Random Walks for Interactive Organ Segmentation in Two and Three Dimensions: Implementation and Validation

By Leo J. Grady, Thomas Schiwietz, S. Aharon, Rüdiger Westermann. 2005

128 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Medical Imaging, 3D Vision & Pose

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

1. Angela Yao
2. Soumajit Majumder
3. Anders Eklund
4. Sébastien Ourselin
5. Qi Yu
6. Jan Deprest
7. Pengcheng Shi
8. Daniel Forsberg

In Computer Vision and Pattern Recognition

1. Sébastien Ourselin
2. Guotai Wang
3. Tom Kamiel Magda Vercauteren
4. Wenqi Li
5. Angela Yao
6. Soumajit Majumder
7. Jan Deprest
8. Michael Aertsen

In Medical Imaging

1. Angela Yao
2. Soumajit Majumder
3. Sébastien Ourselin
4. Tom Kamiel Magda Vercauteren

In 3D Vision & Pose

1. Angela Yao
2. Soumajit Majumder

Citations in the corpus

• Iterative Interaction Training for Segmentation Editing Networks (2018, neutral): “To address the need for editing, interactive segmentation algorithms have been proposed such as GrabCut, GeoS or Random Walker [14,3,5] that allow operators to modify segmentations.”
• A Continual Learning Framework for Uncertainty-Aware Interactive Image Segmentation (2021, background): “2016), scribbles (Grady et al. 2005), bounding boxes (Rajchl et al.”
• Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning (2017, methodology): “5) Comparison With Other Interactive Methods: The two users (an Obstetrician and a Radiologist) used Slic-Seg [21], GrabCut [20], Random Walks [18] and BIFSeg for the fetal MRI segmentation tasks respectively.”
• Content-Aware Multi-Level Guidance for Interactive Instance Segmentation (2019, methodology): “Other methods include Graph Cuts [7], Random Walk [18] and GeoS [13] though more recent methods [32, 35, 36, 52, 53] approach the problem with deep learning architectures such as convolutional neural networks (CNNs).”
• Random Walks for Image Segmentation (2006, background): “We note that a systematic study of the sensitivity of the segmentation to the seed locations/quantities was undertaken in a recent conference paper [64].”
• Medical image processing on the GPU - Past, present and future (2013, methodology): “(2005) proposed interactive volume segmentation by region growing and applied it to MRI volumes, while Grady et al. (2005) instead used an algorithm based on”