论文信息 - A Localisation-Segmentation Approach for Multi-label Annotation of Lumbar Vertebrae using Deep Nets

A Localisation-Segmentation Approach for Multi-label Annotation of Lumbar Vertebrae using Deep Nets

Multi-class segmentation of vertebrae is a non-trivial task mainly due to the high correlation in the appearance of adjacent vertebrae. Hence, such a task calls for the consideration of both global and local context. Based on this motivation, we propose a two-staged approach that, given a computed tomography dataset of the spine, segments the five lumbar vertebrae and simultaneously labels them. The first stage employs a multi-layered perceptron performing non-linear regression for locating the lumbar region using the global context. The second stage, comprised of a fully-convolutional deep network, exploits the local context in the localised lumbar region to segment and label the lumbar vertebrae in one go. Aided with practical data augmentation for training, our approach is highly generalisable, capable of successfully segmenting both healthy and abnormal vertebrae (fractured and scoliotic spines). We consistently achieve an average Dice coefficient of over 90 percent on a publicly available dataset of the xVertSeg segmentation challenge of MICCAI 2016. This is particularly noteworthy because the xVertSeg dataset is beset with severe deformities in the form of vertebral fractures and scoliosis.

[1] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

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

[3] Hao Chen,et al. Automatic Localization and Identification of Vertebrae in Spine CT via a Joint Learning Model with Deep Neural Networks , 2015, MICCAI.

[4] Daniel Forsberg. Atlas-Based Segmentation of the Thoracic and Lumbar Vertebrae , 2015 .

[5] Bulat Ibragimov,et al. Interpolation-Based Shape-Constrained Deformable Model Approach for Segmentation of Vertebrae from CT Spine Images , 2015 .

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

[7] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[8] Purang Abolmaesumi,et al. Deep learning for automatic localization, identification, and segmentation of vertebral bodies in volumetric MR images , 2015, Medical Imaging.

[9] H. Labelle,et al. Spine Segmentation in Medical Images Using Manifold Embeddings and Higher-Order MRFs , 2013, IEEE Transactions on Medical Imaging.

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

[11] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.