TY - GEN
T1 - Segmentation of craniomaxillofacial bony structures from MRI with a 3D deep-learning based cascade framework
AU - Nie, Dong
AU - Wang, Li
AU - Trullo, Roger
AU - Li, Jianfu
AU - Yuan, Peng
AU - Xia, James
AU - Shen, Dinggang
N1 - Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - Computed tomography (CT) is commonly used as a diagnostic and treatment planning imaging modality in craniomaxillofacial (CMF) surgery to correct patient’s bony defects. A major disadvantage of CT is that it emits harmful ionizing radiation to patients during the exam. Magnetic resonance imaging (MRI) is considered to be much safer and noninvasive, and often used to study CMF soft tissues (e.g., temporomandibular joint and brain). However, it is extremely difficult to accurately segment CMF bony structures from MRI since both bone and air appear to be black in MRI, along with low signal-to-noise ratio and partial volume effect. To this end, we proposed a 3D deep-learning based cascade framework to solve these issues. Specifically, a 3D fully convolutional network (FCN) architecture is first adopted to coarsely segment the bony structures. As the coarsely segmented bony structures by FCN tend to be thicker, convolutional neural network (CNN) is further utilized for fine-grained segmentation. To enhance the discriminative ability of the CNN, we particularly concatenate the predicted probability maps from FCN and the original MRI, and feed them together into the CNN to provide more context information for segmentation. Experimental results demonstrate a good performance and also the clinical feasibility of our proposed 3D deep-learning based cascade framework.
AB - Computed tomography (CT) is commonly used as a diagnostic and treatment planning imaging modality in craniomaxillofacial (CMF) surgery to correct patient’s bony defects. A major disadvantage of CT is that it emits harmful ionizing radiation to patients during the exam. Magnetic resonance imaging (MRI) is considered to be much safer and noninvasive, and often used to study CMF soft tissues (e.g., temporomandibular joint and brain). However, it is extremely difficult to accurately segment CMF bony structures from MRI since both bone and air appear to be black in MRI, along with low signal-to-noise ratio and partial volume effect. To this end, we proposed a 3D deep-learning based cascade framework to solve these issues. Specifically, a 3D fully convolutional network (FCN) architecture is first adopted to coarsely segment the bony structures. As the coarsely segmented bony structures by FCN tend to be thicker, convolutional neural network (CNN) is further utilized for fine-grained segmentation. To enhance the discriminative ability of the CNN, we particularly concatenate the predicted probability maps from FCN and the original MRI, and feed them together into the CNN to provide more context information for segmentation. Experimental results demonstrate a good performance and also the clinical feasibility of our proposed 3D deep-learning based cascade framework.
UR - http://www.scopus.com/inward/record.url?scp=85029669526&partnerID=8YFLogxK
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U2 - 10.1007/978-3-319-67389-9_31
DO - 10.1007/978-3-319-67389-9_31
M3 - Conference contribution
AN - SCOPUS:85029669526
SN - 9783319673882
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 266
EP - 273
BT - Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings
A2 - Shi, Yinghuan
A2 - Suk, Heung-Il
A2 - Suzuki, Kenji
A2 - Wang, Qian
PB - Springer-Verlag
T2 - 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
Y2 - 10 September 2017 through 10 September 2017
ER -