Active spline model: A shape based model - interactive segmentation

@article{Tan2014ActiveSM,
  title={Active spline model: A shape based model - interactive segmentation},
  author={Jen Hong Tan and U. Rajendra Acharya},
  journal={ArXiv},
  year={2014},
  volume={abs/1402.6387}
}
Shape-aware Surface Reconstruction from Sparse Data
TLDR
This work proposes to use a statistical shape model (SSM) as a prior for surface reconstruction from a probabilistic perspective based on a Gaussian Mixture Model (GMM) and suggests that this approach leads to superior surface reconstructions compared to Iterative Closest Point methods.
Precise Segmentation of COVID-19 Infected Lung from CT Images Based on Adaptive First-Order Appearance Model with Morphological/Anatomical Constraints
TLDR
The capability of the proposed approach to accurately segment healthy lung tissues in addition to pathological lung tissues caused by COVID-19, outperforming four current, state-of-the-art deep learning-based lung segmentation approaches is shown.
Automated extraction of retinal vasculature.
TLDR
By expressing retinal vasculature as a series of connected points, the proposed algorithm not only provides a means to edit segmentation but also gives knowledge of the shape of the blood vessels and their connections.
Fast Interactive Object Annotation With Curve-GCN
TLDR
This work proposes a new framework that alleviates the sequential nature of Polygon-RNN, by predicting all vertices simultaneously using a Graph Convolutional Network (GCN), and shows that Curve-GCN outperforms all existing approaches in automatic mode, and is significantly more efficient in interactive mode than Polygon.
Automatic Annotation and Segmentation of Object Instances With Deep Active Curve Network
TLDR
The Deep Active Curve Network (DACN) is proposed which combines powerful ResNet models with GCN-based active curves with the improved 5-interpolation Catmull-Rom spline algorithm which exploits key points to control the active curves around objects.
CNN Encoder Boundary Prediction Feature Extraction initialization GCN GCN Feature Extraction image prediction
TLDR
This work proposes a new framework that alleviates the sequential nature of PolygonRNN, by predicting all vertices simultaneously using a Graph Convolutional Network (GCN), which supports object annotation by either polygons or splines, facilitating labeling efficiency for both line-based and curved objects.
Robust segmentation of lung in chest x-ray: applications in analysis of acute respiratory distress syndrome
TLDR
The proposed Total Variation-based Active Contour lung segmentation algorithm demonstrates the most consistent performance of all segmentation methods tested and suggests that TVAC can accurately identify lung fields in chest radiographs in critically ill adults and children.
High Fidelity Interactive Video Segmentation Using Tensor Decomposition, Boundary Loss, Convolutional Tessellations, and Context-Aware Skip Connections
We provide a high fidelity deep learning algorithm (HyperSeg) for interactive video segmentation tasks using a convolutional network with context-aware skip connections, and compressed, ”hypercolumn”
...
...

References

SHOWING 1-10 OF 36 REFERENCES
Interactive shape models
TLDR
It is shown how to extend one of these segmentation methods, active shape models (ASM) so that user interaction can be incorporated, and believes that iASM can be used in many clinical applications.
Active shape model segmentation with optimal features
TLDR
An active shape model segmentation scheme is presented that is steered by optimal local features, contrary to normalized first order derivative profiles, as in the original formulation, using a nonlinear kNN-classifier to find optimal displacements for landmarks.
Oriented Active Shape Models
TLDR
A novel strategy called oriented active shape models (OASM) is presented in an attempt to overcome the following five limitations of ASM: lower delineation accuracy, the requirement of a large number of landmarks, sensitivity to search range, 4) sensitivity to initialization, and 5) inability to fully exploit the specific information present in the given image to be segmented.
Active Shape Models-Their Training and Application
TLDR
This work describes a method for building models by learning patterns of variability from a training set of correctly annotated images that can be used for image search in an iterative refinement algorithm analogous to that employed by Active Contour Models (Snakes).
B-spline snakes: a flexible tool for parametric contour detection
TLDR
A novel formulation for B-spline snakes is presented that can be used as a tool for fast and intuitive contour outlining, and the intrinsic scale of the spline model is adjusted a priori, leading to a reduction of the number of parameters to be optimized and eliminates the need for internal energies.
A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape
TLDR
A survey of a specific class of region-based level set segmentation methods and how they can all be derived from a common statistical framework is presented.
User-Steered Image Segmentation Paradigms: Live Wire and Live Lane
TLDR
Two paradigms for practical image segmentation in large applications are presented, referred to as live wire and live lane, and formal evaluation studies are described to compare the utility of the new methods with that of manual tracing based on speed and repeatability of tracing and on data taken from a large ongoing application.
Interaction in the segmentation of medical images: A survey
...
...