Automatic 3D Research Articles

Bodyformer: Semantics-guided 3D Body Gesture Synthesis with Transformer

Automatic gesture synthesis from speech is a topic that has attracted researchers for applications in remote communication, video games and Metaverse. Learning the mapping between speech and 3D full-body gestures is difficult due to the stochastic nature of the problem and the lack of a rich cross-modal dataset that is needed for training. In this paper, we propose a novel transformer-based framework for automatic 3D body gesture synthesis from speech. To learn the stochastic nature of the body gesture during speech, we propose a variational transformer to effectively model a probabilistic distribution over gestures, which can produce diverse gestures during inference. Furthermore, we introduce a mode positional embedding layer to capture the different motion speeds in different speaking modes. To cope with the scarcity of data, we design an intra-modal pre-training scheme that can learn the complex mapping between the speech and the 3D gesture from a limited amount of data. Our system is trained with either the Trinity speech-gesture dataset or the Talking With Hands 16.2M dataset. The results show that our system can produce more realistic, appropriate, and diverse body gestures compared to existing state-of-the-art approaches.

Automatic 3D Building Model Generation from Airborne LiDAR Data and OpenStreetMap Using Procedural Modeling

This research is primarily focused on utilizing available airborne LiDAR data and spatial data from the OpenStreetMap (OSM) database to generate 3D models of buildings for a large-scale urban area. The city center of Ljubljana, Slovenia, was selected for the study area due to data availability and diversity of building shapes, heights, and functions, which presented a challenge for the automated generation of 3D models. To extract building heights, a range of data sources were utilized, including OSM attribute data, as well as georeferenced and classified point clouds and a digital elevation model (DEM) obtained from openly available LiDAR survey data of the Slovenian Environment Agency. A digital surface model (DSM) and digital terrain model (DTM) were derived from the processed LiDAR data. Building outlines and attributes were extracted from OSM and processed using QGIS. Spatial coverage of OSM data for buildings in the study area is excellent, whereas only 18% have attributes describing external appearance of the building and 6% describing roof type. LASTools software (rapidlasso GmbH, Friedrichshafener Straße 1, 82205 Gilching, GERMANY) was used to derive and assign building heights from 3D coordinates of the segmented point clouds. Various software options for procedural modeling were compared and Blender was selected due to the ability to process OSM data, availability of documentation, and low computing requirements. Using procedural modeling, a 3D model with level of detail (LOD) 1 was created fully automated. After analyzing roof types, a 3D model with LOD2 was created fully automated for 87.64% of buildings. For the remaining buildings, a comparison of procedural roof modeling and manual roof editing was performed. Finally, a visual comparison between the resulting 3D model and Google Earth’s model was performed. The main objective of this study is to demonstrate the efficient modeling process using open data and free software and resulting in an enhanced accuracy of the 3D building models compared to previous LOD2 iterations.

ENABLING SCAN-TO-BIM WORKFLOW FOR HERITAGE CONSERVATION AND MANAGEMENT PROCESS

Abstract. The paper stresses the potential of digital technology in the field of Cultural Heritage (CH), focusing on Architectural Heritage (AH) conservation and management process. It presents a workflow based on non-parametric automatic 3D modeling and Industry Foundation Classes (IFC) authoring to combine geometry and semantic information. The goal of such a methodology is to overcome time-consuming manual tasks that still limit the Historical Building Information Modelling (HBIM), which nevertheless proves to be one of the most effective digital tools for the conservation and management of AH in the medium to long term. The proposed workflow consists of five steps: the 3D survey campaign and raw data processing, the semantic annotation of images according to different information layers, the annotation transfer from images to the point cloud representing the studied architecture, the obtained Point Cloud Information Model (PCIM) automatic segmentation and 3D mesh wrapping, and finally the IFC generation for the HBIM representation. This unconventional digital chain was tested to support the knowledge representation of Villa Leonardi, an historical house in the Marche Region (Italy). The final output is a 3D model of the building, automatically processed from digital survey data, and semantically enriched thanks to the material consistency and decay analysis carried out and annotated on spherical panoramas. The effectiveness of this approach was proven in the conservation project for the façades of the house: enriched data on materials and decay supported an action plan of restoration works, which was then recorded in the HBIM model, thus permitting time and cost control.

Assessing craniofacial growth and form without landmarks: A new automatic approach based on spectral methods.

We present a novel method for the morphometric analysis of series of 3D shapes, and demonstrate its relevance for the detection and quantification of two craniofacial anomalies: trigonocephaly and metopic ridges, using CT-scans of young children. Our approach is fully automatic, and does not rely on manual landmark placement and annotations. Our approach furthermore allows to differentiate shape classes, enabling successful differential diagnosis between trigonocephaly and metopic ridges, two related conditions characterized by triangular foreheads. These results were obtained using recent developments in automatic nonrigid 3D shape correspondence methods and specifically spectral approaches based on the functional map framework. Our method can capture local changes in geometric structure, in contrast to methods based, for instance, on global shape descriptors. As such, our approach allows to perform automatic shape classification and provides visual feedback on shape regions associated with different classes of deformations. The flexibility and generality of our approach paves the way for the application of spectral methods in quantitative medicine.

Automatic 3D CAD models reconstruction from 2D orthographic drawings

This paper introduces a two-stage approach that automatically generates 3D CAD models from 2D orthographic drawings. First, a pattern-matching algorithm is proposed to reconstruct a network of 3D edges by matching 2D edge features extracted from the multiple views of the 2D drawing. Second, starting from the resulting 3D wireframe and generated graph, a loop detection algorithm allows identifying possible loops of faces. Then, a clustering algorithm recognizes the faces from the set of detected loops. The reconstruction ends while trimming and stitching the faces to get a watertight ready-to-use 3D CAD model. This approach has been validated on a public dataset composed of several thousands of 3D shapes, and it achieved 99.59% of well-reconstructed models in F-score.

Augmented Reality Visualization and Interaction for COVID-19 CT-Scan NN Automated Segmentation: A Validation Study

Although medical imaging technology has persisted in evolving over the last decades, the techniques and technologies used for analytical and visualisation purposes have remained constant. Manual or semi-automatic segmentation is, in many cases, complicated. It requires the intervention of a specialist and is time-consuming, especially during the Coronavirus disease (COVID-19) pandemic, which has had devastating medical and economic consequences. Processing and visualising medical images with advanced techniques represent medical professionals’ breakthroughs. This paper studies how augmented reality (AR) and artificial intelligence (AI) can transform medical practice during COVID-19 and post-COVID-19 pandemic. Here we report an augmented reality visualisation and interaction platform; it covers the whole process from uploading chest Ct-scan images to automatic segmentation-based deep learning, 3D reconstruction, 3D visualisation, and manipulation. AR provides a more realistic 3D visualisation system, allowing doctors to effectively interact with the generated 3D model of segmented lungs and COVID-19 lesions. We use the U-Net Neural Network (NN) for automated segmentation. The statistical measures obtained using the Dice score, pixel accuracy, sensitivity, G-mean, and specificity are 0.749, 0.949, 0.956, 0.955, and 0.954, respectively. The user-friendliness and usability are objectified by a formal user study that compared our augmented reality-assisted design to the standard diagnosis setup. One hundred and six doctors and medical students, including eight senior medical lecturers, volunteered to assess our platform. The platform could be used as an aid diagnosis tool to identify and analyse the COVID-19 infectious or as a training tool for residents and medical students. The prototype can be extended to other pulmonary pathologies.

Automatic 3D joint erosion detection for the diagnosis and monitoring of rheumatoid arthritis using hand HR-pQCT images.

Rheumatoid arthritis (RA) is a chronic inflammatory disease. It leads to bone erosion in joints and other complications, which severely affect patients' quality of life. To accurately diagnose and monitor the progression of RA, quantitative imaging and analysis tools are desirable. High-resolution peripheral quantitative computed tomography (HR-pQCT) is such a promising tool for monitoring disease progression in RA. However, automatic erosion detection tools using HR-pQCT images are not yet available. Inspired by the consensus among radiologists on the erosions in HR-pQCT images, in this paper we define erosion as the significant concave regions on the cortical layer, and develop a model-based 3D automatic erosion detection method. It mainly consists of two steps: constructing closed cortical surface, and detecting erosion regions on the surface. In the first step, we propose an initialization-robust region competition methods for joint segmentation, and then fill the surface gaps by using joint bone separation and curvature-based surface alignment. In the second step, we analyze the curvature information of each voxel, and then aggregate the candidate voxels into concave surface regions and use the shape information of the regions to detect the erosions. We perform qualitative assessments of the new method using 59 well-annotated joint volumes. Our method has shown satisfactory and consistent performance compared with the annotations provided by medical experts.

Improved automatic identification of isolated rapid eye movement sleep behavior disorder with a 3D time-of-flight camera.

Automatic 3D video analysis of the lower body during rapid eye movement (REM) sleep has been recently proposed as a novel tool for identifying people with isolated REM sleep behavior disorder (iRBD), but, so far, it has not been validated on unseen subjects. This study aims at validating this technology in a large cohort and at improving its performances by also including an analysis of movements in the head, hands and upper body. Fifty-three people with iRBD and 128 people without RBD (of whom 89 had sleep disorders considered RBD differential diagnoses) were included in the study. An automatic algorithm identified movements from 3D videos during REM sleep in four regions of interest (ROIs): head, hands, upper body and lower body. The movements were divided into categories according to duration: short (0.1-2 s), medium (2-15 s) and long (15-300 s). For each ROI and duration range, features were obtained from the identified movements. Logistic regression models using as predictors the features from one single ROI or a combination of ROIs were trained and tested in a 10-runs 10-fold cross-validation scheme on the task of differentiating people with iRBD from people without RBD. The best differentiation was achieved using short movements in all four ROIs (test accuracy 0.866 ± 0.007, test F1 score = 0.783 ± 0.010). Single group analyses showed that people with iRBD were distinguished successfully from subjects with RBD differential diagnoses. Automatic 3D video analysis might be implemented in clinical routine as a supportive screening tool for identifying people with RBD.

A Light, 3D UNet-based Architecture for Fully Automatic Segmentation of Prostate Lesions from T2-MRI Images.

Prostate magnetic resonance imaging (MRI) has been recently integrated into the pathway of diagnosis of prostate cancer (PCa). However, the lack of an optimal contrast-to-noise ratio hinders automatic recognition of suspicious lesions, thus developing a solution for proper delimitation of the tumour and its separation from the healthy parenchyma, which is of primordial importance. As a solution to this unmet medical need, we aimed to develop a decision support system based on artificial intelligence, which automatically segments the prostate and any suspect area from the 3D MRI images. We assessed retrospective data from all patients diagnosed with PCa by MRI-US fusion prostate biopsy, who underwent prostate MRI in our department due to a clinical or biochemical suspicion of PCa (n=33). All examinations were performed using a 1.5 Tesla MRI scanner. All images were reviewed by two radiologists, who performed manual segmentation of the prostate and all lesions. A total of 145 augmented datasets were generated. The performance of our fully automated end-to-end segmentation model based on a 3D UNet architecture and trained in two learning scenarios (on 14 or 28 patient datasets) was evaluated by two loss functions. Our model had an accuracy of over 90% for automatic segmentation of prostate and PCa nodules, as compared to manual segmentation. We have shown low complexity networks, UNet architecture with less than five layers, as feasible and to show good performance for automatic 3D MRI image segmentation. A larger training dataset could further improve the results. Therefore, herein, we propose a less complex network, a slim 3D UNet with superior performance, being faster than the original five-layer UNet architecture.

Longitudinal volumetric assessment of inflammatory arthritis via photoacoustic imaging and Doppler ultrasound imaging

Aiming at clinical translation, we developed an automatic 3D imaging system combining the emerging photoacoustic imaging with conventional Doppler ultrasound for detecting inflammatory arthritis. This system was built with a GE HealthCare (GEHC) Vivid™ E95 ultrasound system and a Universal Robot UR3 robotic arm. In this work, the performance of this system was examined with a longitudinal study utilizing a clinically relevant adjuvant induced arthritis (AIA) murine model. After adjuvant injection, daily imaging of the rat ankle joints was conducted until joint inflammation was obvious based on visual inspection. Processed imaging results and statistical analyses indicated that both the hyperemia (enhanced blood volume) detected by photoacoustic imaging and the enhanced blood flow detected by Doppler ultrasound reflected the progress of joint inflammation. However, photoacoustic imaging, by leveraging the highly sensitive optical contrast, detected inflammation earlier than Doppler ultrasound, and also showed changes that are more statistically significant. This side-by-side comparison between photoacoustic imaging and Doppler ultrasound using the same commercial grade GEHC ultrasound machine demonstrates the advantage and potential value of the emerging photoacoustic imaging for rheumatology clinical care of arthritis.

Research on a 3D Seismic Horizon Automatic-Tracking Method Based on Corrugated Global Diffusion

The core challenges to automatic full-horizon tracking are how to establish a potential local connection relationship between the horizon points, conduct accurate global diffusion in a three-dimensional space, and finally, how to form a complex horizon surface. The existing attribute-based horizon-tracking methods based on waveform similarity, dip guidance, and RGT (relative geological time) can not solve the problems of local connection and global diffusion at the same time. In view of this challenge, this paper proposes an automatic 3D seismic horizon-tracking method based on global corrugated diffusion, which can completely integrate local connection and global diffusion so that all horizons in the whole data volume can be interpreted simultaneously. For the problem of local horizon-point connection, this paper uses the correlation between seismic trace pairs based on DTW (dynamic time warping) correlation to mine the connection mode between horizon points. For the global diffusion problem, this paper proposes the realization of global modeling based on the relationship between seismic samples, constructing a complex 3D horizon through a central ripple-diffusion process. The example shows that the horizon tracked by this method well reflects the original stratum occurrence and stratum-contact relationship, retains the structural details, accurately reflects the structural shape, and realizes automatic tracking across faults.

Deep learning for detection and 3D segmentation of maxillofacial bone lesions in cone beam CT.

To develop an automated deep-learning algorithm for detection and 3D segmentation of incidental bone lesions in maxillofacial CBCT scans. The dataset included 82 cone beam CT (CBCT) scans, 41 with histologically confirmed benign bone lesions (BL) and 41 control scans (without lesions), obtained using three CBCT devices with diverse imaging protocols. Lesions were marked in all axial slices by experienced maxillofacial radiologists. All cases were divided into sub-datasets: training (20,214 axial images), validation (4530 axial images), and testing (6795 axial images). A Mask-RCNN algorithm segmented the bone lesions in each axial slice. Analysis of sequential slices was used for improving the Mask-RCNN performance and classifying each CBCT scan as containing bone lesions or not. Finally, the algorithm generated 3D segmentations of the lesions and calculated their volumes. The algorithm correctly classified all CBCT cases as containing bone lesions or not, with an accuracy of 100%. The algorithm detected the bone lesion in axial images with high sensitivity (95.9%) and high precision (98.9%) with an average dice coefficient of 83.5%. The developed algorithm detected and segmented bone lesions in CBCT scans with high accuracy and may serve as a computerized tool for detecting incidental bone lesions in CBCT imaging. Our novel deep-learning algorithm detects incidental hypodense bone lesions in cone beam CT scans, using various imaging devices and protocols. This algorithm may reduce patients' morbidity and mortality, particularly since currently, cone beam CT interpretation is not always preformed. • A deep learning algorithm was developed for automatic detection and 3D segmentation of various maxillofacial bone lesions in CBCT scans, irrespective of the CBCT device or the scanning protocol. • The developed algorithm can detect incidental jaw lesions with high accuracy, generates a 3D segmentation of the lesion, and calculates the lesion volume.

Application of BIM to Rebar Modeling of a Variable Section Column

A planar construction drawing is usually used to guide the construction of reinforced concrete structures in the construction industry. Due to a lack of effective technical methods, the accuracy of rebar engineering lofting is insufficient. The rejection rate of rebar is high, which results in high processing costs. Under the background of intelligent construction and BIM technology, the prefabricated factory adopts an automatic production mode to concentrate on steel bar processing, which improves production quality and processing efficiency and reduces production costs. Based on Revit secondary development, using the C# programming language, combined with the Revit API development interface, an automatic 3D modeling plugin for a reinforced concrete variable cross-section column is developed. The BIM model of steel bars is quickly generated, and the engineering quantity of steel bars is automatically calculated, which saves time for the designer to build the BIM model. It is conducive to the automatic assembly and intelligent construction of reinforced concrete engineering, which promotes the information and intelligent development of reinforced concrete structure construction.

GEMVS: a novel approach for automatic 3D reconstruction from uncalibrated multi-view Google Earth images using multi-view stereo and projective to metric 3D homography transformation

ABSTRACT This paper proposes a novel approach for automatic 3D surface reconstruction from uncalibrated and multi-view Google Earth images by using a multi-view stereo method and 3D projective to metric transformation. Without the Rational Polynomial Coefficients, it is impossible to obtain the metric reconstruction of the 3D surface from multi-view satellite images. We solve the uncalibrated multi-view satellite image problem by employing a multi-view stereo vision technique followed by a projective to metric transformation. The virtual pose parameters of the satellite images are obtained by using COLMAP, and the virtual 3D projective reconstruction is done by using EnSoft3D. For projective to metric transformation, we propose to employ 3D homography transformation. Eight 3D correspondence pairs on the viewing frustums between the virtual reference camera and the ideal nadir camera are used to derive a 3D homography matrix. Using the 3D homography matrix, we finally obtain the metric reconstruction of 3D surface up to an unknown height scale in a reference coordinate system of the Google Earth desktop software. Experiments are done in several world locations on Google Earth including building and vegetation areas. Reconstruction error analysis with the Data Fusion Contest 19 dataset is also presented. The average of MAE and RMSE of five tile regions in the dataset are 1.596 m and 2.083 m, respectively.

Deep learning based automatic segmentation of the placenta and uterine cavity on prenatal MR images.

Magnetic resonance imaging (MRI) has potential benefits in understanding fetal and placental complications in pregnancy. An accurate segmentation of the uterine cavity and placenta can help facilitate fast and automated analyses of placenta accreta spectrum and other pregnancy complications. In this study, we trained a deep neural network for fully automatic segmentation of the uterine cavity and placenta from MR images of pregnant women with and without placental abnormalities. The two datasets were axial MRI data of 241 pregnant women, among whom, 101 patients also had sagittal MRI data. Our trained model was able to perform fully automatic 3D segmentation of MR image volumes and achieved an average Dice similarity coefficient (DSC) of 92% for uterine cavity and of 82% for placenta on the sagittal dataset and an average DSC of 87% for uterine cavity and of 82% for placenta on the axial dataset. Use of our automatic segmentation method is the first step in designing an analytics tool for to assess the risk of pregnant women with placenta accreta spectrum.

SEGMENTATION OF SPINAL SUBARACHNOID LUMEN WITH 3D ATTENTION U-NET

Phase Contrast Magnetic Resonance Image (PC-MRI) is an emerging noninvasive technique that contains pulsatile information by measuring the parameters of cerebrospinal fluid (CSF) flow. As CSF flow quantities are measured from the selected region on the images, the accuracy in the identification of the interested region is the most essential, and the examination requires a lot of time and experience to analyze and for accurate CSF flow assessment. In this study, a three-dimensional (3D)-Unet architecture, including pulsatile flow data as the third dimension, is proposed to address the issue. The dataset contains 2176 phase and rephase images from 57 slabs of 39 3-tesla PC-MRI subjects collected from the lower thoracic levels of control and Idiopathic Scoliosis (IS) patients. The procedure starts with labeling the CSF containing spaces in the spinal canal. In the preprocessing step, unequal cardiac cycle images (i.e., frame) and the numbers of MRIs in cases are adjusted by interpolation to align the temporal dimension of the dataset to an equal size. The five-fold cross-validation procedure is used to evaluate the 3D Attention-U-Net model after training and achieved an average weighted performance of 97% precision, 95% recall, 98% F1 score, and 95% area under curve. The success of the model is also measured using the CSF flow waveform quantities as well. The mean flow rates through the labeled and predicted CSF lumens have a significant correlation coefficient of 0.96, and the peak CSF flow rates have a coefficient of 0.65. To our knowledge, this is the first fully automatic 3D deep learning architecture implementation to segment spinal CSF-containing spaces that utilizes both spatial and pulsatile information in PC-MRI data. We expect that our work will attract future research on the use of PC-MRI temporal information for training deep models.

360 Assessment of Pedicle Screw Placement Accuracy Utilizing a Novel Augmented Reality and Artificial Intelligence Surgical Guidance System

INTRODUCTION: The integration of artificial intelligence (AI) and augmented reality (AR) technologies in spine navigation has the potential to minimize pedicle screw misplacement, decrease radiation exposure, and improve procedural efficiency, addressing some of the challenges associated with spinal instrumentation. METHODS: Sixty lumbosacral pedicle screws were placed by 4 Orthopedics surgeons in 6 cadavers using the AR/AI surgical navigation system. Two CT intraoperative scans (before and after screw placement) of the cadaveric specimens were obtained for the study to measure the 3D positional and angular discrepancies between virtual and real screws. The validation procedure consisted of 3 steps: (1) automatic registration and 3D coordinate transformation, where the preop CT scan was aligned with the postop scan to determine the virtual screw 3D positions and orientations in postop scan; (2) analysis of the postop scan to determine the real screw 3D positions and orientations as they appeared in the scan volume; (3) automatic comparison between the 3D positions and orientations of the virtual and real screws. RESULTS: A one-sample t-test statistical analysis yielded a mean positional error of 2.16 mm ± 1.00 mm (99% CI: 1.81-2.49 mm), and a mean angular error of 1.49± 0.73° (99% CI: 1.25-1.74°). These results were independent of anatomical level, side, and surgeon. CONCLUSIONS: This study indicates that this AR/AI surgical guidance system provides mean positional and angular errors lower than 3.0 mm and 3.0° respectively, thus complying with FDA-required regulatory acceptance criteria. The system was FDA-cleared as a US 510k Orthopedic Stereotaxic Instrument.

HyM3D: A hybrid method for the automatic 3D reconstruction of a defective cranial vault

Background and objectiveThe ability to accomplish a consistent restoration of a missing or deformed anatomical area is a fundamental step for defining a custom implant, especially in the maxillofacial and cranial reconstruction where the aesthetical aspect is crucial for a successful surgical outcome. At the same time, this task is also the most difficult, time-consuming, and complicated across the whole reconstruction process. This is mostly due to the high geometric complexity of the anatomical structures, insufficient references, and significant interindividual anatomical heterogeneity. Numerous solutions, specifically for the neurocranium, have been put forward in the scientific literature to address the reconstruction issue, but none of them has yet been persuasive enough to guarantee an easily automatable approach with a consistent shape reconstruction. MethodsThis work aims to present a novel reconstruction method (named HyM3D) for the automatic restoration of the exocranial surface by ensuring both the symmetry of the resulting skull and the continuity between the reconstructive patch and the surrounding bone. To achieve this goal, the strengths of the Template-based methods are exploited to provide knowledge of the missing or deformed region and to guide a subsequent Surface Interpolation-based algorithm. HyM3D is an improved version of a methodology presented by the authors in a previous publication for the restoration of unilateral defects. Differently from the first version, the novel procedure applies to all kinds of cranial defects, whether they are unilateral or not. ResultsThe presented method has been tested on several test cases, both synthetic and real, and the results show that it is reliable and trustworthy, providing a consistent outcome with no user intervention even when dealing with complex defects. ConclusionsHyM3D method proved to be a valid alternative to the existing approaches for the digital reconstruction of a defective cranial vault; furthermore, with respect to the current alternatives, it demands less user interaction since the method is landmarks-independent and does not require any patch adaptation.

Online 2D High-pH and Low-pH Reversed-Phase Nano-LC-MS/MS System for Deep Proteome Analysis.

In bottom-up proteomic profiling, the complexity of proteome composition and wide dynamic range has created challenges on the limited number of protein identification and proteome coverage, especially in sample input-limited nanoflow (nano) LC-MS/MS analysis. Herein, we developed a fully automatic online 2D nano-LC-MS/MS system using both high-pH and low-pH reverse phase (RP) LCs on a single LC instrument toward comprehensive proteomics analysis. Compared to conventional microflow 2D-LC, the high-pH RP trapping column demonstrated a low sample requirement of cellular protein digest at the μg level with good fractionation resolution of >90% peptides in a single fraction. Compared to the offline 2D RP-RP nano-LC-QTOF using C18-HPLC column and C18-Stage Tip, and 1D nano-LC-QTOF system, superior coverage was observed on the higher number of identified protein groups/unique peptides by 1.35-/1.68-, 1.46-/1.75-, and 3.21-/4.35-fold, respectively, using an online 2D RP-RP nano-LC-QTOF mass spectrometer. On the evolution of quantitation performance, the online 2D high-/low-pH RP data-independent acquisition (DIA) showed a higher reproducibility in protein groups intensity (R2 > 0.977) and more quantified proteins than that obtained using the offline 2D high-/low-pH RP DIA approach. Using an advanced Orbitrap Exploris 480 mass spectrometer, ∼1.9-fold higher proteome coverage was also observed in our 2D online RP-RP system (6039 protein groups) compared to the 1D nano-LC system (3133 protein groups). In summary, the online 2D nano-LC-MS/MS platform can be a sensitive and robust approach compatible with conventional nano-LC instruments for deep proteome coverage of trace amounts of samples.

Using a New Deep Learning Method for 3D Cephalometry in Patients With Cleft Lip and Palate.

Deep learning algorithms based on automatic 3-dimensional (D) cephalometric marking points about people without craniomaxillofacial deformities has achieved good results. However, there has been no previous report about cleft lip and palate. The purpose of this study is to apply a new deep learning method based on a 3D point cloud graph convolutional neural network to predict and locate landmarks in patients with cleft lip and palate based on the relationships between points. The authors used the PointNet++ model to investigate the automatic 3D cephalometric marking points. And the mean distance error of the center coordinate position and the success detection rate (SDR) were used to evaluate the accuracy of systematic labeling. A total of 150 patients were enrolled. The mean distance error for all 27 landmarks was 1.33mm, and 9 landmarks (30%) showed SDRs at 2mm over 90%, and 3 landmarks (35%) showed SDRs at 2mm under 70%. The automatic 3D cephalometric marking points take 16 seconds per dataset. In summary, our training sets were derived from the cleft lip with/without palate computed tomography to achieve accurate results. The 3D cephalometry system based on the graph convolutional neural network algorithm may be suitable for 3D cephalometry system in cleft lip and palate cases. More accurate results may be obtained if the cleft lip and palate training set is expanded in the future.