Fast Automatic Vertebrae Detection and Localization in Pathological CT Scans - A Deep Learning Approach

Automatic detection and localization of vertebrae in medical images are highly sought after techniques for computer-aided diagnosis systems of the spine. However, the presence of spine pathologies and surgical implants, and limited field-of-view of the spine anatomy in these images, make the development of these techniques challenging. This paper presents an automatic method for detection and localization of vertebrae in volumetric computed tomography (CT) scans. The method makes no assumptions about which section of the vertebral column is visible in the image. An efficient approach based on deep feed-forward neural networks is used to predict the location of each vertebra using its contextual information in the image. The method is evaluated on a public data set of 224 arbitrary-field-of-view CT scans of pathological cases and compared to two state-of-the-art methods. Our method can perform vertebrae detection at a rate of 96% with an overall run time of less than 3 seconds. Its fast and comparably accurate detection makes it appealing for clinical diagnosis and therapy applications.

[1]  Ben Glocker,et al.  Automatic Localization and Identification of Vertebrae in Arbitrary Field-of-View CT Scans , 2012, MICCAI.

[2]  Antonio Criminisi,et al.  Decision Forests with Long-Range Spatial Context for Organ Localization in CT Volumes , 2009 .

[3]  Antonio Criminisi,et al.  Anatomy Detection and Localization in 3D Medical Images , 2013 .

[4]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Purang Abolmaesumi,et al.  Automatic labeling and segmentation of vertebrae in CT images , 2014, Medical Imaging.

[6]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[7]  Zhigang Peng,et al.  Automated Vertebra Detection and Segmentation from the Whole Spine MR Images , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[8]  Dumitru Erhan,et al.  Deep Neural Networks for Object Detection , 2013, NIPS.

[9]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[10]  Dirk P. Kroese,et al.  Kernel density estimation via diffusion , 2010, 1011.2602.

[11]  Ben Glocker,et al.  Vertebrae Localization in Pathological Spine CT via Dense Classification from Sparse Annotations , 2013, MICCAI.

[12]  Thomas Hofmann,et al.  Greedy Layer-Wise Training of Deep Networks , 2007 .

[13]  Li Bai,et al.  Introducing Willmore Flow Into Level Set Segmentation of Spinal Vertebrae , 2013, IEEE Transactions on Biomedical Engineering.

[14]  Jason J. Corso,et al.  Labeling of Lumbar Discs Using Both Pixel- and Object-Level Features With a Two-Level Probabilistic Model , 2011, IEEE Transactions on Medical Imaging.

[15]  Dorin Comaniciu,et al.  Spine detection in CT and MR using iterated marginal space learning , 2013, Medical Image Anal..

[16]  Ayse Betül Oktay,et al.  Simultaneous Localization of Lumbar Vertebrae and Intervertebral Discs With SVM-Based MRF , 2013, IEEE Transactions on Biomedical Engineering.

[17]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[18]  Cristian Lorenz,et al.  Automated model-based vertebra detection, identification, and segmentation in CT images , 2009, Medical Image Anal..