There has been a recent emphasis in surgical science on supplementing surgical training outside of the Operating Room (OR). Combining simulation training with the current surgical apprenticeship enhances surgical skills in the OR, without increasing the time spent in the OR practicing. Computer-assisted surgical (CAS) planning consists of performing operative techniques virtually using three-dimensional (3D) computer-based models reconstructed from 3D crosssectional imaging. The purpose of this paper is to present a CAS system to rehearse, visualize and quantify osteotomies, and demonstrate its usefulness in two different osteotomy surgical procedures, cranial vault reconstruction and femoral osteotomy. We found that the system could sufficiently simulate these two procedures. Our system takes advantage of the high-quality visualizations possible with 3DSlicer, as well as implements new infrastructure to allow for direct 3D interaction (cutting and positioning) with the bone models. We see the proposed osteotomy planner tool evolving towards incorporating different cutting templates to help depict several surgical scenarios, help 'trained' surgeons maintain operating skills, help rehearse a surgical sequence before heading to the OR, or even to help surgical planning for specific patient cases.
The evaluation of cranial malformations plays an essential role both in the early diagnosis and in the decision to perform surgical treatment for craniosynostosis. In clinical practice, both cranial shape and suture fusion are evaluated using CT images, which involve the use of harmful radiation on children. Three-dimensional (3D) photography offers noninvasive, radiation-free, and anesthetic-free evaluation of craniofacial morphology. The aim of this study is to develop an automated framework to objectively quantify cranial malformations in patients with craniosynostosis from 3D photography. We propose a new method that automatically extracts the cranial shape by identifying a set of landmarks from a 3D photograph. Specifically, it registers the 3D photograph of a patient to a reference template in which the position of the landmarks is known. Then, the method finds the closest cranial shape to that of the patient from a normative statistical shape multi-atlas built from 3D photographs of healthy cases, and uses it to quantify objectively cranial malformations. We calculated the cranial malformations on 17 craniosynostosis patients and we compared them with the malformations of the normative population used to build the multi-atlas. The average malformations of the craniosynostosis cases were 2.68 ± 0.75 mm, which is significantly higher (p<0.001) than the average malformations of 1.70 ± 0.41 mm obtained from the normative cases. Our approach can support the quantitative assessment of surgical procedures for cranial vault reconstruction without exposing pediatric patients to harmful radiation.
Conference Committee Involvement (3)
Computer-Aided Diagnosis
17 February 2025 | San Diego, California, United States
Computer-Aided Diagnosis
19 February 2024 | San Diego, California, United States
Computer-Aided Diagnosis
20 February 2023 | San Diego, California, United States
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