Purpose In cranio-maxillofacial surgery, it is of great clinical significance to segment mandible accurately and automatically from CT images. However, the connected region and blurred boundary in teeth and condyles make the process challenging. At present, the mandible is commonly segmented by experienced doctors using manually or semi-automatic methods, which is time-consuming and has poor segmentation consistency. In addition, existing automatic segmentation methods still have problems such as region misjudgment, low accuracy, and time-consuming. Methods For these issues, an automatic mandibular segmentation method using 3d fully convolutional neural network based on densely connected atrous spatial pyramid pooling (DenseASPP) and attention gates (AG) was proposed in this paper. Firstly, the DenseASPP module was added to the network for extracting dense features at multiple scales. Thereafter, the AG module was applied in each skip connection to diminish irrelevant background information and make the network focus on segmentation regions. Finally, a loss function combining dice coefficient and focal loss was used to solve the imbalance among sample categories. Results Test results showed that the proposed network obtained a relatively good segmentation result, with a Dice score of 97.588 ± 0.425%, Intersection over Union of 95.293 ± 0.812%, sensitivity of 96.252 ± 1.106%, average surface distance of 0.065 ± 0.020 mm and 95% Hausdorff distance of 0.491 ± 0.021 mm in segmentation accuracy. The comparison with other segmentation networks showed that our network not only had a relatively high segmentation accuracy but also effectively reduced the network's misjudgment. Meantime, the surface distance error also showed that our segmentation results were relatively close to the ground truth.
ConclusionThe proposed network has better segmentation performance and realizes accurate and automatic segmentation of the mandible. Furthermore, its segmentation time is 50.43 s for one CT scan, which greatly improves the doctor's work efficiency. It will have practical significance in cranio-maxillofacial surgery in the future.
Background
The patient‐specific templates for osteotomy often have complex surface features. Using current commercial software to design such templates is quite complicated, tedious and unrepeatable.
Aims
In this study, a novel surgical planning system for oral and maxillofacial surgery named EasyTemplate is developed, aiming to help doctors shorten the modelling time and assure the reliability in template design.
Materials & Methods
In the simplified design process of an osteotomy guide, the main template can be formed efficiently using a surface offsetting algorithm, which is based on isosurface extraction and oriented bounding box. Thereafter, the cutting grooves can be generated automatically.
Results
A complicated surgical guide could be built accurately in about 10 min. Clinical orthognathic cases were conducted successfully using osteotomy and repositioning templates designed by EasyTemplate.
Discussion
Compared with commercially available softwares, higher efficiency and simpler design process were achieved, moreover, the time cost is one‐third or even less.
Conclusion
EasyTemplate can be a useful alternative to traditional softwares. This software allows the auto‐generation algorithm which helps avoid a tedious modeling process while providing basic shapes for designers.
Objectives: The purpose of this study is to establish a novel, reproducible technique to obtain the BIC area (BICA) between zygomatic implants and zygomatic bone based on post-operative cone-beam computed tomography (CBCT) images. Three-dimensional (3D) image registration and segmentation were used to eliminate the effect of metal-induced artifacts of zygomatic implants. Methods: An ex-vivo study was included to verify the feasibility of the new method. Then, the radiographic bone-to-implant contact (rBIC) of 143 implants was measured in a total of 50 patients. To obtain the BICA of zygomatic implants and the zygomatic bone, several steps were necessary, including image preprocessing of CBCT scans, identification of the position of zygomatic implants, registration, and segmentation of pre- and post-operative CBCT images, and 3D reconstruction of models. The conventional two-dimensional (2D) linear rBIC (rBICc) measurement method with post-operative CBCT images was chosen as a comparison. Results: The mean values of rBIC and rBICc were 15.08 ± 5.92 mm and 14.77 ± 5.14 mm, respectively. A statistically significant correlation was observed between rBIC and rBICc values ([Formula: see text]=0.86, p < 0.0001). Conclusions: This study proposed a standardized, repeatable, noninvasive technique to quantify the rBIC of post-operative zygomatic implants in 3D terms. This technique is comparable to conventional 2D linear measurements and seems to be more reliable than these conventional measurements; thus, this method could serve as a valuable tool in the performance of clinical research protocols.
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