Learning Shape-Driven Segmentation Based on Neural Network and Sparse Reconstruction Toward Automated Cell Analysis of Cervical Smears

The development of an automatic and accurate segmentation approach for both nuclei and cytoplasm remains an open problem due to the complexities of cell structures resulting from inconsistent staining, poor contrast, and the presence of mucus, blood, inflammatory cells, and highly overlapping cells. This paper introduces a computer vision slide analysis technique of two stages: the 3-class cellular component classification, and individual cytoplasm segmentation. Feed forward neural network along with discriminative shape and texture features is applied to classify the cervical cell images in the cellular components. Then, a learned shape prior incorporated with variational framework is applied for accurate localization and delineation of overlapping cells. The shape prior is dynamically modelled during the segmentation process as a weighted linear combination of shape templates from an over-complete shape repository. The proposed approach is evaluated and compared to the state-of-the-art methods on a dataset of synthetically generated overlapping cervical cell images, with competitive results in both nuclear and cytoplasmic segmentation accuracy.