TY - GEN
T1 - Robust and fully automated segmentation of mandible from CT scans
AU - Torosdagli, Neslisah
AU - Liberton, Denise K.
AU - Verma, Payal
AU - Sincan, Murat
AU - Lee, Janice
AU - Pattanaik, Sumanta
AU - Bagci, Ulas
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/15
Y1 - 2017/6/15
N2 - Mandible bone segmentation from computed tomography (CT) scans is challenging due to mandible's structural irregularities, complex shape patterns, and lack of contrast in joints. Furthermore, connections of teeth to mandible and mandible to remaining parts of the skull make it extremely difficult to identify mandible boundary automatically. This study addresses these challenges by proposing a novel framework where we define the segmentation as two complementary tasks: recognition and delineation. For recognition, we use random forest regression to localize mandible in 3D. For delineation, we propose to use 3D gradient-based fuzzy connectedness (FC) image segmentation algorithm, operating on the recognized mandible sub-volume. Despite heavy CT artifacts and dental fillings, consisting half of the CT image data in our experiments, we have achieved highly accurate detection and delineation results. Specifically, detection accuracy more than 96% (measured by union of intersection (UoI)), the delineation accuracy of 91% (measured by dice similarity coefficient), and less than 1 mm in shape mismatch (Hausdorff Distance) were found.
AB - Mandible bone segmentation from computed tomography (CT) scans is challenging due to mandible's structural irregularities, complex shape patterns, and lack of contrast in joints. Furthermore, connections of teeth to mandible and mandible to remaining parts of the skull make it extremely difficult to identify mandible boundary automatically. This study addresses these challenges by proposing a novel framework where we define the segmentation as two complementary tasks: recognition and delineation. For recognition, we use random forest regression to localize mandible in 3D. For delineation, we propose to use 3D gradient-based fuzzy connectedness (FC) image segmentation algorithm, operating on the recognized mandible sub-volume. Despite heavy CT artifacts and dental fillings, consisting half of the CT image data in our experiments, we have achieved highly accurate detection and delineation results. Specifically, detection accuracy more than 96% (measured by union of intersection (UoI)), the delineation accuracy of 91% (measured by dice similarity coefficient), and less than 1 mm in shape mismatch (Hausdorff Distance) were found.
KW - Computed Tomography
KW - Craniofacial Image Analysis
KW - Fuzzy Connectivity
KW - Mandible Segmentation
KW - Random Forest
UR - http://www.scopus.com/inward/record.url?scp=85023169955&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85023169955&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2017.7950734
DO - 10.1109/ISBI.2017.7950734
M3 - Conference contribution
AN - SCOPUS:85023169955
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1209
EP - 1212
BT - 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
PB - IEEE Computer Society
T2 - 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017
Y2 - 18 April 2017 through 21 April 2017
ER -