.
- Affiliations
- University of North Carolina at Charlotte (US)
- University of Florida (US)
- Corresponding author
- Zhang, Xiaofan
- Zhang, Shaoting
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2015-12
- Volume
- 26
- Issue
- 1
- Pages
- 306-315
- ISSN
- 1361-8415 (ISSN-L)
- PMC
- PMC4679540
- OpenAlex
- W2173930701
- Cited by
- 87
- References
- 90
- Citations by year
- 2025: 1 · 2024: 6 · 2023: 7 · 2022: 5 · 2021: 10 · 2020: 9 · 2019: 12 · 2018: 10 · 2017: 12 · 2016: 14 · 2015: 1
- FWCI
- 12.09
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Medical Image Analysis (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH (R01 AR06547901A1)
- Oak Ridge Associated Universities
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Keywords
- histopathology image analysis, cell segmentation, hashing, large-scale image retrieval, computer-aided diagnosis, lung cancer, adenocarcinoma, squamous cell carcinoma
- MeSH terms
- Adenocarcinoma, Carcinoma, Squamous Cell, Diagnosis, Differential, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Lung Neoplasms, Microscopy, Pattern Recognition, Automated, Sensitivity and Specificity, Reproducibility of Results, High-Throughput Screening Assays, Cell Tracking
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