Chapter title |
Automated Nucleus and Cytoplasm Segmentation of Overlapping Cervical Cells
|
---|---|
Chapter number | 57 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013
|
Published in |
Lecture notes in computer science, January 2013
|
DOI | 10.1007/978-3-642-40811-3_57 |
Pubmed ID | |
Book ISBNs |
978-3-64-240810-6, 978-3-64-240811-3
|
Authors |
Lu, Zhi, Carneiro, Gustavo, Bradley, Andrew P., Zhi Lu, Gustavo Carneiro, Andrew P. Bradley |
Editors |
Mori, Kensaku, Navab, Nassir, Barillot, Christian, Sato, Yoshinobu, Sakuma, Ichiro |
Abstract |
In this paper we describe an algorithm for accurately segmenting the individual cytoplasm and nuclei from a clump of overlapping cervical cells. Current methods cannot undertake such a complete segmentation due to the challenges involved in delineating cells with severe overlap and poor contrast. Our approach initially performs a scene segmentation to highlight both free-lying cells, cell clumps and their nuclei. Then cell segmentation is performed using a joint level set optimization on all detected nuclei and cytoplasm pairs. This optimisation is constrained by the length and area of each cell, a prior on cell shape, the amount of cell overlap and the expected gray values within the overlapping regions. We present quantitative nuclei detection and cell segmentation results on a database of synthetically overlapped cell images constructed from real images of free-lying cervical cells. We also perform a qualitative assessment of complete fields of view containing multiple cells and cell clumps. |
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