MRF-Based Deformable Registration and Ventilation Estimation of Lung CT

By Mattias Paul Heinrich, Mark Jenkinson, M. Brady, Julia Anne Schnabel. IEEE Transactions on Medical Imaging, 2013

320 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/4f41001d42cf2e27a25da9ebe105e62825bd8207

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Adrian V. Dalca
2. John V. Guttag
3. Guha Balakrishnan
4. Pingkun Yan
5. Uwe Kruger
6. Grant Haskins
7. Mert Rory Sabuncu
8. Yingda Xia

In Radiology, Nuclear Medicine and Imaging

1. Adrian V. Dalca
2. John V. Guttag
3. Guha Balakrishnan
4. Mert Rory Sabuncu
5. Pingkun Yan
6. Uwe Kruger
7. Grant Haskins

Citations in the corpus

• Fully Convolutional Neural Networks Improve Abdominal Organ Segmentation (2018, unknown): “The linear transformation tool we used was Dense Displacement Sampling (deeds)’s linearBCV [15-18].”
• Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces (2019, background): “Common representations are displacement vector fields, including elastic-type models [8, 21, 62], free-form deformations with b-splines [61], statistical parametric mapping [6], Demons [56, 66], and more recently discrete methods [19, 30, 28].”
• Deep learning in medical image registration: a survey (2019, methodology): “ware, which is often 16 Table 8 Comparison of Target Registration Error (TRE) values among different methods on DIRLAB datasets, TRE unit: (mm), *: Traditional DIR methods Set Initial Heinrich* et al. [62] Delmon* et al. [24] Staring* et al. [141] Eppenhof et al. [35] De Vos et al. [23] Sentker et al”
• Automatic Multi-organ Segmentation on Abdominal CT with Dense V-networks (2018, unknown): “DEEDS and JLF computations were performed using the publicly available deedsRegSSC (http://www.mpheinrich.de/software.html) and PICSL Multi-Atlas Segmentation Tool (https:// www.nitrc.org/projects/picsl_malf) implementations, respectively, using published abdominal CT registration parameters for DEE”
• Large-scale pancreatic cancer detection via non-contrast CT and deep learning (2023, methodology): “First, we set the arterial phase as the target phase and registered all the other phases (noncontrast and venous) to the arterial phase using DEEDS [2] image registration algorithm.”
• TransMorph: Transformer for unsupervised medical image registration (2021, unknown): “Traditional methods formulate image registration as a variational problem for estimating a smooth mapping between the points in one image and those in another (Avants et al., 2008; Beg et al., 2005; Vercauteren et al., 2009; Heinrich et al., 2013a; Modat et al., 2010).”