Objective classification system for sagittal craniosynostosis based on suture segmentation.
OBJECTIVE
Spring-assisted surgery is an effective and minimally invasive treatment for sagittal craniosynostosis (CSO). The principal barrier to the advancement of spring-assisted surgery is the patient-specific spring selection. The selection of spring force depends on the suture involved, subtypes of sagittal CSO, and age of the infant, among other factors. Clinically, physicians manually judge the subtype of sagittal CSO patients based on their CT image data, which may cause bias from different clinicians. An objective system would be helpful to stratify the sagittal CSO patients and make spring choice less subjective.


METHODS
The authors developed a novel informatics system to automatically segment and characterize sutures and classify sagittal CSO. The proposed system is composed of three phases: preprocessing, sutures segmentation, and classification. First, the three-dimensional (3D) skull was extracted from the CT images and aligned with the symmetry of the cranial vault. Second, a "hemispherical projection" algorithm was developed to transform 3D surface of the skull to a polar two-dimensional plane. Through the transformation, an "effective" projected region can be obtained to enable easy segmentation of sutures. Then, the different types of sutures, such as coronal sutures, lambdoid sutures, sagittal suture, and metopic suture, obtained from the segmented sutures were further identified by a dual-projection technique of the midline of the sutures. Finally, 108 quantified features of sutures were extracted and selected by a proposed multiclass feature scoring system. The sagittal CSO patients were classified into four subtypes: anterior, central, posterior, and complex with the support vector machine approach. Fivefold cross validation (CV) was employed to evaluate the capability of selected features in discriminating the four subtypes in 33 sagittal CSO patients. Receiver operating characteristics (ROC) curves were used to assess the robustness of the developed system.


RESULTS
The segmentation results of the proposed method were clinically acceptable for the qualitative evaluation. For the quantitative evaluation, the fivefold CV accuracy of the classification for the four subtypes was 72.7%. This classification system was reliable with the area under curve (in ROC analysis) being greater than 0.8 for four two-class problems.


CONCLUSIONS
The proposed hemispherical projection algorithm based on backtracking search can successfully segment sutures of the cranial vault. The classification system can also offer a desirable performance. As a result, the proposed segmentation and classification system is expected to bring insights into clinic research and the selection of the spring force to facilitate widespread application of this minimally invasive treatment.
Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157.
Xiaohua
X
Qian
Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157.
Hua
H
Tan
Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157.
Jian
J
Zhang
SJTU-CU, International Cooperative Research Center, Department of Engineering Mechanics, School of Naval Architecture Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, Chinaand Medical Center Boulevard, Winston-Salem, North Carolina 27157.
Xiahai
X
Zhuang
Department of Plastic and Reconstructive Surgery, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157.
Leslie
L
Branch
Department of Plastic and Reconstructive Surgery, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157.
Chaire
C
Sanger
Department of Plastic and Reconstructive Surgery, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157.
Allison
A
Thompson
Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157.
Weiling
W
Zhao
Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157.
King Chuen
KC
Li
Department of Plastic and Reconstructive Surgery, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157.
Lisa
L
David
Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157.
Xiaobo
X
Zhou
D000465:Algorithms; D003393:Cranial Sutures / Q000000981:diagnostic imaging* / Q000601:surgery; D003398:Craniosynostoses / Q000000981:diagnostic imaging* / Q000601:surgery; D006801:Humans; D021621:Imaging, Three-Dimensional; D008490:Medical Informatics; D019060:Minimally Invasive Surgical Procedures; D014057:Tomography, X-Ray Computed