Towards intelligent robust detection of anatomical structures in incomplete volumetric data

By Florin C. Ghesu, Bogdan Georgescu, Sasa Grbic, Andreas K. Maier, Joachim Hornegger, Dorin Comaniciu. 2018

37 citations recorded by the source corpus.

HighlightsMulti‐scale DRL with robust statistical shape modeling for anatomy detection.Multi‐scale processing enables real‐time speed and high detection accuracy.Robust and principled recognition of anatomy that is missing from the field‐of‐view.Extensive experiments on up to 50 anatomical landmarks and over 5000 3D‐CT scans. Graphical abstract Figure. No caption available. ABSTRACT Robust and fast detection of anatomical structures represents an important component of medical image analysis technologies. Current solutions for anatomy detection are based on machine learning, and are generally driven by suboptimal and exhaustive search strategies. In particular, these techniques do not effectively address cases of incomplete data, i.e., scans acquired with a partial field‐of‐view. We address these challenges by following a new paradigm, which reformulates the detection task to teaching an intelligent artificial agent how to actively search for an anatomical structure. Using the principles of deep reinforcement learning with multi‐scale image analysis, artificial agents are taught optimal navigation paths in the scale‐space representation of an image, while accounting for structures that are missing from the field‐of‐view. The spatial coherence of the observed anatomical landmarks is ensured using elements from statistical shape modeling and robust estimation theory. Experiments show that our solution outperforms marginal space deep learning, a powerful deep learning method, at detecting different anatomical structures without any failure. The dataset contains 5043 3D‐CT volumes from over 2000 patients, totaling over 2,500,000 image slices. In particular, our solution achieves 0% false‐positive and 0% false‐negative rates at detecting whether the landmarks are captured in the field‐of‐view of the scan (excluding all border cases), with an average detection accuracy of 2.78 mm. In terms of runtime, we reduce the detection‐time of the marginal space deep learning method by 20–30 times to under 40 ms, an unmatched performance for high resolution incomplete 3D‐CT data.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Oral Surgery

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Citations in the corpus

Reinforcement Learning in Healthcare: A Survey (2019, methodology): “onance Images (MRI), UI and 3D Computed Tomography (CT) images. This approach was later extended to exploit multi-scale image representations for large 3D CT scans [256], and consider incomplete data [257] or nonlinear multi-dimensional parametric space in MRI scans of the brain region [258]. Alansa”
Partial Policy-Based Reinforcement Learning for Anatomical Landmark Localization in 3D Medical Images (2018, background): “Previous RL-based localization approaches focused on implicit policy learning through training a value function approximator [4]–[6], [23].”
Artificial Intelligence in Cardiovascular Imaging (2019, methodology): “One of the most recent methods for robust localization of anatomical landmarks relies on multiscale deep reinforcement learning (41,42).”