3D Image Data Research Articles

Supervised learning techniques for dairy cattle body weight prediction from 3D digital images.

Introduction: The use of automation and sensor-based systems in livestock production allows monitoring of individual cows in real-time and provides the possibility of early warning systems to take necessary management actions against possible anomalies. Among the different RT monitoring parameters, body weight (BW) plays an important role in tracking the productivity and health status. Methods: In this study, various supervised learning techniques representing different families of methods in the machine learning space were implemented and compared for performance in the prediction of body weight from 3D image data in dairy cows. A total of 83,011 records of contour data from 3D images and body weight measurements taken from a total of 914 Danish Holstein and Jersey cows from 3 different herds were used for the predictions. Various metrics including Pearson's correlation coefficient (r), the root mean squared error (RMSE), and the mean absolute percentage error (MAPE) were used for robust evaluation of the various supervised techniques and to facilitate comparison with other studies. Prediction was undertaken separately within each breed and subsequently in a combined multi-breed dataset. Results and discussion: Despite differences in predictive performance across the different supervised learning techniques and datasets (breeds), our results indicate reasonable prediction accuracies with mean correlation coefficient (r) as high as 0.94 and MAPE and RMSE as low as 4.0 % and 33.0 (kg), respectively. In comparison to the within-breed analyses (Jersey, Holstein), prediction using the combined multi-breed data set resulted in higher predictive performance in terms of high correlation coefficient and low MAPE. Additional tests showed that the improvement in predictive performance is mainly due to increase in data size from combining data rather than the multi-breed nature of the combined data. Of the different supervised learning techniques implemented, the tree-based group of supervised learning techniques (Catboost, AdaBoost, random forest) resulted in the highest prediction performance in all the metrics used to evaluate technique performance. Reported prediction errors in our study (RMSE and MAPE) are one of the lowest in the literature for prediction of BW using image data in dairy cattle, highlighting the promising predictive value of contour data from 3D images for BW in dairy cows under commercial farm conditions.

Shape-Aware 3D Small Vessel Segmentation with Local Contrast Guided Attention.

The automated segmentation and analysis of small vessels from in vivo imaging data is an important task for many clinical applications. While current filtering and learning methods have achieved good performance on the segmentation of large vessels, they are sub-optimal for small vessel detection due to their apparent geometric irregularity and weak contrast given the relatively limited resolution of existing imaging techniques. In addition, for supervised learning approaches, the acquisition of accurate pixel-wise annotations in these small vascular regions heavily relies on skilled experts. In this work, we propose a novel self-supervised network to tackle these challenges and improve the detection of small vessels from 3D imaging data. First, our network maximizes a novel shape-aware flux-based measure to enhance the estimation of small vasculature with non-circular and irregular appearances. Then, we develop novel local contrast guided attention(LCA) and enhancement(LCE) modules to boost the vesselness responses of vascular regions of low contrast. In our experiments, we compare with four filtering-based methods and a state-of-the-art self-supervised deep learning method in multiple 3D datasets to demonstrate that our method achieves significant improvement in all datasets. Further analysis and ablation studies have also been performed to assess the contributions of various modules to the improved performance in 3D small vessel segmentation. Our code is available at https://github.com/dengchihwei/LCNetVesselSeg.

Pore scale investigation of unsaturated granular soil behaviour by means ofin situCT experiments

With continuing evolution of imaging techniques from medical applications and materials science, non-destructive imaging experiments have also become an important method to investigate soil specimens. Amongst other methods, computed tomography (CT) has developed to a tool to visualise and better understand the microstructure of different soils based on 3D image data. Furthermore, the acquisition of a temporal series of CT images allows to study processes in soils on the microscale, e. g., during mechanical loading. In order to study the hydraulic and mechanical behaviour of unsaturated granular soils, we combine different custom-built miniaturised experimental set-ups with geomechanics background with computed tomography, yielding so-calledin situCT experiments. By means of image reconstruction and further image analysis based on segmented CT images acquired during different hydraulic and mechanical experiments, we study the drainage and imbibition process as well as the shear process of unsaturated sand and glass bead specimens on the pore or grain scale. The analysis of data on the microscopic level, including the phase distribution, interfacial areas, contact lines as well as radii of curvature of capillary menisci, allows to obtain insights into the macroscopic water retention behaviour and shear behaviour of granular soils.

Two- and Three-Dimensional Benchmarks for Particle Detection from an Industrial Rotary Kiln Combustion Chamber Based on Light-Field-Camera Recording

This paper describes a benchmark dataset for the detection of fuel particles in 2D and 3D image data in a rotary kiln combustion chamber. The specific challenges of detecting the small particles under demanding environmental conditions allows for the performance of existing and new particle detection techniques to be evaluated. The data set includes a classification of burning and non-burning particles, which can be in the air but also on the rotary kiln wall. The light-field camera used for data generation offers the potential to develop and objectively evaluate new advanced particle detection methods due to the additional 3D information. Besides explanations of the data set and the contained ground truth, an evaluation procedure of the particle detection based on the ground truth and results for an own particle detection procedure for the data set are presented.

Fusion of Complementary 2D and 3D Mesostructural Datasets Using Generative Adversarial Networks

AbstractModelling the impact of a material's mesostructure on device level performance typically requires access to 3D image data containing all the relevant information to define the geometry of the simulation domain. This image data must include sufficient contrast between phases, be of high enough resolution to capture the key details, but also have a large enough 3D field‐of‐view to be representative of the material in general. It is rarely possible to obtain data with all of these properties from a single imaging technique. In this paper, we present a method for combining information from pairs of distinct but complementary imaging techniques in order to accurately reconstruct the desired multi‐phase, high‐resolution, representative, 3D images. Specifically, the deep convolutional generative adversarial networks to implement super‐resolution, style‐transfer and dimensionality expansion. It is believed that this data‐driven approach is superior to previously reported statistical material reconstruction methods, both in terms of its fidelity and ease of use. Furthermore, much of the data required to train this algorithm already exists in the literature, waiting to be combined. As such, our open‐source code could precipitate a step change in the materials sciences by generating the desired high quality image volumes necessary to simulate behaviour at the mesoscale.

3D CNN-Residual Neural Network Based Multimodal Medical Image Classification

Multimodal medical imaging has become incredibly common in the area of biomedical imaging. Medical image classification has been used to extract useful data from multimodality medical image data. Magnetic resonance imaging (MRI) and Computed tomography (CT) are some of the imaging methods. Different imaging technologies provide different imaging information for the same part. Traditional ways of illness classification are effective, but in today's environment, 3D images are used to identify diseases. In comparison to 1D and 2D images, 3D images have a very clear vision. The proposed method uses 3D Residual Convolutional Neural Network (CNN ResNet) for the 3D image classification. Various methods are available for classifying the disease, like cluster, KNN, and ANN. Traditional techniques are not trained to classify 3D images, so an advanced approach is introduced in the proposed method to predict the 3D images. Initially, the multimodal 2D medical image data is taken. This 2D input image is turned into 3D image data because 3D images give more information than the 2D image data. Then the 3D CT and MRI images are fused and using the Guided filtering, and the combined image is filtered for the further process. The fused image is then augmented. Finally, this fused image is fed to 3DCNN ResNet for classification purposes. The 3DCNN ResNet classifies the image data and produces the output as five different stages of the disease. The proposed method achieves 98% of accuracy. Thus the designed modal has predicted the stage of the disease in an effective manner.

A New Approach to the Quantitative Analysis of Bone Surface Modifications: the Bowser Road Mastodon and Implications for the Data to Understand Human-Megafauna Interactions in North America

Toward the end of the Pleistocene, the world experienced a mass extinction of megafauna. In North America these included its proboscideans—the mammoths and mastodons. Researchers in conservation biology, paleontology, and archaeology have debated the role played by human predation in these extinctions. They point to traces of human butchery, such as cut marks and other bone surface modifications (BSM), as evidence of human-animal interactions—including predation and scavenging, between early Americans and proboscideans. However, others have challenged the validity of the butchery evidence observed on several proboscidean assemblages, largely due to questions of qualitative determination of the agent responsible for creating BSM. This study employs a statistical technique that relies on three-dimensional (3D) imaging data and 3D geometric morphometrics to determine the origin of the BSM observed on the skeletal remains of the Bowser Road mastodon (BR mastodon), excavated in Middletown, New York. These techniques have been shown to have high accuracy in identifying and distinguishing among different types of BSM. To better characterize the BSM on the BR mastodon, we compared them quantitatively to experimental BSM resulting from a stone tool chopping experiment using “Arnold,” the force-calibrated chopper. This study suggests that BSM on the BR mastodon are not consistent with the BSM generated by the experimental chopper. Future controlled experiments will compare other types of BSM to those on BR. This research contributes to continued efforts to decrease the uncertainty surrounding human-megafauna associations at the level of the archaeological site and faunal assemblage—specifically that of the BR mastodon assemblage. Consequently, we also contribute to the dialogue surrounding the character of the human-animal interactions between early Americans and Late Pleistocene megafauna, and the role of human foraging behavior in the latter’s extinction.

The impact of organ motion and the appliance of mitigation strategies on the effectiveness of hypoxia-guided proton therapy for non-small cell lung cancer

Background and PurposeTo investigate the impact of organ motion on hypoxia-guided proton therapy treatments for non-small cell lung cancer (NSCLC) patients. Materials and MethodsHypoxia PET and 4D imaging data of six NSCLC patients were used to simulate hypoxia-guided proton therapy with different motion mitigation strategies including rescanning, breath-hold, respiratory gating and tumour tracking. Motion-induced dose degradation was estimated for treatment plans with dose painting of hypoxic tumour sub-volumes at escalated dose levels. Tumour control probability (TCP) and dosimetry indices were assessed to weigh the clinical benefit of dose escalation and motion mitigation. In addition, the difference in normal tissue complication probability (NTCP) between escalated proton and photon VMAT treatments has been assessed. ResultsMotion-induced dose degradation was found for target coverage (CTV V95% up to −4%) and quality of the dose-escalation-by-contour (QRMS up to 6%) as a function of motion amplitude and amount of dose escalation. The TCP benefit coming from dose escalation (+4–13%) outweighs the motion-induced losses (<2%). Significant average NTCP reductions of dose-escalated proton plans were found for lungs (−14%), oesophagus (−10%) and heart (−16%) compared to conventional VMAT plans. The best plan dosimetry was obtained with breath hold and respiratory gating with rescanning. ConclusionNSCLC affected by hypoxia appears to be a prime target for proton therapy which, by dose-escalation, allows to mitigate hypoxia-induced radio-resistance despite the sensitivity to organ motion. Furthermore, substantial reduction in normal tissue toxicity can be expected compared to conventional VMAT. Accessibility and standardization of hypoxia imaging and clinical trials are necessary to confirm these findings in a clinical setting.

FFT-based homogenization at finite strains using composite boxels (ComBo)

Computational homogenization is the gold standard for concurrent multi-scale simulations (e.g., FE2) in scale-bridging applications. Often the simulations are based on experimental and synthetic material microstructures represented by high-resolution 3D image data. The computational complexity of simulations operating on such voxel data is distinct. The inability of voxelized 3D geometries to capture smooth material interfaces accurately, along with the necessity for complexity reduction, has motivated a special local coarse-graining technique called composite voxels (Kabel et al. Comput Methods Appl Mech Eng 294: 168–188, 2015). They condense multiple fine-scale voxels into a single voxel, whose constitutive model is derived from the laminate theory. Our contribution generalizes composite voxels towards composite boxels (ComBo) that are non-equiaxed, a feature that can pay off for materials with a preferred direction such as pseudo-uni-directional fiber composites. A novel image-based normal detection algorithm is devised which (i) allows for boxels in the firsts place and (ii) reduces the error in the phase-averaged stresses by around 30% against the orientation cf. Kabel et al. (Comput Methods Appl Mech Eng 294: 168–188, 2015) even for equiaxed voxels. Further, the use of ComBo for finite strain simulations is studied in detail. An efficient and robust implementation is proposed, featuring an essential selective back-projection algorithm preventing physically inadmissible states. Various examples show the efficiency of ComBo against the original proposal by Kabel et al. (Comput Methods Appl Mech Eng 294: 168–188, 2015) and the proposed algorithmic enhancements for nonlinear mechanical problems. The general usability is emphasized by examining various Fast Fourier Transform (FFT) based solvers, including a detailed description of the Doubly-Fine Material Grid (DFMG) for finite strains. All of the studied schemes benefit from the ComBo discretization.

A novel densely connected neural network for proton exchange membrane fuel cell fault diagnosis

It is of great significance to perform proton exchange membrane fuel cell (PEMFC) fault diagnosis and take action timely to mitigate or even eliminate the faults, which can strengthen PEMFC reliability and durability. In previous studies, cell voltage is extensively used for PEMFC fault diagnosis. However, there exists similar cell voltage drop phenomenon as different PEMFC faults occur, especially for faults like flooding and air starvation having extremely similar voltage dynamic variation, which makes it difficult to capture the features sensitive to faults. Moreover, cell voltages collected from different MEAs follow different distributions even in the same operation condition, which challenges the diagnosis consistency of fault diagnosis methods. In this paper, in order to break through the hindrances, a novel densely connected neural network codenamed Inc-DenseNet is proposed for PEMFC fault diagnosis, which integrates advantages of InceptionNet and DenseNet to extract more specific and robust features from cell voltage. In the analysis, the collected PEMFC voltage signal is transformed into 2D image data, which is then used to train the Inc-DenseNet. Results demonstrate that with the trained Inc-DenseNet, the diagnostic accuracy for four PEMFC states of health (normal, flooding, dehydration, air starvation) can reach 95.3%, especially for flooding and air starvation. In addition, by using the voltage datasets collected from two different MEAs, the generalization capacity of the Inc-DenseNet is proved. With the findings, the proposed network Inc-DenseNet can not only achieve high-precision fault diagnosis, but also has a high computing efficiency, which makes it promising in real-time PEMFC fault diagnosis in the future.

X23D—Intraoperative 3D Lumbar Spine Shape Reconstruction Based on Sparse Multi-View X-ray Data

Visual assessment based on intraoperative 2D X-rays remains the predominant aid for intraoperative decision-making, surgical guidance, and error prevention. However, correctly assessing the 3D shape of complex anatomies, such as the spine, based on planar fluoroscopic images remains a challenge even for experienced surgeons. This work proposes a novel deep learning-based method to intraoperatively estimate the 3D shape of patients’ lumbar vertebrae directly from sparse, multi-view X-ray data. High-quality and accurate 3D reconstructions were achieved with a learned multi-view stereo machine approach capable of incorporating the X-ray calibration parameters in the neural network. This strategy allowed a priori knowledge of the spinal shape to be acquired while preserving patient specificity and achieving a higher accuracy compared to the state of the art. Our method was trained and evaluated on 17,420 fluoroscopy images that were digitally reconstructed from the public CTSpine1K dataset. As evaluated by unseen data, we achieved an 88% average F1 score and a 71% surface score. Furthermore, by utilizing the calibration parameters of the input X-rays, our method outperformed a counterpart method in the state of the art by 22% in terms of surface score. This increase in accuracy opens new possibilities for surgical navigation and intraoperative decision-making solely based on intraoperative data, especially in surgical applications where the acquisition of 3D image data is not part of the standard clinical workflow.

EmbedSeg: Embedding-based Instance Segmentation for Biomedical Microscopy Data.

Automatic detection and segmentation of biological objects in 2D and 3D image data is central for countless biomedical research questions to be answered. While many existing computational methods are used to reduce manual labeling time, there is still a huge demand for further quality improvements of automated solutions. In the natural image domain, spatial embedding-based instance segmentation methods are known to yield high-quality results, but their utility to biomedical data is largely unexplored. Here we introduce EmbedSeg, an embedding-based instance segmentation method designed to segment instances of desired objects visible in 2D or 3D biomedical image data. We apply our method to four 2D and seven 3D benchmark datasets, showing that we either match or outperform existing state-of-the-art methods. While the 2D datasets and three of the 3D datasets are well known, we have created the required training data for four new 3D datasets, which we make publicly available online. Next to performance, also usability is important for a method to be useful. Hence, EmbedSeg is fully open source (https://github.com/juglab/EmbedSeg), offering (i)tutorial notebooks to train EmbedSeg models and use them to segment object instances in new data, and (ii)a napari plugin that can also be used for training and segmentation without requiring any programming experience. We believe that this renders EmbedSeg accessible to virtually everyone who requires high-quality instance segmentations in 2D or 3D biomedical image data.

Spinal Cord Boundary Conditions Affect Brain Tissue Strains in Impact Simulations.

Brain and spinal cord injuries have devastating consequences on quality of life but are challenging to assess experimentally due to the traumatic nature of such injuries. Finite element human body models (HBM) have been developed to investigate injury but are limited by a lack of biofidelic spinal cord implementation. In many HBM, brain models terminate with a fixed boundary condition at the brain stem. The goals of this study were to implement a comprehensive representation of the spinal cord into a contemporary head and neck HBM, and quantify the effect of the spinal cord on brain deformation during simulated impacts. Spinal cord tissue geometries were developed, based on 3D medical imaging and literature data, meshed, and implemented into the GHBMC 50th percentile male model. The model was evaluated in frontal, lateral, rear, and oblique impact conditions, and the resulting maximum principal strains in the brain tissue were compared, with and without the spinal cord. A new cumulative strain curve metric was proposed to quantify brain strain distribution. Presence of the spinal cord increased brain tissue strains in all simulated cases, owing to a more compliant boundary condition, highlighting the importance of the spinal cord to assess brain response during impact.

A 3D convolutional neural network accurately predicts the permeability of gas diffusion layer materials directly from image data

Three dimensional convolutional neural networks (3D CNNs) were used to accurately predict the permeability of fuel cell gas diffusion layer (GDL) materials directly from 3D binary image data. A straightforward 3D CNN architecture was trained and evaluated on a dataset of 7500 numerically generated GDL materials using five-fold cross validation. The permeability of the generated GDL materials was calculated using pore network modelling and comported with literature reported values. The 3D CNN converged to acceptable prediction error quickly and with minimal hand tuning of hyperparameters. The trained 3D CNN had low prediction error when compared to contemporary models performing similar tasks, with an average mean absolute error (MAE) of 4.660E-13 m2 and an R2 of 0.9885, when evaluated on test fold materials. Lastly, a learning curve analysis was also performed.

Multispectral confocal 3D imaging of intact healthy and tumor tissue using mLSR-3D.

Revealing the 3D composition of intact tissue specimens is essential for understanding cell and organ biology in health and disease. State-of-the-art 3D microscopy techniques aim to capture tissue volumes on an ever-increasing scale, while also retaining sufficient resolution for single-cell analysis. Furthermore, spatial profiling through multi-marker imaging is fast developing, providing more context and better distinction between cell types. Following these lines of technological advance, we here present a protocol based on FUnGI (fructose, urea and glycerol clearing solution for imaging) optical clearing of tissue before multispectral large-scale single-cell resolution 3D (mLSR-3D) imaging, which implements 'on-the-fly' linear unmixing of up to eight fluorophores during a single acquisition. Our protocol removes the need for repetitive illumination, thereby allowing larger volumes to be scanned with better image quality in less time, also reducing photo-bleaching and file size. To aid in the design of multiplex antibody panels, we provide a fast and manageable intensity equalization assay with automated analysis to design a combination of markers with balanced intensities suitable for mLSR-3D. We demonstrate effective mLSR-3D imaging of various tissues, including patient-derived organoids and xenografted tumors, and, furthermore, describe an optimized workflow for mLSR-3D imaging of formalin-fixed paraffin-embedded samples. Finally, we provide essential steps for 3D image data processing, including shading correction that does not require pre-acquired shading references and 3D inhomogeneity correction to correct fluorescence artefacts often afflicting 3D datasets. Together, this provides a one-week protocol for eight-fluorescent-marker 3D visualization and exploration of intact tissue of various origins at single-cell resolution.

3D Bioprinted Patient-Specific Extracellular Matrix Scaffolds for Soft Tissue Defects.

Soft tissue injuries such as volumetric muscle loss (VML) are often too large to heal normally on their own, resulting in scar formation and functional deficits. Decellularized extracellular matrix (dECM) scaffolds placed into these wounds have shown the ability to modulate the immune response and drive constructive healing. This provides a potential solution for functional tissue regeneration, however, these acellular dECM scaffolds are challenging to fabricate into complex geometries. 3D bioprinting is uniquely positioned to address this, being able to create patient-specific scaffolds based on clinical 3D imaging data. Here, a process to use freeform reversible embedding of suspended hydrogels (FRESH) 3D bioprinting and computed tomography (CT) imaging to build large volume, patient-specific dECM patches (≈12×8×2cm) for implantation into canine VML wound models is developed. Quantitative analysis shows that these dECM patches are dimensionally accurate and conformally adapt to the surface of complex wounds. Finally, this approach is extendedto a human VML injury to demonstrate the fabrication of clinically relevant dECM scaffolds with precise control over fiber alignment and micro-architecture. Together these advancements represent a step towards an improved, clinically translatable, patient-specific treatment for soft tissue defects from trauma, tumor resection, and other surgical procedures.

Methodological approaches in aggregate formation and microscopic analysis to assess pseudoislet morphology and cellular interactions.

Microscopy has revolutionised our view on biology and has been vital for many discoveries since its invention around 200 years ago. Recent developments in cell biology have led to a strong interest in generating spheroids and organoids that better represent tissue. However, the current challenge faced by many researchers is the culture and analysis of these three-dimensional (3D) cell cultures. With the technological improvements in reconstructing volumetric datasets by optical sections, it is possible to quantify cells, their spatial arrangement, and the protein distribution without destroying the physical organization. We assessed three different microwell culture plates and four analysis tools for 3D imaging data for their applicability for the analysis of 3D cultures. A key advantage of microwell plates is their potential to perform high-throughput experiments in which cell cultures are generated and analysed in one single system. However, it was shown that this potential could be impacted by the material composition and microwell structure. For example, antibody staining was not possible in a hydrogel microwell, and truncated pyramid-structured microwells had increased background fluorescence due to their structure. Regarding analysis tools, four different software, namely CellProfiler, Fiji/ImageJ, Nikon GA3 and Imaris, were compared for their accuracy and applicability in analysing datasets from 3D cultures. The results showed that the open-access software, CellProfiler and Fiji, could quantify nuclei and cells, yet with varying results compared to manual counting, and may require post-processing optimisation. On the other hand, the GA3 and Imaris software packages showed excellent versatility in usage and accuracy in the quantification of nuclei and cells, and could classify cell localisation. Together these results provide critical considerations for microscopic imaging and analysis of 3D cell cultures.

Quantitative Comparison of Different Approaches for Reconstructing the Carbon‐Binder Domain from Tomographic Image Data of Cathodes in Lithium‐Ion Batteries and Its Influence on Electrochemical Properties

It is well known that the spatial distribution of the carbon‐binder domain (CBD) offers a large potential to further optimize lithium‐ion batteries. However, it is challenging to reconstruct the CBD from tomographic image data obtained by synchrotron tomography. Herein, several approaches are considered to segment 3D image data of two different cathodes into three phases, namely, active material, CBD, and pores. More precisely, it is focused on global thresholding, a local closing approach based on energy‐dispersive X‐ray spectroscopy data, ak‐means clustering method, and a procedure based on a neural network that has been trained by correlative microscopy, i.e., based on data gained by synchrotron tomography and focused ion beam scanning electron microscopy data representing the same electrode. The impact of the considered segmentation approaches on morphological characteristics as well as on the resulting performance by spatially resolved transport simulations is quantified. Furthermore, experimentally determined electrochemical properties are used to identify an appropriate range for the effective transport parameter of the CBD. The developed methodology is applied to two differently manufactured cathodes, namely, an ultrathick unstructured cathode and a two‐layer cathode with varying CBD content in both layers. This comparison elucidates the impact of a specific structuring concept on the 3D microstructure of cathodes.

Meta-Analysis of the Effects of Three-Dimensional Visualized Medical Techniques Hepatectomy for Liver Cancer with and without the Treatment of Sorafenib.

Background The application of medical image three-dimensional (3D) reconstruction technology can provide intuitive 3D image data support for accurate preoperative evaluation, surgical planning, and operation safety. However, there is still a lack of high-quality evidence to support whether 3D reconstruction technology is more advantageous in liver resection. Therefore, this study systematically evaluated the clinical effects of 3D reconstruction and two-dimensional (2D) image-assisted hepatectomy. Methods Databases were searched to collect published clinical studies on 3D reconstruction technology and 2D image-assisted liver resection. Data were extracted from the database construction to March 2022 and the risk of bias in the included studies was evaluated. Meta-analysis was performed using RevMan5.3 software. Results A total of 13 clinical studies were included, including 1616 patients, 795 in the 2D group and 819 in the 3D group. The meta-analysis showed that the incidence of postoperative complications was lower in the 3D group than in the 2D group (OR = 0.64, 95% CI = 0.49–0.83, P=0.001) and also reduced operation time (SMD = −0.51, 95% CI = −0.74∼−0.27, P < 0.0001), decreased intraoperative blood loss (SMD = −63.85, 95% CI = −98.66–29.04, P=0.0003), decreased incidence of postoperative liver failure (OR = 2.42, 95% CI = 0.99–5.95, P=0.05), decreased postoperative recurrence rate (OR = 0.29, 95% CI = 0.16–0.53, P < 0.0001), and increased postoperative survival rate (OR = 2.19, 95% CI = 1.49–3.23, P < 0.0001). Conclusions Current data suggest that 3D reconstruction-assisted hepatectomy can reduce intraoperative blood loss, postoperative complications, and recurrence, and improve postoperative survival. Therefore, the 3D reconstruction technique is worthy of application and promotion in assisted liver resection.

Geometric Analysis of 3D Facial Image Data: A Survey

Background: 3D facial image data has become an important data source in many biometric computing applications due to the increasing availability of 3D surface image collection technologies. Objective: In this survey paper, we aim to review recent advances in 3D geometry-based techniques for facial image analysis and their roles in several critical applications. Method: We first study the 3D facial landmark detection techniques which are often required for many facial data analysis applications. We then review the literatures related to several critical 3D facial image based applications including face recognition, medical diagnosis, and 3D face reconstruction. Conclusion: Our review shows that while 3D facial image data has been widely used as an important biometric data source, critical solutions still need to be developed in applications that require substantial understanding of the underlying anatomic and geometric structures of human faces.