TY - GEN
T1 - Patch-based segmentation without registration
T2 - 4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
AU - Wang, Zehan
AU - Donoghue, Claire
AU - Rueckert, Daniel
PY - 2013
Y1 - 2013
N2 - Atlas based segmentation techniques have been proven to be effective in many automatic segmentation applications. However, the reliance on image correspondence means that the segmentation results can be affected by any registration errors which occur, particularly if there is a high degree of anatomical variability. This paper presents a novel multi-resolution patch-based segmentation framework which is able to work on images without requiring registration. Additionally, an image similarity metric using 3D histograms of oriented gradients is proposed to enable atlas selection in this context. We applied the proposed approach to segment MR images of the knee from the MICCAI SKI10 Grand Challenge, where 100 training atlases are provided and evaluation is conducted on 50 unseen test images. The proposed method achieved good scores overall and is comparable to the top entries in the challenge for cartilage segmentation, demonstrating good performance when comparing against state-of-the-art approaches customised to Knee MRI.
AB - Atlas based segmentation techniques have been proven to be effective in many automatic segmentation applications. However, the reliance on image correspondence means that the segmentation results can be affected by any registration errors which occur, particularly if there is a high degree of anatomical variability. This paper presents a novel multi-resolution patch-based segmentation framework which is able to work on images without requiring registration. Additionally, an image similarity metric using 3D histograms of oriented gradients is proposed to enable atlas selection in this context. We applied the proposed approach to segment MR images of the knee from the MICCAI SKI10 Grand Challenge, where 100 training atlases are provided and evaluation is conducted on 50 unseen test images. The proposed method achieved good scores overall and is comparable to the top entries in the challenge for cartilage segmentation, demonstrating good performance when comparing against state-of-the-art approaches customised to Knee MRI.
UR - http://www.scopus.com/inward/record.url?scp=84886733426&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-02267-3_13
DO - 10.1007/978-3-319-02267-3_13
M3 - Conference contribution
AN - SCOPUS:84886733426
SN - 9783319022666
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 98
EP - 105
BT - Machine Learning in Medical Imaging - 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Proceedings
PB - Springer Verlag
Y2 - 22 September 2013 through 22 September 2013
ER -