Segmenting overlapping cervical cell in pap smear images - 2016 PROJECT TITLE : Segmenting overlapping cervical cell in pap smear images - 2016 ABSTRACT: Correct segmentation of cervical cells in Pap smear images is a crucial task for automatic identification of pre-cancerous changes in the uterine cervix. One in all the most important segmentation challenges is that the overlapping of cytoplasm, that was less addressed by previous studies. During this paper, we tend to propose a learning-primarily based method to tackle the overlapping issue with strong form priors by segmenting individual cell in Pap smear pictures. Specifically, we 1st outline the matter as a discrete labeling task for multiple cells with a appropriate cost operate. We tend to then use the coarse labeling result to initialize our dynamic multiple-template deformation model for additional boundary refinement on every cell. Multiple-scale deep convolutional networks are adopted to learn the diverse cell appearance features. Additionally, we tend to incorporate high level form info to guide segmentation where the cells boundary is noisy or lost due to touching and overlapping cells. We evaluate the proposed algorithm on 2 completely different datasets, and our comparative experiments demonstrate the promising performance of the proposed method in terms of segmentation accuracy. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Dynamic Multiple-Template Deformation Model Cervical Cancer Overlapping Cells Splitting Multiple-Scale Deep Convolutional Networks Pulmonary fissure detection in ct images using a Derivative of stick filter - 2016 Tumour ROI estimation in ultrasound images via radon barcodes - 2016