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Accurate Cervical Cell Segmentation from Overlapping Clumps in Pap Smear Images
- Source :
- IEEE Transactions on Medical Imaging. 36:288-300
- Publication Year :
- 2017
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2017.
-
Abstract
- Accurate segmentation of cervical cells in Pap smear images is an important step in automatic pre-cancer identification in the uterine cervix. One of the major segmentation challenges is overlapping of cytoplasm, which has not been well-addressed in previous studies. To tackle the overlapping issue, this paper proposes a learning-based method with robust shape priors to segment individual cell in Pap smear images to support automatic monitoring of changes in cells, which is a vital prerequisite of early detection of cervical cancer. We define this splitting problem as a discrete labeling task for multiple cells with a suitable cost function. The labeling results are then fed into our dynamic multi-template deformation model for further boundary refinement. Multi-scale deep convolutional networks are adopted to learn the diverse cell appearance features. We also incorporated high-level shape information to guide segmentation where cell boundary might be weak or lost due to cell overlapping. An evaluation carried out using two different datasets demonstrates the superiority of our proposed method over the state-of-the-art methods in terms of segmentation accuracy.
- Subjects :
- ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Uterine Cervical Neoplasms
Boundary (topology)
Scale-space segmentation
02 engineering and technology
Cervical cell
Accurate segmentation
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
0202 electrical engineering, electronic engineering, information engineering
Humans
Segmentation
Computer vision
Electrical and Electronic Engineering
Mathematics
Radiological and Ultrasound Technology
Segmentation-based object categorization
business.industry
Image segmentation
Computer Science Applications
Identification (information)
Female
020201 artificial intelligence & image processing
Artificial intelligence
business
Algorithms
Software
Papanicolaou Test
Subjects
Details
- ISSN :
- 1558254X and 02780062
- Volume :
- 36
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Medical Imaging
- Accession number :
- edsair.doi.dedup.....8065aa83e71bd38490fa0ea49a35d9fe