Regression Forests for Efficient Anatomy Detection and Localization in CT Studies

By Antonio Criminisi, Jamie Shotton, Duncan P. Robertson, Ender Konukoglu. MCV, 2010

326 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/84fe699bea32c11560c8c2d8cf89cb614659a41f

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans (2019, background): “databases [1], [10], [11], [12].”
Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning (2011, methodology): “Forest-based probabilistic regression was introduced in [24] and it will be discussed in detail in chapter 4.”
Efficient regression of general-activity human poses from depth images (2011, background): “A regression forest is an ensemble of decision trees [3] that predicts continuous outputs [6].”
Random Forests (2012, unknown): “3 応用例 Random Forests を回帰問題に適用した例としては,画像中の物体検出 [11], [17]や 3次元CT画像からの臓器の検出 [9]が挙げられる.”
Efficient Human Pose Estimation from Single Depth Images (2013, methodology): “The forest used here is a regression forest [54], [50] since the leaves make continuous predictions.”
Real Time Head Pose Estimation from Consumer Depth Cameras (2011, methodology): “Then, similarly to [9], we select only a percentage of the remaining votes, starting from the ones with smallest uncertainty: if more votes than a threshold are left, we declare a head detected.”
A Survey on Human Motion Analysis from Depth Data (2013, methodology): “Currently the estimation of this joint with [25] is not very reliable, especially when the subject is not facing towards the camera.”
Random Forests for Real Time 3D Face Analysis (2012, methodology): “Recent works showed the power of random forests in mapping image features to votes in a generalized Hough space (Gall and Lempitsky 2009) or, in a regression framework, to real-valued functions (Criminisi et al. 2010).”
Real time head pose estimation with random regression forests (2011, methodology): “Recent works showed the power of random forests in mapping image features to votes in a generalizedHough space [11] or to real-valued functions [10, 12].”
Real-time facial feature detection using conditional regression forests (2012, methodology): “In contrast to a regression objective as used in [8] and [12], it is faster to compute and also performed slightly better in our experiments.”
Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning (2012, background): “Recent years have seen an explosion of forest-based techniques in the machine learning, vision and medical imaging literature [9, 15, 25, 31, 35, 37, 58, 59, 65, 67, 68, 69, 74, 79, 89, 92, 97, 110].”