TY - GEN
T1 - Vertebral Labelling in Radiographs
T2 - 6th International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019
AU - Bayat, Amirhossein
AU - Sekuboyina, Anjany
AU - Hofmann, Felix
AU - Husseini, Malek El
AU - Kirschke, Jan S.
AU - Menze, Bjoern H.
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Localizing and labeling vertebrae in spinal radiographs has important applications in spinal shape analysis in scoliosis and degenerative disorders. However, due to tissue overlaying and size of spinal radiographs, vertebrae localization and labeling are challenging and complicated. To address this, we propose a robust approach for landmark detection in large and noisy images and apply it on spinal radiographs. In this approach, the model has a holistic view of the input image irrespective to its size. Our model predicts the labels and locations of vertebrae in two steps: Firstly, a fully convolutional network (FCN) is used to estimate the vertebrae location and label, by predicting 2D Gaussians. Then, we introduce the Residual Corrector (RC) component, that extracts the coordinates of each vertebral centroid from the 2D Gaussians, and correct the location and label estimations by taking into account the entire image. The functionality of the RC component is differentiable. Thus, it can be merged to the deep neural network, and trained end-to-end with other sub-networks. We achieve identification rates of 85.32% and 52.28% for sagittal and coronal views and localization distance of 4.57 mm and 5.33 mm in sagittal and coronal views radiographs, respectively.
AB - Localizing and labeling vertebrae in spinal radiographs has important applications in spinal shape analysis in scoliosis and degenerative disorders. However, due to tissue overlaying and size of spinal radiographs, vertebrae localization and labeling are challenging and complicated. To address this, we propose a robust approach for landmark detection in large and noisy images and apply it on spinal radiographs. In this approach, the model has a holistic view of the input image irrespective to its size. Our model predicts the labels and locations of vertebrae in two steps: Firstly, a fully convolutional network (FCN) is used to estimate the vertebrae location and label, by predicting 2D Gaussians. Then, we introduce the Residual Corrector (RC) component, that extracts the coordinates of each vertebral centroid from the 2D Gaussians, and correct the location and label estimations by taking into account the entire image. The functionality of the RC component is differentiable. Thus, it can be merged to the deep neural network, and trained end-to-end with other sub-networks. We achieve identification rates of 85.32% and 52.28% for sagittal and coronal views and localization distance of 4.57 mm and 5.33 mm in sagittal and coronal views radiographs, respectively.
UR - http://www.scopus.com/inward/record.url?scp=85080921714&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-39752-4_4
DO - 10.1007/978-3-030-39752-4_4
M3 - Conference contribution
AN - SCOPUS:85080921714
SN - 9783030397517
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 39
EP - 46
BT - Computational Methods and Clinical Applications for Spine Imaging - 6th International Workshop and Challenge, CSI 2019, Proceedings
A2 - Cai, Yunliang
A2 - Wang, Liansheng
A2 - Audette, Michel
A2 - Zheng, Guoyan
A2 - Li, Shuo
PB - Springer
Y2 - 17 October 2019 through 17 October 2019
ER -