Orthognathic surgery is a crucial intervention for correcting dentofacial skeletal deformities to enhance occlusal functionality and facial aesthetics. Accurate postoperative facial appearance prediction remains challenging due to the complex nonlinear interactions between skeletal movements and facial soft tissue. Existing biomechanical, parametric models and deep-learning approaches either lack computational efficiency or fail to fully capture these intricate interactions. To address these limitations, we propose Neural Implicit Craniofacial Model (NICE) which employs implicit neural representations for accurate anatomical reconstruction and surgical outcome prediction. NICE comprises a shape module, which employs region-specific implicit Signed Distance Function (SDF) decoders to reconstruct the facial surface, maxilla, and mandible, and a surgery module, which employs region-specific deformation decoders. These deformation decoders are driven by a shared surgical latent code to effectively model the complex, nonlinear biomechanical response of the facial surface to skeletal movements, incorporating anatomical prior knowledge. The deformation decoders output point-wise displacement fields, enabling precise modeling of surgical outcomes. Extensive experiments demonstrate that NICE outperforms current state-of-the-art methods, notably improving prediction accuracy in critical facial regions such as lips and chin, while robustly preserving anatomical integrity. This work provides a clinically viable tool for enhanced surgical planning and patient consultation in orthognathic procedures.

Bioresorbable Vascular Scaffolds (BVS) are currently one of the most frequently-used type of stent during percutaneous coronary intervention. It's very important to conduct struts malappostion analysis during operation. Currently, BVS malappostion analysis in intravascular optical coherence tomography (IVOCT) images is mainly conducted manually, which is labor intensive and time consuming. In our previous work, a novel framework was presented to automatically detect and segment BVS struts for malappostion analysis. However, limited by the detection performance, the framework faced some challenges under complex background. In this paper, we proposed a robust BVS struts detection method based on Region-based Fully Convolutional Network (R-FCN). The detection model mainly consisted of two modules: 1) a Region Proposal Network (RPN), used to extract struts region of interest (ROIs) in the image and, 2) a detection module, used to classify the ROIs and regress a bounding box for each ROI. The network was initialized by pre-trained ImageNet model and then trained based on our labeled data which contained 1231 IVOCT images. Tested on a total of 480 IVOCT images with 4096 BVS struts, our method achieved 97.9% true positive rate with 4.79% false positive rate. It concludes that the proposed method is efficient and robust for BVS struts detection.

Intravascular optical coherence tomography is the state-of-the-art imaging modality in percutaneous coronary intervention planning and evaluation, in which side branch ostium and main vascular measurements play critical roles. However, manual measurement is time consuming and labor intensive. In this paper, we propose a fully automatic method for side branch ostium detection and main vascular segmentation to make up manual deficiency. In our method, side branch ostium points are first detected and subsequently used to divide the lumen contour into side branch and main vascular regions. Based on the division, main vascular contour is then smoothly fitted for segmentation. In side branch ostium detection, our algorithm creatively converts the definition of curvature into the calculation of the signed included angles in global view, and originally applies a differential filter to highlight the feature of side branch ostium points. A total of 4618 images from 22 pullback runs were used to evaluate the performance of the presented method. The validation results of side branch detection were TPR = 82.8 $\%$, TNR = 98.7$\%$ , PPV = 86.8$\%$, NPV = 98.7$\%$. The average ostial distance error (ODE) was 0.22 mm, and the DSC of main vascular segmentation was 0.96. In conclusion, the qualitative and quantitative evaluation indicated that the presented method is effective and accurate.

Bioresorbable Vascular Scaffolds (BVS) are the most promising type of stent in percutaneous coronary intervention. For accurate BVS struts apposition assessment, intravascular optical coherence tomography (IVOCT) is the state-of-the-art imaging modality. However, manual analysis for IVOCT frames is time consuming and labor intensive. In this paper, we propose an automatic method for BVS struts center and region detection based on Adaboost algorithm and Haar-like features. Then, dynamic programming algorithm is applied to segment the contour of BVS struts. Based on the segmentation results, the apposed or malapposed struts can be automatically distinguished. By comparing the manual and automatic detection and segmentation results, our method correctly detected and segmented 87.7% of 4029 BVS struts with 18.6% false positives. The average Dice's coefficient for the correctly detected struts was 0.78. In conclusion, the evaluation suggested that this method is accurate and robust for BVS struts detection and segmentation.

Multiatlas based method is commonly used in medical image segmentation. In multiatlas based image segmentation, atlas selection and combination are considered as two key factors affecting the performance. Recently, manifold learning based atlas selection methods have emerged as very promising methods. However, due to the complexity of prostate structures in raw images, it is difficult to get accurate atlas selection results by only measuring the distance between raw images on the manifolds. Although the distance between the regions to be segmented across images can be readily obtained by the label images, it is infeasible to directly compute the distance between the test image (gray) and the label images (binary). This paper tries to address this problem by proposing a label image constrained atlas selection method, which exploits the label images to constrain the manifold projection of raw images. Analyzing the data point distribution of the selected atlases in the manifold subspace, a novel weight computation method for atlas combination is proposed. Compared with other related existing methods, the experimental results on prostate segmentation from T2w MRI showed that the selected atlases are closer to the target structure and more accurate segmentation were obtained by using our proposed method.

Atlas selection plays an important role in multiatlas based image segmentation. In atlas selection methods, manifold learning based techniques have recently emerged as very promisingly. However, due to the complexity of anatomical structures in raw images, it is difficult to get accurate atlas selection results by measuring only the distance between raw images on the manifolds. In this paper, we tackle this problem by proposing a label image constrained atlas selection (LICAS) method to exploit the shape and size information of the regions to be segmented from the label images. Constrained by the label images, a new manifold projection method is developed to help uncover the intrinsic similarity between the regions of interest across images. Compared with other existing methods, the experimental results of segmentation on 60 Magnetic Resonance (MR) images showed that the selected atlases are closer to the target structure and more accurate segmentation can be obtained by using the proposed method.

In medical image analysis, atlas-based segmentation has become a popular approach. Given a target image, how to select the atlases with the similar shape of anatomical structure to the input image is one of the most critical factors affecting the segmentation accuracy. In this paper, we propose a novel strategy by putting the images on a manifold to analyze the intrinsic similarity between the images. A subset of atlases can be selected and the optimal fusion weights are computed in a low-dimensional manifold space. Finally, it combines the selected atlases by using the corresponding weights for image segmentation. The experimental results demonstrated that our proposed method is robust and accurate especially when a large number of training samples are available.