Spatial Prior-Guided Bi-Directional Cross-Attention Transformers for Tooth Instance Segmentation.

Tooth instance segmentation of dental panoramic X-ray images represents a task of significant clinical importance. Teeth demonstrate symmetry within the upper and lower jawbones and are arranged in a specific order. However, previous studies frequently overlook this crucial spatial prior information, resulting in misidentifications of tooth categories for adjacent or similarly shaped teeth. In this paper, we propose SPGTNet, a spatial prior-guided transformer method, designed to both the extracted tooth positional features from CNNs and the long-range contextual information from vision transformers for dental panoramic X-ray image segmentation. Initially, a center-based spatial prior perception module is employed to identify each tooth's centroid, thereby enhancing the spatial prior information for the CNN sequence features. Subsequently, a bi-directional cross-attention module is designed to facilitate the interaction between the spatial prior information of the CNN sequence features and the long-distance contextual features of the vision transformer sequence features. Finally, an instance identification head is employed to derive the tooth segmentation results. Extensive experiments on three public benchmark datasets have demonstrated the effectiveness and superiority of our proposed method in comparison with other state-of-the-art approaches. The proposed method demonstrates the capability to accurately identify and analyze tooth structures, thereby providing crucial information for dental diagnosis, treatment planning, and research.

The relationship of tongue fat content and efficacy of uvulopalatopharyngoplasty in Chinese patients with obstructive sleep apnea.

BACKGROUND: To investigate the relationship between tongue fat content and severity of obstructive sleep apnea (OSA) and its effects on the efficacy of uvulopalatopharyngoplasty (UPPP) in the Chinese group. METHOD: Fifty-two participants concluded to this study were diagnosed as OSA by performing polysomnography (PSG) then they were divided into moderate group and severe group according to apnea hypopnea index (AHI). All of them were also collected a series of data including age, BMI, height, weight, neck circumference, abdominal circumference, magnetic resonance imaging (MRI) of upper airway and the score of Epworth Sleepiness Scale (ESS) on the morning after they completed PSG. The relationship between tongue fat content and severity of OSA as well as the association between tongue fat content in pre-operation and surgical efficacy were analyzed.Participants underwent UPPP and followed up at 3rd month after surgery, and they were divided into two groups according to the surgical efficacy. RESULTS: There were 7 patients in the moderate OSA group and 45 patients in the severe OSA group. The tongue volume was significantly larger in the severe OSA group than that in the moderate OSA group. There was no difference in tongue fat volume and tongue fat rate between the two groups. There was no association among tongue fat content, AHI, obstructive apnea hypopnea index, obstructive apnea index and Epworth sleepiness scale (all P > 0.05), but tongue fat content was related to the lowest oxygen saturation (r=-0.335, P < 0.05). There was no significantly difference in pre-operative tongue fat content in two different surgical efficacy groups. CONCLUSIONS: This study didn't show an association between tongue fat content and the severity of OSA in the Chinese group, but it suggested a negative correlation between tongue fat content and the lowest oxygen saturation (LSaO2). Tongue fat content didn't influence surgical efficacy of UPPP in Chinese OSA patients. TRIAL REGISTRATION: This study didn't report on a clinical trial, it was retrospectively registered.

Volumetric Analysis of Amygdala and Hippocampal Subfields for Infants with Autism.

Previous studies have demonstrated abnormal brain overgrowth in children with autism spectrum disorder (ASD), but the development of specific brain regions, such as the amygdala and hippocampal subfields in infants, is incompletely documented. To address this issue, we performed the first MRI study of amygdala and hippocampal subfields in infants from 6 to 24 months of age using a longitudinal dataset. A novel deep learning approach, Dilated-Dense U-Net, was proposed to address the challenge of low tissue contrast and small structural size of these subfields. We performed a volume-based analysis on the segmentation results. Our results show that infants who were later diagnosed with ASD had larger left and right volumes of amygdala and hippocampal subfields than typically developing controls.

Simulation of Postoperative Facial Appearances via Geometric Deep Learning for Efficient Orthognathic Surgical Planning.

Orthognathic surgery corrects jaw deformities to improve aesthetics and functions. Due to the complexity of the craniomaxillofacial (CMF) anatomy, orthognathic surgery requires precise surgical planning, which involves predicting postoperative changes in facial appearance. To this end, most conventional methods involve simulation with biomechanical modeling methods, which are labor intensive and computationally expensive. Here we introduce a learning-based framework to speed up the simulation of postoperative facial appearances. Specifically, we introduce a facial shape change prediction network (FSC-Net) to learn the nonlinear mapping from bony shape changes to facial shape changes. FSC-Net is a point transform network weakly-supervised by paired preoperative and postoperative data without point-wise correspondence. In FSC-Net, a distance-guided shape loss places more emphasis on the jaw region. A local point constraint loss restricts point displacements to preserve the topology and smoothness of the surface mesh after point transformation. Evaluation results indicate that FSC-Net achieves 15× speedup with accuracy comparable to a state-of-the-art (SOTA) finite-element modeling (FEM) method.

BloodNet: An attention-based deep network for accurate, efficient, and costless bloodstain time since deposition inference.

The time since deposition (TSD) of a bloodstain, i.e., the time of a bloodstain formation is an essential piece of biological evidence in crime scene investigation. The practical usage of some existing microscopic methods (e.g., spectroscopy or RNA analysis technology) is limited, as their performance strongly relies on high-end instrumentation and/or rigorous laboratory conditions. This paper presents a practically applicable deep learning-based method (i.e., BloodNet) for efficient, accurate, and costless TSD inference from a macroscopic view, i.e., by using easily accessible bloodstain photos. To this end, we established a benchmark database containing around 50,000 photos of bloodstains with varying TSDs. Capitalizing on such a large-scale database, BloodNet adopted attention mechanisms to learn from relatively high-resolution input images the localized fine-grained feature representations that were highly discriminative between different TSD periods. Also, the visual analysis of the learned deep networks based on the Smooth Grad-CAM tool demonstrated that our BloodNet can stably capture the unique local patterns of bloodstains with specific TSDs, suggesting the efficacy of the utilized attention mechanism in learning fine-grained representations for TSD inference. As a paired study for BloodNet, we further conducted a microscopic analysis using Raman spectroscopic data and a machine learning method based on Bayesian optimization. Although the experimental results show that such a new microscopic-level approach outperformed the state-of-the-art by a large margin, its inference accuracy is significantly lower than BloodNet, which further justifies the efficacy of deep learning techniques in the challenging task of bloodstain TSD inference. Our code is publically accessible via https://github.com/shenxiaochenn/BloodNet. Our datasets and pre-trained models can be freely accessed via https://figshare.com/articles/dataset/21291825.

Dynamic Cross-Task Representation Adaptation for Clinical Targets Co-Segmentation in CT Image-Guided Post-Prostatectomy Radiotherapy.

Adjuvant and salvage radiotherapy after radical prostatectomy requires precise delineations of prostate bed (PB), i.e., the clinical target volume, and surrounding organs at risk (OARs) to optimize radiotherapy planning. Segmenting PB is particularly challenging even for clinicians, e.g., from the planning computed tomography (CT) images, as it is an invisible/virtual target after the operative removal of the cancerous prostate gland. Very recently, a few deep learning-based methods have been proposed to automatically contour non-contrast PB by leveraging its spatial reliance on adjacent OARs (i.e., the bladder and rectum) with much more clear boundaries, mimicking the clinical workflow of experienced clinicians. Although achieving state-of-the-art results from both the clinical and technical aspects, these existing methods improperly ignore the gap between the hierarchical feature representations needed for segmenting those fundamentally different clinical targets (i.e., PB and OARs), which in turn limits their delineation accuracy. This paper proposes an asymmetric multi-task network integrating dynamic cross-task representation adaptation (i.e., DyAdapt) for accurate and efficient co-segmentation of PB and OARs in one-pass from CT images. In the learning-to-learn framework, the DyAdapt modules adaptively transfer the hierarchical feature representations from the source task of OARs segmentation to match up with the target (and more challenging) task of PB segmentation, conditioned on the dynamic inter-task associations learned from the learning states of the feed-forward path. On a real-patient dataset, our method led to state-of-the-art results of PB and OARs co-segmentation. Code is available at https://github.com/ladderlab-xjtu/DyAdapt.

Bidirectional prediction of facial and bony shapes for orthognathic surgical planning.

This paper proposes a deep learning framework to encode subject-specific transformations between facial and bony shapes for orthognathic surgical planning. Our framework involves a bidirectional point-to-point convolutional network (P2P-Conv) to predict the transformations between facial and bony shapes. P2P-Conv is an extension of the state-of-the-art P2P-Net and leverages dynamic point-wise convolution (i.e., PointConv) to capture local-to-global spatial information. Data augmentation is carried out in the training of P2P-Conv with multiple point subsets from the facial and bony shapes. During inference, network outputs generated for multiple point subsets are combined into a dense transformation. Finally, non-rigid registration using the coherent point drift (CPD) algorithm is applied to generate surface meshes based on the predicted point sets. Experimental results on real-subject data demonstrate that our method substantially improves the prediction of facial and bony shapes over state-of-the-art methods.

Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans.

Accurately segmenting teeth and identifying the corresponding anatomical landmarks on dental mesh models are essential in computer-aided orthodontic treatment. Manually performing these two tasks is time-consuming, tedious, and, more importantly, highly dependent on orthodontists' experiences due to the abnormality and large-scale variance of patients' teeth. Some machine learning-based methods have been designed and applied in the orthodontic field to automatically segment dental meshes (e.g., intraoral scans). In contrast, the number of studies on tooth landmark localization is still limited. This paper proposes a two-stage framework based on mesh deep learning (called TS-MDL) for joint tooth labeling and landmark identification on raw intraoral scans. Our TS-MDL first adopts an end-to-end iMeshSegNet method (i.e., a variant of the existing MeshSegNet with both improved accuracy and efficiency) to label each tooth on the downsampled scan. Guided by the segmentation outputs, our TS-MDL further selects each tooth's region of interest (ROI) on the original mesh to construct a light-weight variant of the pioneering PointNet (i.e., PointNet-Reg) for regressing the corresponding landmark heatmaps. Our TS-MDL was evaluated on a real-clinical dataset, showing promising segmentation and localization performance. Specifically, iMeshSegNet in the first stage of TS-MDL reached an averaged Dice similarity coefficient (DSC) at 0.964±0.054 , significantly outperforming the original MeshSegNet. In the second stage, PointNet-Reg achieved a mean absolute error (MAE) of 0.597±0.761 mm in distances between the prediction and ground truth for 66 landmarks, which is superior compared with other networks for landmark detection. All these results suggest the potential usage of our TS-MDL in orthodontics.

Semantic Graph Attention With Explicit Anatomical Association Modeling for Tooth Segmentation From CBCT Images.

Accurate tooth identification and delineation in dental CBCT images are essential in clinical oral diagnosis and treatment. Teeth are positioned in the alveolar bone in a particular order, featuring similar appearances across adjacent and bilaterally symmetric teeth. However, existing tooth segmentation methods ignored such specific anatomical topology, which hampers the segmentation accuracy. Here we propose a semantic graph-based method to explicitly model the spatial associations between different anatomical targets (i.e., teeth) for their precise delineation in a coarse-to-fine fashion. First, to efficiently control the bilaterally symmetric confusion in segmentation, we employ a lightweight network to roughly separate teeth as four quadrants. Then, designing a semantic graph attention mechanism to explicitly model the anatomical topology of the teeth in each quadrant, based on which voxel-wise discriminative feature embeddings are learned for the accurate delineation of teeth boundaries. Extensive experiments on a clinical dental CBCT dataset demonstrate the superior performance of the proposed method compared with other state-of-the-art approaches.

Localization of Craniomaxillofacial Landmarks on CBCT Images Using 3D Mask R-CNN and Local Dependency Learning.

Cephalometric analysis relies on accurate detection of craniomaxillofacial (CMF) landmarks from cone-beam computed tomography (CBCT) images. However, due to the complexity of CMF bony structures, it is difficult to localize landmarks efficiently and accurately. In this paper, we propose a deep learning framework to tackle this challenge by jointly digitalizing 105 CMF landmarks on CBCT images. By explicitly learning the local geometrical relationships between the landmarks, our approach extends Mask R-CNN for end-to-end prediction of landmark locations. Specifically, we first apply a detection network on a down-sampled 3D image to leverage global contextual information to predict the approximate locations of the landmarks. We subsequently leverage local information provided by higher-resolution image patches to refine the landmark locations. On patients with varying non-syndromic jaw deformities, our method achieves an average detection accuracy of 1.38± 0.95mm, outperforming a related state-of-the-art method.

A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images.

Accurate delineation of individual teeth and alveolar bones from dental cone-beam CT (CBCT) images is an essential step in digital dentistry for precision dental healthcare. In this paper, we present an AI system for efficient, precise, and fully automatic segmentation of real-patient CBCT images. Our AI system is evaluated on the largest dataset so far, i.e., using a dataset of 4,215 patients (with 4,938 CBCT scans) from 15 different centers. This fully automatic AI system achieves a segmentation accuracy comparable to experienced radiologists (e.g., 0.5% improvement in terms of average Dice similarity coefficient), while significant improvement in efficiency (i.e., 500 times faster). In addition, it consistently obtains accurate results on the challenging cases with variable dental abnormalities, with the average Dice scores of 91.5% and 93.0% for tooth and alveolar bone segmentation. These results demonstrate its potential as a powerful system to boost clinical workflows of digital dentistry.

White Matter Free Water is a Composite Marker of Cerebral Small Vessel Degeneration.

To investigate the association between white matter free water (FW) and common imaging markers of cerebral small vessel diseases (CSVD) in two groups of subjects with different clinical status. One hundred and forty-four community subjects (mean age 60.5) and 84 CSVD subjects (mean age 61.2) were retrospectively included in the present study. All subjects received multi-modal magnetic resonance imaging and clinical assessments. The association between white matter FW and common CSVD imaging markers, including white matter hyperintensities (WMH), dilated perivascular space (PVS), lacunes, and microbleeds, were assessed using simple and multiple regression analysis. The association between FW and cognitive scores were also investigated. White matter FW was positively associated with WMH volume (ß = 0.270, p = 0.001), PVS volume (ß = 0.290, p < 0.001), number of microbleeds (ß = 0.148, p = 0.043), and age (ß = 0.170, p = 0.036) in the community cohort. In the CSVD cohort, FW was positively associated with WMH volume (ß = 0.648, p < 0.001), PVS score (ß = 0.224, p < 0.001), number of lacunes (ß = 0.140, p = 0.046), and sex (ß = 0.125, p = 0.036). The associations between FW and cognitive scores were stronger than conventional CSVD markers in both datasets. White matter FW is a potential composite marker that can sensitively detect cerebral small vessel degeneration and also reflect cognitive impairments.

Attention-Guided Hybrid Network for Dementia Diagnosis With Structural MR Images.

Deep-learning methods (especially convolutional neural networks) using structural magnetic resonance imaging (sMRI) data have been successfully applied to computer-aided diagnosis (CAD) of Alzheimer's disease (AD) and its prodromal stage [i.e., mild cognitive impairment (MCI)]. As it is practically challenging to capture local and subtle disease-associated abnormalities directly from the whole-brain sMRI, most of those deep-learning approaches empirically preselect disease-associated sMRI brain regions for model construction. Considering that such isolated selection of potentially informative brain locations might be suboptimal, very few methods have been proposed to perform disease-associated discriminative region localization and disease diagnosis in a unified deep-learning framework. However, those methods based on task-oriented discriminative localization still suffer from two common limitations, that is: 1) identified brain locations are strictly consistent across all subjects, which ignores the unique anatomical characteristics of each brain and 2) only limited local regions/patches are used for model training, which does not fully utilize the global structural information provided by the whole-brain sMRI. In this article, we propose an attention-guided deep-learning framework to extract multilevel discriminative sMRI features for dementia diagnosis. Specifically, we first design a backbone fully convolutional network to automatically localize the discriminative brain regions in a weakly supervised manner. Using the identified disease-related regions as spatial attention guidance, we further develop a hybrid network to jointly learn and fuse multilevel sMRI features for CAD model construction. Our proposed method was evaluated on three public datasets (i.e., ADNI-1, ADNI-2, and AIBL), showing superior performance compared with several state-of-the-art methods in both tasks of AD diagnosis and MCI conversion prediction.

Multi-Task Weakly-Supervised Attention Network for Dementia Status Estimation With Structural MRI.

Accurate prediction of clinical scores (of neuropsychological tests) based on noninvasive structural magnetic resonance imaging (MRI) helps understand the pathological stage of dementia (e.g., Alzheimer's disease (AD)) and forecast its progression. Existing machine/deep learning approaches typically preselect dementia-sensitive brain locations for MRI feature extraction and model construction, potentially leading to undesired heterogeneity between different stages and degraded prediction performance. Besides, these methods usually rely on prior anatomical knowledge (e.g., brain atlas) and time-consuming nonlinear registration for the preselection of brain locations, thereby ignoring individual-specific structural changes during dementia progression because all subjects share the same preselected brain regions. In this article, we propose a multi-task weakly-supervised attention network (MWAN) for the joint regression of multiple clinical scores from baseline MRI scans. Three sequential components are included in MWAN: 1) a backbone fully convolutional network for extracting MRI features; 2) a weakly supervised dementia attention block for automatically identifying subject-specific discriminative brain locations; and 3) an attention-aware multitask regression block for jointly predicting multiple clinical scores. The proposed MWAN is an end-to-end and fully trainable deep learning model in which dementia-aware holistic feature learning and multitask regression model construction are integrated into a unified framework. Our MWAN method was evaluated on two public AD data sets for estimating clinical scores of mini-mental state examination (MMSE), clinical dementia rating sum of boxes (CDRSB), and AD assessment scale cognitive subscale (ADAS-Cog). Quantitative experimental results demonstrate that our method produces superior regression performance compared with state-of-the-art methods. Importantly, qualitative results indicate that the dementia-sensitive brain locations automatically identified by our MWAN method well retain individual specificities and are biologically meaningful.

Two-Stream Graph Convolutional Network for Intra-Oral Scanner Image Segmentation.

Precise segmentation of teeth from intra-oral scanner images is an essential task in computer-aided orthodontic surgical planning. The state-of-the-art deep learning-based methods often simply concatenate the raw geometric attributes (i.e., coordinates and normal vectors) of mesh cells to train a single-stream network for automatic intra-oral scanner image segmentation. However, since different raw attributes reveal completely different geometric information, the naive concatenation of different raw attributes at the (low-level) input stage may bring unnecessary confusion in describing and differentiating between mesh cells, thus hampering the learning of high-level geometric representations for the segmentation task. To address this issue, we design a two-stream graph convolutional network (i.e., TSGCN), which can effectively handle inter-view confusion between different raw attributes to more effectively fuse their complementary information and learn discriminative multi-view geometric representations. Specifically, our TSGCN adopts two input-specific graph-learning streams to extract complementary high-level geometric representations from coordinates and normal vectors, respectively. Then, these single-view representations are further fused by a self-attention module to adaptively balance the contributions of different views in learning more discriminative multi-view representations for accurate and fully automatic tooth segmentation. We have evaluated our TSGCN on a real-patient dataset of dental (mesh) models acquired by 3D intraoral scanners. Experimental results show that our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.

Skull Segmentation from CBCT Images via Voxel-Based Rendering.

Skull segmentation from three-dimensional (3D) cone-beam computed tomography (CBCT) images is critical for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Convolutional neural network (CNN)-based methods are currently dominating volumetric image segmentation, but these methods suffer from the limited GPU memory and the large image size (e.g., 512 × 512 × 448). Typical ad-hoc strategies, such as down-sampling or patch cropping, will degrade segmentation accuracy due to insufficient capturing of local fine details or global contextual information. Other methods such as Global-Local Networks (GLNet) are focusing on the improvement of neural networks, aiming to combine the local details and the global contextual information in a GPU memory-efficient manner. However, all these methods are operating on regular grids, which are computationally inefficient for volumetric image segmentation. In this work, we propose a novel VoxelRend-based network (VR-U-Net) by combining a memory-efficient variant of 3D U-Net with a voxel-based rendering (VoxelRend) module that refines local details via voxel-based predictions on non-regular grids. Establishing on relatively coarse feature maps, the VoxelRend module achieves significant improvement of segmentation accuracy with a fraction of GPU memory consumption. We evaluate our proposed VR-U-Net in the skull segmentation task on a high-resolution CBCT dataset collected from local hospitals. Experimental results show that the proposed VR-U-Net yields high-quality segmentation results in a memory-efficient manner, highlighting the practical value of our method.

A Self-Supervised Deep Framework for Reference Bony Shape Estimation in Orthognathic Surgical Planning.

Virtual orthognathic surgical planning involves simulating surgical corrections of jaw deformities on 3D facial bony shape models. Due to the lack of necessary guidance, the planning procedure is highly experience-dependent and the planning results are often suboptimal. A reference facial bony shape model representing normal anatomies can provide an objective guidance to improve planning accuracy. Therefore, we propose a self-supervised deep framework to automatically estimate reference facial bony shape models. Our framework is an end-to-end trainable network, consisting of a simulator and a corrector. In the training stage, the simulator maps jaw deformities of a patient bone to a normal bone to generate a simulated deformed bone. The corrector then restores the simulated deformed bone back to normal. In the inference stage, the trained corrector is applied to generate a patient-specific normal-looking reference bone from a real deformed bone. The proposed framework was evaluated using a clinical dataset and compared with a state-of-the-art method that is based on a supervised point-cloud network. Experimental results show that the estimated shape models given by our approach are clinically acceptable and significantly more accurate than that of the competing method.

DLLNet: An Attention-Based Deep Learning Method for Dental Landmark Localization on High-Resolution 3D Digital Dental Models.

Dental landmark localization is a fundamental step to analyzing dental models in the planning of orthodontic or orthognathic surgery. However, current clinical practices require clinicians to manually digitize more than 60 landmarks on 3D dental models. Automatic methods to detect landmarks can release clinicians from the tedious labor of manual annotation and improve localization accuracy. Most existing landmark detection methods fail to capture local geometric contexts, causing large errors and misdetections. We propose an end-to-end learning framework to automatically localize 68 landmarks on high-resolution dental surfaces. Our network hierarchically extracts multi-scale local contextual features along two paths: a landmark localization path and a landmark area-of-interest segmentation path. Higher-level features are learned by combining local-to-global features from the two paths by feature fusion to predict the landmark heatmap and the landmark area segmentation map. An attention mechanism is then applied to the two maps to refine the landmark position. We evaluated our framework on a real-patient dataset consisting of 77 high-resolution dental surfaces. Our approach achieves an average localization error of 0.42 mm, significantly outperforming related start-of-the-art methods.

SkullEngine: A Multi-Stage CNN Framework for Collaborative CBCT Image Segmentation and Landmark Detection.

Accurate bone segmentation and landmark detection are two essential preparation tasks in computer-aided surgical planning for patients with craniomaxillofacial (CMF) deformities. Surgeons typically have to complete the two tasks manually, spending ~12 hours for each set of CBCT or ~5 hours for CT. To tackle these problems, we propose a multi-stage coarse-to-fine CNN-based framework, called SkullEngine, for high-resolution segmentation and large-scale landmark detection through a collaborative, integrated, and scalable JSD model and three segmentation and landmark detection refinement models. We evaluated our framework on a clinical dataset consisting of 170 CBCT/CT images for the task of segmenting 2 bones (midface and mandible) and detecting 175 clinically common landmarks on bones, teeth, and soft tissues. Experimental results show that SkullEngine significantly improves segmentation quality, especially in regions where the bone is thin. In addition, SkullEngine also efficiently and accurately detect all of the 175 landmarks. Both tasks were completed simultaneously within 3 minutes regardless of CBCT or CT with high segmentation quality. Currently, SkullEngine has been integrated into a clinical workflow to further evaluate its clinical efficiency.

Deep Simulation of Facial Appearance Changes Following Craniomaxillofacial Bony Movements in Orthognathic Surgical Planning.

Facial appearance changes with the movements of bony segments in orthognathic surgery of patients with craniomaxillofacial (CMF) deformities. Conventional bio-mechanical methods, such as finite element modeling (FEM), for simulating such changes, are labor intensive and computationally expensive, preventing them from being used in clinical settings. To overcome these limitations, we propose a deep learning framework to predict post-operative facial changes. Specifically, FC-Net, a facial appearance change simulation network, is developed to predict the point displacement vectors associated with a facial point cloud. FC-Net learns the point displacements of a pre-operative facial point cloud from the bony movement vectors between pre-operative and simulated post-operative bony models. FC-Net is a weakly-supervised point displacement network trained using paired data with strict point-to-point correspondence. To preserve the topology of the facial model during point transform, we employ a local-point-transform loss to constrain the local movements of points. Experimental results on real patient data reveal that the proposed framework can predict post-operative facial appearance changes remarkably faster than a state-of-the-art FEM method with comparable prediction accuracy.