Masoud Nosrati

TL;DR: The first Overlapping Cervical Cytology Image Segmentation Challenge as discussed by the authors was organized to encourage the development and benchmarking of techniques capable of segmenting individual cells from overlapping cellular clumps in cervical cytology images.

TL;DR: This survey focuses on optimization-based methods that incorporate prior information into their frameworks and reviews and compares these methods in terms of the types of prior employed, the domain of formulation, and the optimization techniques.

TL;DR: Features, basic concepts, algorithm and the approaches of each type of star algorithms is investigated separately in this paper.

TL;DR: A new continuous variational segmentation framework with star-shape prior using directional derivatives to segment overlapping cervical cells in Pap smear images is proposed and it is shown that the star- shape constraint better models the underlying problem and outperforms state-of-the-art methods in terms of accuracy and speed.

TL;DR: This paper proposes a multi-modal approach to segmentation where preoperative 3D computed tomography scans and intraoperative stereo-endoscopic video data are jointly analyzed and estimates and track the pose of the preoperative models in 3D and consider the models' non-rigid deformations to match with corresponding visual cues in multi-channel endoscopic video and segment the objects of interest.