Segmentation and Shape Tracking of Whole Fluorescent Cells Based on the Chan–Vese Model
By Martin Maška, Ondrej Danek, Saray Garasa, Ana Rouzaut, Arrate Muñoz-Barrutia, Carlos Ortiz-de-Solorzano. 2013
100 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Lin F. Yang
- Fuyong Xing
- Rengül Çetin-Atalay
- Carlos Ortiz-de-Solorzano
- Arrate Muñoz-Barrutia
- Wentong Li
- Jianke Zhu
- Erik H. W. Meijering
In Computer Vision and Pattern Recognition
- Lin F. Yang
- Fuyong Xing
- Erik H. W. Meijering
- Carlos Ortiz-de-Solorzano
- Arrate Muñoz-Barrutia
- Wentong Li
- Jianke Zhu
- David Svoboda
Citations in the corpus
- A benchmark for comparison of cell tracking algorithms (2014, methodology): “…either parametrically (Dufour et al. , 2011; Zimmer et al. , 2002) or implicitly (Dufour et al. , 2005; Dzyubachyk et al. , 2010; Li et al. , 2008; Masˇka et al. , 2013), using a velocity term defined by the content of the ‘‘target’’ frame (e.g. gradient features or intra-and inter-region…”
- Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, background): “though graph cut-based active contour models [188], [217] are presented, the two major implementations of deformable models for nucleus or cell segmentation are geodesic and parametric snakes, which are with implicit and explicit contour representations, respectively.”
- Box-supervised Instance Segmentation with Level Set Evolution (2022, background): “Chan and Vese [7] later simplified the Mumford-Shah functional as a variational level set, which has been explored aplenty [31,34,46,52].”
- Object‐Oriented Segmentation of Cell Nuclei in Fluorescence Microscopy Images (2018, background): “These algorithms de fi ne their energy functions on the gradients and converge the fi nal boundaries by minimizing these energy functions (17,18).”
- An Objective Comparison of Cell Tracking Algorithms (2017, unknown): “Cell tracking methods can be broadly categorized into two groups: (i) Tracking by contour evolution methods 21,22,24,25 start by segmenting the cells in the first frame of a video and evolve their contours in consecutive frames, thus solving the segmentation and tracking tasks simultaneously, one…”