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|a 10.1109/TPAMI.2017.2782687
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|a eng
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|a Ghesu, Florin-Cristian
|e verfasserin
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|a Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans
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|c 2019
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|a Date Revised 08.04.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Robust and fast detection of anatomical structures is a prerequisite for both diagnostic and interventional medical image analysis. Current solutions for anatomy detection are typically based on machine learning techniques that exploit large annotated image databases in order to learn the appearance of the captured anatomy. These solutions are subject to several limitations, including the use of suboptimal feature engineering techniques and most importantly the use of computationally suboptimal search-schemes for anatomy detection. To address these issues, we propose a method that follows a new paradigm by reformulating the detection problem as a behavior learning task for an artificial agent. We couple the modeling of the anatomy appearance and the object search in a unified behavioral framework, using the capabilities of deep reinforcement learning and multi-scale image analysis. In other words, an artificial agent is trained not only to distinguish the target anatomical object from the rest of the body but also how to find the object by learning and following an optimal navigation path to the target object in the imaged volumetric space. We evaluated our approach on 1487 3D-CT volumes from 532 patients, totaling over 500,000 image slices and show that it significantly outperforms state-of-the-art solutions on detecting several anatomical structures with no failed cases from a clinical acceptance perspective, while also achieving a 20-30 percent higher detection accuracy. Most importantly, we improve the detection-speed of the reference methods by 2-3 orders of magnitude, achieving unmatched real-time performance on large 3D-CT scans
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|a Georgescu, Bogdan
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| 700 |
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|a Zheng, Yefeng
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| 700 |
1 |
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|a Grbic, Sasa
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| 700 |
1 |
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|a Maier, Andreas
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| 700 |
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|a Hornegger, Joachim
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|a Comaniciu, Dorin
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
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|g 41(2019), 1 vom: 28. Jan., Seite 176-189
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|u http://dx.doi.org/10.1109/TPAMI.2017.2782687
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