Multi-scale 3D Research Articles

Supervised poststack 3D seismic data classification via multiscale and multilabel consistent feature reduction

Following the advancement of machine learning-based seismic feature classification techniques for complex reservoirs, the acquisition and analysis of reliable seismic samples involved in seismic facies analysis and network-based inversion have emerged as a current research hotspot in the field of intelligent seismic processing. Many investigations focus on the improvement of model classification algorithms and neural networks. However, creating and collecting labels for massive seismic data are highly time-consuming and laborious, and suffer from sample unreliability and category imbalance in the case of small-sample labels. To address such problems, a multiscale and multilabel consistent principal component analysis-linear discriminant analysis (PCA-LDA) algorithm to learn a robust feature discriminative dictionary for classification is presented. In addition to the automatic use of multilabels from well logs and core analysis, we have associated multiscale with well trajectory locations to enrich sample information and enhance the reliability of the samples during 3D sample acquisition. More specifically, we begin by proposing an approach for the automatic collection of multiscale multilabel 3D poststack seismic samples along the well track. Next, the multilabel sequence in the scan window is fed into the Boyer-Moore majority vote algorithm for sample segmentation, which constructs multilabel hierarchies for each sample. Then to enhance the model training bias due to small-sample label imbalance, we develop a novel label-shuffling balanced strategy, which obtains a complete database by filling random unduplicated augmented training samples (spatial and frequency-domain augmentation operations). Finally, the linear robustness decision-making space of PCA-LDA is obtained using the feature mapping space of PCA, as well as its visual representation. Experimental results on synthetic and field seismic data demonstrate that robust feature extraction with a trustworthy and complete multiscale and multilabel sample database increases classification accuracy.

SS-TMNet: Spatial–Spectral Transformer Network with Multi-Scale Convolution for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is a significant foundation for remote sensing image analysis, widely used in biology, aerospace, and other applications. Convolution neural networks (CNNs) and attention mechanisms have shown outstanding ability in HSI classification and have been widely studied in recent years. However, the existing CNN-based and attention mechanism-based methods cannot fully use spatial–spectral information, which is not conducive to further improving HSI classification accuracy. This paper proposes a new spatial–spectral Transformer network with multi-scale convolution (SS-TMNet), which can effectively extract local and global spatial–spectral information. SS-TMNet includes two key modules, i.e., multi-scale 3D convolution projection module (MSCP) and spatial–spectral attention module (SSAM). The MSCP uses multi-scale 3D convolutions with different depths to extract the fused spatial–spectral features. The spatial–spectral attention module includes three branches: height spatial attention, width spatial attention, and spectral attention, which can extract the fusion information of spatial and spectral features. The proposed SS-TMNet was tested on three widely used HSI datasets: Pavia University, IndianPines, and Houston2013. The experimental results show that the proposed SS-TMNet is superior to the existing methods.

3D pyramidal densely connected network with cross-frame uncertainty guidance for intravascular ultrasound sequence segmentation

Objective. Automatic extraction of external elastic membrane border (EEM) and lumen-intima border (LIB) in intravascular ultrasound (IVUS) sequences aids atherosclerosis diagnosis. Existing IVUS segmentation networks ignored longitudinal relations among sequential images and neglected that IVUS images of different vascular conditions vary largely in intricacy and informativeness. As a result, they suffered from performance degradation in complicated parts in IVUS sequences. Approach. In this paper, we develop a 3D Pyramidal Densely-connected Network (PDN) with Adaptive learning and post-Correction guided by a novel cross-frame uncertainty (CFU). The proposed method is named PDN-AC. Specifically, the PDN enables the longitudinal information exploitation and the effective perception of size-varied vessel regions in IVUS samples, by pyramidally connecting multi-scale 3D dilated convolutions. Additionally, the CFU enhances the robustness of the method to complicated pathology from the frame-level (f-CFU) and pixel-level (p-CFU) via exploiting cross-frame knowledge in IVUS sequences. The f-CFU weighs the complexity of IVUS frames and steers an adaptive sampling during the PDN training. The p-CFU visualizes uncertain pixels probably misclassified by the PDN and guides an active contour-based post-correction. Main results. Human and animal experiments were conducted on IVUS datasets acquired from atherosclerosis patients and pigs. Results showed that the f-CFU weighted adaptive sampling reduced the Hausdorff distance (HD) by 10.53%/7.69% in EEM/LIB detection. Improvements achieved by the p-CFU guided post-correction were 2.94%/5.56%. Significance. The PDN-AC attained mean Jaccard values of 0.90/0.87 and HD values of 0.33/0.34 mm in EEM/LIB detection, preferable to state-of-the-art IVUS segmentation methods.

Multiscale 3D genome reorganization during skeletal muscle stem cell lineage progression and aging.

Little is known about three-dimensional (3D) genome organization in skeletal muscle stem cells [also called satellite cells (SCs)]. Here, we comprehensively map the 3D genome topology reorganization during mouse SC lineage progression. Specifically, rewiring at the compartment level is most pronounced when SCs become activated. Marked loss in topologically associating domain (TAD) border insulation and chromatin looping also occurs during early activation process. Meanwhile, TADs can form TAD clusters and super-enhancer-containing TAD clusters orchestrate stage-specific gene expression. Furthermore, we uncover that transcription factor PAX7 is pivotal in enhancer-promoter (E-P) loop formation. We also identify cis-regulatory elements that are crucial for local chromatin organization at Pax7 locus and Pax7 expression. Lastly, we unveil that geriatric SC displays a prominent gain in long-range contacts and loss of TAD border insulation. Together, our results uncover that 3D chromatin extensively reorganizes at multiple architectural levels and underpins the transcriptome remodeling during SC lineage development and SC aging.

Integrated HBIM-GIS Models for Multi-Scale Seismic Vulnerability Assessment of Historical Buildings

The complexity of historical urban centres progressively needs a strategic improvement in methods and the scale of knowledge concerning the vulnerability aspect of seismic risk. A geographical multi-scale point of view is increasingly preferred in the scientific literature and in Italian regulation policies, that considers systemic behaviors of damage and vulnerability assessment from an urban perspective according to the scale of the data, rather than single building damage analysis. In this sense, a geospatial data sciences approach can contribute towards generating, integrating, and making virtuous relations between urban databases and emergency-related data, in order to constitute a multi-scale 3D database supporting strategies for conservation and risk assessment scenarios. The proposed approach developed a vulnerability-oriented GIS/HBIM integration in an urban 3D geodatabase, based on multi-scale data derived from urban cartography and emergency mapping 3D data. Integrated geometric and semantic information related to historical masonry buildings (specifically the churches) and structural data about architectural elements and damage were integrated in the approach. This contribution aimed to answer the research question supporting levels of knowledge required by directives and vulnerability assessment studies, both about the generative workflow phase, the role of HBIM models in GIS environments and toward user-oriented webGIS solutions for sharing and public use fruition, exploiting the database for expert operators involved in heritage preservation.

Bridging the View Disparity Between Radar and Camera Features for Multi-Modal Fusion 3D Object Detection

Environmental perception with the multi-modal fusion is crucial in autonomous driving to increase accuracy, completeness, and robustness. This paper focuses on utilizing millimeter-wave (MMW) radar and camera sensor fusion for 3D object detection. A novel method that realizes the feature-level fusion under the bird's-eye view (BEV) for a better feature representation is proposed. Firstly, radar points are augmented with temporal accumulation and sent to a spatial-temporal encoder for radar feature extraction. Meanwhile, multi-scale image 2D features which adapt to various spatial scales are obtained by image backbone and neck model. Then, image features are transformed to BEV with the designed view transformer. In addition, this work fuses the multi-modal features with a two-stage fusion model called point-fusion and ROI-fusion, respectively. Finally, a detection head regresses objects category and 3D locations. Experimental results demonstrate that the proposed method realizes the state-of-the-art (SOTA) performance under the most crucial detection metrics–mean average precision (mAP) and nuScenes detection score (NDS) on the challenging nuScenes dataset.

Productivity enhancement in multilayered coalbed methane reservoirs by radial borehole fracturing

Coalbed methane (CBM) is an important unconventional natural gas. Exploitation of multilayered CBM reservoir is still facing the challenge of low production rate. Radial borehole fracturing, which integrates radial jet drilling and hydraulic fracturing, is expected to create complex fracture networks in multilayers and enhance CBM recovery. The main purpose of this paper is to investigate the mechanisms and efficacy of radial borehole fracturing in increasing CBM production in multiple layers. First, a two-phase flow and multi-scale 3D fracture network including radial laterals, hydraulic fractures and face/butt cleats model is established, and embedded discrete fracture model (EDFM) is applied to handle the complex fracture networks. Then, effects of natural-fracture nonuniform distribution are investigated to show the advantages of targeted stimulation for radial borehole fracturing. Finally, two field CBM wells located in eastern Yunnan-western Guizhou, China were presented to illuminate the stimulation efficiency by radial borehole fracturing. The results indicated that compared with vertical well fracturing, radial borehole fracturing can achieve higher gas/water daily production rate and cumulative gas/water production, approximately 2 times higher. Targeted communications to cleats and sweet spots and flexibility in designing radial borehole parameters in different layers so as to increase fracture-network complexity and connectivity are the major reasons for production enhancement of radial borehole fracturing. Furthermore, the integration of geology-engineering is vital for the decision of radial borehole fracturing designing scheme. The key findings of this paper could provide useful insights towards understanding the capability of radial borehole fracturing in developing CBM and coal-measure gas in multiple-thin layers.

Multi-Scale 2D Temporal Adjacency Networks for Moment Localization With Natural Language.

We address the problem of retrieving a specific moment from an untrimmed video by natural language. It is a challenging problem because a target moment may take place in the context of other temporal moments in the untrimmed video. Existing methods cannot tackle this challenge well since they do not fully consider the temporal contexts between temporal moments. In this paper, we model the temporal context between video moments by a set of predefined two-dimensional maps under different temporal scales. For each map, one dimension indicates the starting time of a moment and the other indicates the duration. These 2D temporal maps can cover diverse video moments with different lengths, while representing their adjacent contexts at different temporal scales. Based on the 2D temporal maps, we propose a Multi-Scale Temporal Adjacency Network (MS-2D-TAN), a single-shot framework for moment localization. It is capable of encoding the adjacent temporal contexts at each scale, while learning discriminative features for matching video moments with referring expressions. We evaluate the proposed MS-2D-TAN on three challenging benchmarks, i.e., Charades-STA, ActivityNet Captions, and TACoS, where our MS-2D-TAN outperforms the state of the art.

Multiscale lightweight 3D segmentation algorithm with attention mechanism: Brain tumor image segmentation

This study proposes a lightweight automatic 3D algorithm with an attention mechanism for the segmentation of brain-tumor images to address the challenges. Accurate segmentation of brain-tumor regions in medical images is essential in patient diagnosis using the 3D U-Net; however, existed 3D networks lack the ability to automatically focus on smaller targets and have low precision on segmenting brain-tumor regions. Existed 3D networks use numerous parameters and are difficult to deploy in practice. The 3D-UNet serves as the basic structure of the proposed approach. (1) this study replaces the standard convolutions with hierarchical decoupled convolutions to reduce the number of parameters; (2) this study adds dilated convolutions to enhance the ability of the network to express multiscale information in the bottom convolution module; (3) this study introduces an attention mechanism to the output layer so that the network can automatically focus on the region of the tumor and use the relationships among the enhancing tumor, whole tumor, and tumor core to increase segmentation precision. The results of the proposed network model, applied to the BraTS 2019 test set, revealed that the Dice coefficients of the proposed ADHDC-Net for enhancing tumor, whole tumor, and tumor core were 77.91 %, 89.94 %, and 83.89 %, respectively, using 0.30 M parameters and 25.81 G floating-point operations. Extensive experiments on the BraTS 2018, BraTS 2019 and BraTS 2020 dadasets show that the proposed model has better potential with regard to the efficient segmentation of small brain-tumor regions.

A novel multi-scale 2D CNN with weighted focal loss for arrhythmias detection on varying-dimensional ECGs

Objective. The ECG is a standard diagnostic tool for identifying many arrhythmias. Accurate diagnosis and early intervention for arrhythmias are of great significance to the prevention and treatment of cardiovascular disease. Our objective is to develop an algorithm that can automatically identify 30 arrhythmias by using varying-dimensional ECG signals. Approach. In this paper, we firstly proposed a novel multi-scale 2D CNN that can effectively capture pathological information from small-scale to large-scale from ECG signals to identify 30 arrhythmias from 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead ECGs. Secondly, we explored the effects of varying convolution kernels sizes and branch subnetworks on the model’s performance for each arrhythmia. Thirdly, we introduced the weighted focal loss to alleviate the positive-negative class imbalance problem in the multi-label arrhythmias classification. Fourthly, we explored the utility of reduced-lead ECGs in detecting arrhythmias by comparing the performances of models on varying-dimensional ECGs. Main results. As a follow-up entry after the PhysioNet/Computing in Cardiology Challenge (2021), our proposed approach achieved the official test scores of 0.52, 0.47, 0.53, 0.51, and 0.50 for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead ECGs on the hidden test set (comparable to that of 6th, 11th, 4th, 5th, and 7th out of 39 teams in the Challenge). Significance. A multi-scale framework capable of detecting 30 arrhythmias from varying-dimensional ECGs was proposed in our work. We preliminarily verified that the multi-scale perception fields may be necessary to capture more comprehensive pathological information for arrhythmias detection. Besides, we also verified that the weighted focal loss may alleviate the positive–negative class imbalance and improve the model’s generalization performance on the cross-dataset. In addition, we observed that some reduced-lead models, such as the 4-lead and 3-lead models, can even achieve performance that is almost comparable to that of the 12-lead model. The excellent performance of our proposed framework demonstrates its great potential in detecting a wide range of arrhythmias.

Indirect 3D Bioprinting of a Robust Trilobular Hepatic Construct with Decellularized Liver Matrix Hydrogel.

The liver exhibits complex geometrical morphologies of hepatic cells arranged in a hexagonal lobule with an extracellular matrix (ECM) organized in a specific pattern on a multi-scale level. Previous studies have utilized 3D bioprinting and microfluidic perfusion systems with various biomaterials to develop lobule-like constructs. However, they all lack anatomical relevance with weak control over the size and shape of the fabricated structures. Moreover, most biomaterials lack liver-specific ECM components partially or entirely, which might limit their biomimetic mechanical properties and biological functions. Here, we report 3D bioprinting of a sacrificial PVA framework to impart its trilobular hepatic structure to the decellularized liver extracellular matrix (dLM) hydrogel with polyethylene glycol-based crosslinker and tyrosinase to fabricate a robust multi-scale 3D liver construct. The 3D trilobular construct exhibits higher crosslinking, viscosity (182.7 ± 1.6 Pa·s), and storage modulus (2554 ± 82.1 Pa) than non-crosslinked dLM. The co-culture of HepG2 liver cells and NIH 3T3 fibroblast cells exhibited the influence of fibroblasts on liver-specific activity over time (7 days) to show higher viability (90-91.5%), albumin secretion, and increasing activity of four liver-specific genes as compared to the HepG2 monoculture. This technique offers high lumen patency for the perfusion of media to fabricate a densely populated scaled-up liver model, which can also be extended to other tissue types with different biomaterials and multiple cells to support the creation of a large functional complex tissue.

Flexible needle puncture path planning for liver tumors based on deep reinforcement learning

Objective. Minimally invasive surgery has been widely adopted in the treatment of patients with liver tumors. In liver tumor puncture surgery, an image-guided ablation needle for puncture surgery, which first reaches a target tumor along a predetermined path, and then ablates the tumor or injects drugs near the tumor, is often used to reduce patient trauma, improving the safety of surgery operations and avoiding possible damage to large blood vessels and key organs. In this paper, a path planning method for computer tomography (CT) guided ablation needle in liver tumor puncture surgery is proposed. Approach. Given a CT volume containing abdominal organs, we first classify voxels and optimize the number of voxels to reduce volume rendering pressure, then we reconstruct a multi-scale 3D model of the liver and hepatic vessels. Secondly, multiple entry points of the surgical path are selected based on the strong and weak constraints of clinical puncture surgery through multi-agent reinforcement learning. We select the optimal needle entry point based on the length measurement. Then, through the incremental training of the double deep Q-learning network (DDQN), the transmission of network parameters from the small-scale environment to the larger-scale environment is accomplished, and the optimal surgical path with more optimized details is obtained. Main results. To avoid falling into local optimum in network training, improve both the convergence speed and performance of the network, and maximize the cumulative reward, we train the path planning network on different scales 3D reconstructed organ models, and validate our method on tumor samples from public datasets. The scores of human surgeons verified the clinical relevance of the proposed method. Significance. Our method can robustly provide the optimal puncture path of flexible needle for liver tumors, which is expected to provide a reference for surgeons’ preoperative planning.

A photoacoustics-enhanced drilling probe for radiation-free pedicle screw implantation in spinal surgery.

This article proposes a novel intra-operative navigation and sensing system that optimizes the functional accuracy of spinal pedicle screw implantation. It does so by incorporating radiation-free and multi-scale macroscopic 3D ultrasound (US) imaging and local tissue-awareness from in situ photoacoustic (PA) sensing at a clinically relevant mesoscopic scale. More specifically, 3D US imaging is employed for online status updates of spinal segment posture to determine the appropriate entry point and coarse drilling path once non-negligible or relative patient motion occurs between inter-vertebral segments in the intra-operative phase. Furthermore, a sophisticated sensor-enhanced drilling probe has been developed to facilitate fine-grained local navigation that integrates a PA endoscopic imaging component for in situ tissue sensing. The PA signals from a sideways direction to differentiate cancellous bone from harder cortical bone, or to indicate weakened osteoporotic bone within the vertebrae. In so doing it prevents cortical breaches, strengthens implant stability, and mitigates iatrogenic injuries of the neighboring artery and nerves. To optimize this PA-enhanced endoscopic probe design, the light absorption spectrum of cortical bone and cancellous bone are measured in vitro, and the associated PA signals are characterized. Ultimately, a pilot study is performed on an ex vivo bovine spine to validate our developed multi-scale navigation and sensing system. The experimental results demonstrate the clinical feasibility, and hence the great potential, for functionally accurate screw implantation in complex spinal stabilization interventions.

Multiscale 3D genome organization underlies ILC2 ontogenesis and allergic airway inflammation.

Innate lymphoid cells (ILCs) are well-characterized immune cells that play key roles in host defense and tissue homeostasis. Yet, how the three-dimensional (3D) genome organization underlies the development and functions of ILCs is unknown. Herein, we carried out an integrative analysis of the 3D genome structure, chromatin accessibility and gene expression in mature ILCs. Our results revealed that the local 3D configuration of the genome is rewired specifically at loci associated with ILC biology to promote their development and functional differentiation. Importantly, we demonstrated that the ontogenesis of ILC2s and the progression of allergic airway inflammation are determined by a unique local 3D configuration of the region containing the ILC-lineage-defining factor Id2, which is characterized by multiple interactions between the Id2 promoter and distal regulatory elements bound by the transcription factors GATA-3 and RORα, unveiling the mechanism whereby the Id2 expression is specifically controlled in group 2 ILCs.

A multiscale 3D convolution with context attention network for hyperspectral image classification

A multiscale 3D convolution with context attention network for hyperspectral image classification

The multiscale 3D convolutional network for emotion recognition based on electroencephalogram.

Emotion recognition based on EEG (electroencephalogram) has become a research hotspot in the field of brain-computer interfaces (BCI). Compared with traditional machine learning, the convolutional neural network model has substantial advantages in automatic feature extraction in EEG-based emotion recognition. Motivated by the studies that multiple smaller scale kernels could increase non-linear expression than a larger scale, we propose a 3D convolutional neural network model with multiscale convolutional kernels to recognize emotional states based on EEG signals. We select more suitable time window data to carry out the emotion recognition of four classes (low valence vs. low arousal, low valence vs. high arousal, high valence vs. low arousal, and high valence vs. high arousal). The results using EEG signals in the DEAP and SEED-IV datasets show accuracies for our proposed emotion recognition network model (ERN) of 95.67 and 89.55%, respectively. The experimental results demonstrate that the proposed approach is potentially useful for enhancing emotional experience in BCI.

Lightweight multi-scale convolutional neural network for real time stereo matching

In order to accurately estimate disparities in textureless and slim regions, spatial pyramid pooling and stacked 3D CNN, which can capture global context information, are widely used in state-of-the-art stereo matching algorithms. Unfortunately, the computational complexity and high memory consumption make these methods not friendly to real-time applications such as autonomous driving and augmented realities. In order to balance the real-time performance and accuracy, we design lightweight multi-scale convolutional neural network for real-time stereo matching. First, Lightweight multi-scale 2D and 3D CNN modules are proposed for feature extraction and initial disparity computation respectively. Both of above modules only run on a low resolution to further reduce the amount of calculation. Second, multi-scale RGB images guided network is utilized to refine the final disparity estimation. Experiments on several datasets show that the proposed algorithm can achieve competitive results with speed of 64fps on a NIVDIA 1080 GPU. • The proposed LMNet can balance time efficiency and matching accuracy. • Lightweight MS2D module can extract more global features with less computation. • Lightweight MS3D module yield roubust results especially for slim objects.

Hybrid Open-Top Light-Sheet Microscopy for Multi-Scale 3D Imaging of Cleared and Expanded Tissues

Hybrid Open-Top Light-Sheet Microscopy for Multi-Scale 3D Imaging of Cleared and Expanded Tissues

X-ray multiscale 3D neuroimaging to quantify cellular aging and neurodegeneration postmortem in a model of Alzheimer’s disease

PurposeModern neuroimaging lacks the tools necessary for whole-brain, anatomically dense neuronal damage screening. An ideal approach would include unbiased histopathologic identification of aging and neurodegenerative disease.MethodsWe report the postmortem application of multiscale X-ray phase-contrast computed tomography (X-PCI-CT) for the label-free and dissection-free organ-level to intracellular-level 3D visualization of distinct single neurons and glia. In deep neuronal populations in the brain of aged wild-type and of 3xTgAD mice (a triply-transgenic model of Alzheimer’s disease), we quantified intracellular hyperdensity, a manifestation of aging or neurodegeneration.ResultsIn 3xTgAD mice, the observed hyperdensity was identified as amyloid-β and hyper-phosphorylated tau protein deposits with calcium and iron involvement, by correlating the X-PCI-CT data to immunohistochemistry, X-ray fluorescence microscopy, high-field MRI, and TEM. As a proof-of-concept, X-PCI-CT was used to analyze hippocampal and cortical brain regions of 3xTgAD mice treated with LY379268, selective agonist of group II metabotropic glutamate receptors (mGlu2/3 receptors). Chronic pharmacologic activation of mGlu2/3 receptors significantly reduced the hyperdensity particle load in the ventral cortical regions of 3xTgAD mice, suggesting a neuroprotective effect with locoregional efficacy.ConclusionsThis multiscale micro-to-nano 3D imaging method based on X-PCI-CT enabled identification and quantification of cellular and sub-cellular aging and neurodegeneration in deep neuronal and glial cell populations in a transgenic model of Alzheimer’s disease. This approach quantified the localized and intracellular neuroprotective effects of pharmacological activation of mGlu2/3 receptors.

Automated Defect Recognition for Additive Manufactured Parts Using Machine Perception and Visual Saliency.

Metal additive manufacturing (AM) is known to produce internal defects that can impact performance. As the technology becomes more mainstream, there is a growing need to establish nondestructive inspection technologies that can assess and quantify build quality with high confidence. This article presents a complete, three-dimensional (3D) solution for automated defect recognition in AM parts using X-ray computed tomography (CT) scans. The algorithm uses a machine perception framework to automatically separate visually salient regions, that is, anomalous voxels, from the CT background. Compared with supervised approaches, the proposed concept relies solely on visual cues in 3D similar to those used by human operators in two-dimensional (2D) assuming no a priori information about defect appearance, size, and/or shape. To ingest any arbitrary part geometry, a binary mask is generated using statistical measures that separate lighter, material voxels from darker, background voxels. Therefore, no additional part or scan information, such as CAD files, STL models, or laser scan vector data, is needed. Visual saliency is established using multiscale, symmetric, and separable 3D convolution kernels. Separability of the convolution kernels is paramount when processing CT scans with potentially billions of voxels because it allows for parallel processing and thus faster execution of the convolution operation in single dimensions. Based on the CT scan resolution, kernel sizes may be adjusted to identify defects of different sizes. All adjacent anomalous voxels are subsequently merged to form defect clusters, which in turn reveals additional information regarding defect size, morphology, and orientation to the user, information that may be linked to mechanical properties, such as fatigue response. The algorithm was implemented in MATLAB™ using hardware acceleration, that is, graphics processing unit support, and tested on CT scans of AM components available at the Center for Innovative Materials Processing through Direct Digital Deposition (CIMP-3D) at Penn State's Applied Research Laboratory. Initial results show adequate processing times of just a few minutes and very low false-positive rates, especially when addressing highly salient and larger defects. All developed analytic tools can be simplified to accommodate 2D images.