Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part IV

The extraction of spines from medical records in a fast yet accurate way is a challenging task, especially for large data sets. Addressing this issue, we present a framework based on convolutional neural networks for the reconstruction of the spinal shape and curvature, making statistical assessments feasible on epidemiological scale. Our method uses a two-step strategy. First, anchor vertebrae and the spinal centerline in between them get extracted. Second, the centerlines are transformed into a common coordinate system to enable comparisons and statistical assessments across subjects. Our networks were trained on 103 subjects, where we achieved accuracies of 3.3 mm on average, taking at most 1 s per record, which eases the handling of even very large cohorts. Without any further training, we validated our model on study data of about 3400 subjects with only 10 cases of failure, which demonstrates the robustness of our method with respect to the natural variability in spinal shape and curvature. A thorough statistical analysis of the results underpins the importance of our work. Specifically, we show that the spinal curvature is significantly influenced by the body mass index of a subject. Moreover, we show that the same findings arise when Cobb angles are considered instead of direct curvature measures. To this end, we propose a generalization of classical Cobb angles that can be evaluated algorithmically and can also serve as a useful (visual) tool for physicians in everyday clinical practice.

[1]  Shimon Whiteson,et al.  Learning to Communicate with Deep Multi-Agent Reinforcement Learning , 2016, NIPS.

[2]  Shimon Whiteson,et al.  QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning , 2018, ICML.

[3]  Isabelle Bloch,et al.  Multi-organ localization with cascaded global-to-local regression and shape prior , 2015, Medical Image Anal..

[4]  Jiebo Luo,et al.  Image Captioning with Semantic Attention , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Steen Moeller,et al.  The Human Connectome Project: A data acquisition perspective , 2012, NeuroImage.

[6]  Shimon Whiteson,et al.  Learning with Opponent-Learning Awareness , 2017, AAMAS.

[7]  Dorin Comaniciu,et al.  An Artificial Agent for Anatomical Landmark Detection in Medical Images , 2016, MICCAI.

[8]  Roland Vollgraf,et al.  Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms , 2017, ArXiv.

[9]  Dorin Comaniciu,et al.  Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Daniel Rueckert,et al.  Population-based studies of myocardial hypertrophy: high resolution cardiovascular magnetic resonance atlases improve statistical power , 2014, Journal of Cardiovascular Magnetic Resonance.

[11]  Loïc Le Folgoc,et al.  Evaluating reinforcement learning agents for anatomical landmark detection , 2019, Medical Image Anal..

[12]  J. Alison Noble,et al.  Image Analysis Using Machine Learning: Anatomical Landmarks Detection in Fetal Ultrasound Images , 2012, 2012 IEEE 36th Annual Computer Software and Applications Conference.

[13]  Nick C Fox,et al.  The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods , 2008, Journal of magnetic resonance imaging : JMRI.

[14]  Loïc Le Folgoc,et al.  Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents , 2018, MICCAI.

[15]  Mykel J. Kochenderfer,et al.  Cooperative Multi-agent Control Using Deep Reinforcement Learning , 2017, AAMAS Workshops.

[16]  D. Rueckert,et al.  Stratified Decision Forests for Accurate Anatomical Landmark Localization in Cardiac Images. , 2017, IEEE transactions on medical imaging.

[17]  Bishesh Khanal,et al.  Fast Multiple Landmark Localisation Using a Patch-based Iterative Network , 2018, MICCAI.

[18]  Mohammad Reza Emami,et al.  Concurrent Markov decision processes for robot team learning , 2015, Eng. Appl. Artif. Intell..

[19]  Yann LeCun,et al.  Signature Verification Using A "Siamese" Time Delay Neural Network , 1993, Int. J. Pattern Recognit. Artif. Intell..

[20]  Michael I. Jordan,et al.  Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems , 1994, NIPS.

[21]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.