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Interactive Segmentation of Structures in the Head and Neck Using Steerable Active Contours

Institution:
1Comprehensive Cancer Center, Birmingham, AL, USA.
2Massachusetts General Hospital, Boston, MA, USA.
3Georgia Institute of Technology, Atlanta, GA, USA.
Publisher:
Wiley
Publication Date:
Jun-2013
Journal:
Med Phys
Volume Number:
40
Issue Number:
6Part32
Pages:
536
Citation:
Med Phys. 2013 Jun;40(6Part32):536.
Keywords:
Bioinformatics, Brain, Computed tomography, Control systems, Machine vision, Medical image contrast, Medical image segmentation, Medical imaging, Medical imaging, Tissues
Appears in Collections:
NAC, NA-MIC
Sponsors:
P41 RR013218/RR/NCRR NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
R01 MH082918/MH/NIMH NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Kolesov I., Karasev P., Shusharina N., Vela P., Tannenbaum A., Sharp G. Interactive Segmentation of Structures in the Head and Neck Using Steerable Active Contours. Med Phys. 2013 Jun;40(6Part32):536.
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PURPOSE: The purpose of this work is to investigate the performance of an interactive image segmentation method for radiotherapy contouring on computed tomography (CT) images. Manual segmentation is a time consuming task that is essential for treatment. Due to the low contrast of target structures, their similarity to surrounding tissue, and the required precision for the final segmentation Result, automatic methods do not exhibit robust performance. Furthermore, when an automatic segmentation algorithm produces errors at the structure boundary, they are tedious for a human user to correct. For this experiment, it is hypothesized that an interactive algorithm can attain ground truth results in a fraction of the the time needed for manual segmentation. METHODS: The proposed method is interactive segmentation that tightly couples a human "expert user" with a framework from computer vision called "active contours" to create a closed loop control system. As a Result, the strengths (i.e., quickly delineating complicated target boundaries) of the automatic method can be leveraged by the user, who guides the algorithm based on his expert knowledge throughout the process. Experimental segmentations have been performed both with and without the control system feedback, the accuracy of the resulting labels will be compared along with the time required to create the labels. RESULTS: Four structures were evaluated: left/right eye ball, brain stem, and mandible. Tests show that virtually identical segmentations are performed with and without control system feedback. However, the time required to complete the task is significantly less than what is needed for fully manual contouring. CONCLUSION: Interactive segmentation using control system feedback is shown to reduce the time and effort needed to segment targets in CT volumes of the head and neck region.