TY - GEN
T1 - LINKS
T2 - International Workshop on Medical Computer Vision: Algorithms for Big Data was held in conjunction with 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI-bigMCV 2014
AU - Wang, Li
AU - Gao, Yaozong
AU - Shi, Feng
AU - Li, Gang
AU - Gilmore, John H.
AU - Lin, Weili
AU - Shen, Dinggang
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2014.
PY - 2014
Y1 - 2014
N2 - Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and the ongoing maturation and myelination processes. In particular, the image contrast inverts around 6-8 months of age, and the white and gray matter tissues are isointense in both T1- and T2-weighted MR images and thus exhibit the extremely low tissue contrast, which poses the significant challenges for automated segmentation. Most previous studies used multi-atlas label fusion strategy, which has the limitation of equally treating the available multi-modality images and is often computationally expensive. In this paper, we propose a novel learning-based multi-source integration framework for infant brain image segmentation. Specifically, we employ the random forest technique to effectively integrate features from multi-source images together for tissue segmentation. The multi-source images include initially only the multi-modality (T1, T2 and FA) images and later also the iteratively estimated and refined tissue probability maps of gray matter, white matter, and cerebrospinal fluid. Experimental results on 119 infant subjects and MICCAI challenges show that the proposed method achieves better performance than other state-of-the-art automated segmentation methods, with significantly reduction of running time from hours to 5 minutes.
AB - Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and the ongoing maturation and myelination processes. In particular, the image contrast inverts around 6-8 months of age, and the white and gray matter tissues are isointense in both T1- and T2-weighted MR images and thus exhibit the extremely low tissue contrast, which poses the significant challenges for automated segmentation. Most previous studies used multi-atlas label fusion strategy, which has the limitation of equally treating the available multi-modality images and is often computationally expensive. In this paper, we propose a novel learning-based multi-source integration framework for infant brain image segmentation. Specifically, we employ the random forest technique to effectively integrate features from multi-source images together for tissue segmentation. The multi-source images include initially only the multi-modality (T1, T2 and FA) images and later also the iteratively estimated and refined tissue probability maps of gray matter, white matter, and cerebrospinal fluid. Experimental results on 119 infant subjects and MICCAI challenges show that the proposed method achieves better performance than other state-of-the-art automated segmentation methods, with significantly reduction of running time from hours to 5 minutes.
UR - http://www.scopus.com/inward/record.url?scp=84917682088&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-13972-2_3
DO - 10.1007/978-3-319-13972-2_3
M3 - Conference contribution
AN - SCOPUS:84917682088
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 22
EP - 33
BT - Medical Computer Vision
A2 - Müller, Henning
A2 - Menze, Bjoern
A2 - Zhang, Shaoting
A2 - Cai, Weidong (Tom)
A2 - Menze, Bjoern
A2 - Langs, Georg
A2 - Metaxas, Dimitris
A2 - Langs, Georg
A2 - Müller, Henning
A2 - Kelm, Michael
A2 - Montillo, Albert
A2 - Cai, Weidong (Tom)
PB - Springer Verlag
Y2 - 18 September 2014 through 18 September 2014
ER -