Segmentation of craniomaxillofacial bony structures from MRI with a 3D deep-learning based cascade framework

Dong Nie, Li Wang, Roger Trullo, Jianfu Li, Peng Yuan, James Xia, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings
PublisherSpringer Verlag
Pages266-273
Number of pages8
Volume10541 LNCS
ISBN (Print)9783319673882
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event8th 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 - Quebec City, Canada
Duration: 2017 Sep 102017 Sep 10

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10541 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th 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
CountryCanada
CityQuebec City
Period17/9/1017/9/10

Fingerprint

Magnetic Resonance Imaging
Magnetic resonance imaging
Cascade
Segmentation
Computed Tomography
Neural Networks
Neural networks
Tomography
Soft Tissue
Ionizing radiation
Network Architecture
Network architecture
Bone
Modality
Surgery
Brain
Signal to noise ratio
Diagnostics
Defects
Radiation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Nie, D., Wang, L., Trullo, R., Li, J., Yuan, P., Xia, J., & Shen, D. (2017). Segmentation of craniomaxillofacial bony structures from MRI with a 3D deep-learning based cascade framework. In Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings (Vol. 10541 LNCS, pp. 266-273). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10541 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-67389-9_31

Segmentation of craniomaxillofacial bony structures from MRI with a 3D deep-learning based cascade framework. / Nie, Dong; Wang, Li; Trullo, Roger; Li, Jianfu; Yuan, Peng; Xia, James; Shen, Dinggang.

Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. Vol. 10541 LNCS Springer Verlag, 2017. p. 266-273 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10541 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Nie, D, Wang, L, Trullo, R, Li, J, Yuan, P, Xia, J & Shen, D 2017, Segmentation of craniomaxillofacial bony structures from MRI with a 3D deep-learning based cascade framework. in Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. vol. 10541 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10541 LNCS, Springer Verlag, pp. 266-273, 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, Quebec City, Canada, 17/9/10. https://doi.org/10.1007/978-3-319-67389-9_31
Nie D, Wang L, Trullo R, Li J, Yuan P, Xia J et al. Segmentation of craniomaxillofacial bony structures from MRI with a 3D deep-learning based cascade framework. In Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. Vol. 10541 LNCS. Springer Verlag. 2017. p. 266-273. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-67389-9_31
Nie, Dong ; Wang, Li ; Trullo, Roger ; Li, Jianfu ; Yuan, Peng ; Xia, James ; Shen, Dinggang. / Segmentation of craniomaxillofacial bony structures from MRI with a 3D deep-learning based cascade framework. Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings. Vol. 10541 LNCS Springer Verlag, 2017. pp. 266-273 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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