Automatic segmentation of the mandible from computed tomography scans for 3D virtual surgical planning using the convolutional neural network. Physics in Medicine and Biology, 64(17), [175020].
Objectives: In case of surgical removal of oral squamous cell carcinomas, a resection of mandibular bone is frequently part of the treatment. Nowadays, such resections frequently include the application of 3D virtual surgical planning (VSP) and guided surgery techniques. In this paper, current methods for 3D VSP leads for optimisation of the workflow, and patient-specific application of guides and implants are reviewed. Recent findings: Current methods for 3D VSP enable multi-modality fusion of images. This fusion of images is not restricted to a specific software package or workflow. New strategies for 3D VSP in Oral and Maxillofacial Surgery include finite element analysis, deep learning and advanced augmented reality techniques. These strategies aim to improve the treatment in terms of accuracy, predictability and safety. Conclusions: Application of the discussed novel technologies and strategies will improve the accuracy and safety of mandibular resection and reconstruction planning. Accurate, easy-to-use, safe and efficient three-dimensional VSP can be applied for every patient with malignancies needing resection of the mandible.
Medical imaging techniques, such as (cone beam) computed tomography and magnetic resonance imaging, have proven to be a valuable component for oral and maxillofacial surgery (OMFS). Accurate segmentation of the mandible from head and neck (H&N) scans is an important step in order to build a personalized 3D digital mandible model for 3D printing and treatment planning of OMFS. Segmented mandible structures are used to effectively visualize the mandible volumes and to evaluate particular mandible properties quantitatively. However, mandible segmentation is always challenging for both clinicians and researchers, due to complex structures and higher attenuation materials, such as teeth (filling) or metal implants that easily lead to high noise and strong artifacts during scanning. Moreover, the size and shape of the mandible vary to a large extent between individuals. Therefore, mandible segmentation is a tedious and time-consuming task and requires adequate training to be performed properly. With the advancement of computer vision approaches, researchers have developed several algorithms to automatically segment the mandible during the last two decades. The objective of this review was to present the available fully (semi)automatic segmentation methods of the mandible published in different scientific articles. This review provides a vivid description of the scientific advancements to clinicians and researchers in this field to help develop novel automatic methods for clinical applications.
Over the last decennia, a large variety of osteosynthesis plates and prostheses has been presented in the literature for the treatment of, for example, trauma, oncology or TMJ patients. These applications have in common that failure (loosening or fracturing) of the implanted material, or the screw-fixation of the implant to the patient's bone will often result in failure of the application. It appears crucial for success that the implanted osteosynthesis plates and its fixations are of a matching strength for the patient's specific situation. Mandibular reconstruction failure through either osteosynthesis plate failure or screw loosening are widely reported in the literature as the most common
Background
The aim of this study was to introduce a complete 3D workflow for immediate implant retained prosthetic rehabilitation following maxillectomy in cancer surgery. The workflow consists of a 3D virtual surgical planning for tumor resection, zygomatic implant placement, and for an implant-retained prosthetic-obturator to fit the planned outcome situation for immediate loading.
Materials and methods
In this study, 3D virtual surgical planning and resection of the maxilla, followed by guided placement of 10 zygomatic implants, using custom cutting and drill/placement-guides, was performed on 5 fresh frozen human cadavers. A preoperatively digitally designed and printed obturator prosthesis was placed and connected to the zygomatic implants. The accuracy of the implant positioning was obtained using 3D deviation analysis by merging the pre- and post-operative CT scan datasets.
Results
The preoperatively designed and manufactured obturator prostheses matched accurately the per-operative implant positions. All five obturators could be placed and fixated for immediate loading. The mean prosthetic point deviation on the cadavers was 1.03 ± 0.85 mm; the mean entry point deviation was 1.20 ± 0.62 mm; and the 3D angle deviation was 2.97 ± 1.44°.
Conclusions
It is possible to 3D plan and accurately execute the ablative surgery, placement of zygomatic implants, and immediate placement of an implant-retained obturator prosthesis with 3D virtual surgical planning.The next step is to apply the workflow in the operating room in patients planned for maxillectomy.
Purpose of reviewThe present review describes the latest development of 3D virtual surgical planning (VSP) and computer aided design (CAD) for reconstruction of maxillary defects with an aim of fully prosthetic rehabilitation. The purpose is to give an overview of different methods that use CAD in maxillary reconstruction in patients with head and neck cancer.Recent findings 3D VSP enables preoperative planning of resection margins and osteotomies. The current 3D VSP workflow is expanded with multimodal imaging, merging decision supportive information. Development of more personalized implants is possible using CAD, individualized virtual muscle modelling and topology optimization. Meanwhile the translation of the 3D VSP towards surgery is improved by techniques like intraoperative imaging and augmented reality. Recent improvements of preoperative 3D VSP enables surgical reconstruction and/or prosthetic rehabilitation of the surgical defect in one combined procedure.
SummaryWith the use of 3D VSP and CAD, ablation surgery, reconstructive surgery, and prosthetic rehabilitation can be planned preoperatively. Many reconstruction possibilities exist and a choice depends on patient characteristics, tumour location and experience of the surgeon. The overall objective in patients with maxillary defects is to follow a prosthetic-driven reconstruction with the aim to restore facial form, oral function, and do so in accordance with the individual needs of the patient.
Accurate mandible segmentation is significant in the field of maxillofacial surgery to guide clinical diagnosis and treatment and develop appropriate surgical plans. In particular, cone-beam computed tomography (CBCT) images with metal parts, such as those used in oral and maxillofacial surgery (OMFS), often have susceptibilities when metal artifacts are present such as weak and blurred boundaries caused by a high-attenuation material and a low radiation dose in image acquisition. To overcome this problem, this paper proposes a novel deep learning-based approach (SASeg) for automated mandible segmentation that perceives overall mandible anatomical knowledge. SASeg utilizes a prior shape feature extractor (PSFE) module based on a mean mandible shape, and recurrent connections maintain the continuity structure of the mandible. The effectiveness of the proposed network is substantiated on a dental CBCT dataset from orthodontic treatment containing 59 patients. The experiments show that the proposed SASeg can be easily used to improve the prediction accuracy in a dental CBCT dataset corrupted by metal artifacts. In addition, the experimental results on the PDDCA dataset demonstrate that, compared with the state-of-the-art mandible segmentation models, our proposed SASeg can achieve better segmentation performance.
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