Building Large-Scale Quantitative Imaging Databases with Multi-Scale Deep Reinforcement Learning: Initial Experience with Whole-Body Organ Volumetric Analyses

Winkel, David; Breit, Hanns‐Christian; Weikert, Thomas; Stieltjes, Bram

doi:10.1007/s10278-020-00398-y

Cited by 5 publications

(4 citation statements)

References 22 publications

(30 reference statements)

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“…Because the absolute quadratic curve is a planar quadratic curve on the infinity plane, as long as the relevant information is found, the position information of the infinity plane can be determined. According to the theory of hierarchical reconstruction, the first is to calculate the projective reconstruction, upgrade it to affine reconstruction, and then use the result of affine reconstruction to upgrade to metric reconstruction [14]. As the result of calculating the affine reconstruction already includes the relevant information of the infinity plane, the absolute conic can be obtained directly.…”

Section: Reconstruction Spacementioning

confidence: 99%

Algorithm of 3D Virtual Reconstruction of Ancient Buildings in Qing Dynasty Based on Image Sequence

2021

Security and Communication Networks

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Aiming at the problem of low accuracy and poor integrity of traditional Qing Dynasty ancient architecture 3D virtual reconstruction algorithm, a 3D virtual reconstruction algorithm of Qing Dynasty ancient architecture based on image sequence is proposed. Acquire the sequence images of ancient buildings in the Qing Dynasty through the pinhole camera model, analyze the projective space and reconstruction space of the sequence images, redefine the similarity measurement coefficient according to the improved 2DPCA-SIFT feature matching algorithm, match the feature points of the ancient architecture images in the Qing Dynasty, and use random sampling to be consistent. The algorithm solves the basic matrix, removes the interference error in the image reconstruction process, and realizes the design of the three-dimensional reconstruction algorithm through image sequence fusion. The experimental results show that, compared with the existing methods, the completeness of the three-dimensional virtual reconstruction 3D model of ancient Qing Dynasty buildings constructed by the designed algorithm is 87.26% on average, and the completeness and accuracy of the 3D model construction of the subparts of the ancient Qing Dynasty buildings of this method are better. The height of the building fully shows that the designed building has good performance in the construction of the three-dimensional model of ancient buildings in the Qing Dynasty.

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Section: Reconstruction Spacementioning

confidence: 99%

Algorithm of 3D Virtual Reconstruction of Ancient Buildings in Qing Dynasty Based on Image Sequence

2021

Security and Communication Networks

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“…The UCB is used to reweight multisite data, and update parameters by maximizing the data log-likelihood of the probability policy with given labels. Winkel et al (2020Winkel et al ( , 2021 although did not specify the adopted RL algorithm, proposed a robust and fast DRL agent to distinguish the target anatomical object, and find an optimal navigation path to the target object in the imaged volumetric space. Pradella et al (2021) adopted a DRL approach in Ghesu et al (2017) to detect 6 aortic landmarks along the thoracic aorta.…”

Section: Rl/drl-supported Segmentationmentioning

confidence: 99%

Deep reinforcement learning and its applications in medical imaging and radiation therapy: a survey

Xu¹,

Zhu²

2022

Phys. Med. Biol.

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Reinforcement learning takes sequential decision-making approaches by learning the policy through trial and error based on interaction with the environment. Combining deep learning and reinforcement learning can empower the agent to learn the interactions and the distribution of rewards from state-action pairs to achieve effective and efficient solutions in more complex and dynamic environments. Deep reinforcement learning (DRL) has demonstrated astonishing performance in surpassing the human-level performance in the game domain and many other simulated environments. This paper introduces the basics of reinforcement learning and reviews various categories of DRL algorithms and DRL models developed for medical image analysis and radiation treatment planning optimization. We will also discuss the current challenges of DRL and approaches proposed to make DRL more generalizable and robust in a real-world environment. DRL algorithms, by fostering the designs of reward function, agents interactions and environment models, can resolve the challenges from scarce and heterogeneous annotated medical image data, which has been a major obstacle to implementing deep learning models in the clinic. DRL is an active research area with enormous potential to improve deep learning applications in medical imaging and radiation therapy planning.

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“…It can use end-to-end reinforcement learning to learn successful strategies directly from highdimensional sensory input. Literature [20] proposed the use of multiscale deep reinforcement learning to establish a large-scale quantitative image database and integrate the preliminary experience of human behavior analysis. In addition, the paper also explores the feasibility of a full-body volume analysis full-automatic workflow based on deep reinforcement learning as well as the influence of contrast and slice thickness on the calculation of organ volume.…”

Section: Related Workmentioning

confidence: 99%

Multifeature Fusion Human Motion Behavior Recognition Algorithm Using Deep Reinforcement Learning

2021

Mobile Information Systems

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Through the recognition and analysis of human motion information, the actual motion state of human body can be obtained. However, the multifeature fusion of human behavior has limitations in recognition accuracy and robustness. Combined with deep reinforcement learning, multifeature fusion human behavior recognition is studied and we proposed a multifeature fusion human behavior recognition algorithm using deep reinforcement learning. Firstly, several typical human behavior data sets are selected as the research data in the benchmark data set. In the selected data sets, the behavior category contained in each video is the same behavior, and there are category tags. Secondly, the attention model is constructed. In the deep reinforcement learning network, the small sampling area is used as the model input. Finally, the corresponding position of the next visual area is estimated according to the time series information obtained after the input. The human behavior recognition algorithm based on deep reinforcement learning multifeature fusion is completed. The results show that the average accuracy of multifeature fusion of the algorithm is about 95%, the human behavior recognition effect is good, the identification accuracy rate is as high as about 98% and passed the camera movement impact performance test and the algorithm robustness, and the average time consumption of the algorithm is only 12.7 s, which shows that the algorithm has very broad application prospects.

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Building Large-Scale Quantitative Imaging Databases with Multi-Scale Deep Reinforcement Learning: Initial Experience with Whole-Body Organ Volumetric Analyses

Cited by 5 publications

References 22 publications

Algorithm of 3D Virtual Reconstruction of Ancient Buildings in Qing Dynasty Based on Image Sequence

Algorithm of 3D Virtual Reconstruction of Ancient Buildings in Qing Dynasty Based on Image Sequence

Deep reinforcement learning and its applications in medical imaging and radiation therapy: a survey

Multifeature Fusion Human Motion Behavior Recognition Algorithm Using Deep Reinforcement Learning

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