3D Heat Maps Research Articles

Three-Dimensional Mapping of the Rotation of Interactive Virtual Objects with Eye-Tracking Data.

We present a method for real-time recording of human interaction with three-dimensional (3D) virtual objects. The approach consists of associating rotation data of the manipulated object with behavioral measures, such as eye tracking, to make better inferences about the underlying cognitive processes. The task consists of displaying two identical models of the same 3D object (a molecule), presented on a computer screen: a rotating, interactive object (iObj) and a static, target object (tObj). Participants must rotate iObj using the mouse until they consider its orientation to be identical to that of tObj. The computer tracks all interaction data in real time. The participant's gaze data are also recorded using an eye tracker. The measurement frequency is 10 Hz on the computer and 60 Hz on the eye tracker. The orientation data of iObj with respect to tObj are recorded in rotation quaternions. The gaze data are synchronized to the orientation of iObj and referenced using this same system. This method enables us to obtain the following visualizations of the human interaction process with iObj and tObj: (1) angular disparity synchronized with other time-dependent data; (2) 3D rotation trajectory inside what we decided to call a "ball of rotations"; (3) 3D fixation heatmap. All steps of the protocol have used free software, such as GNU Octave and Jmol, and all scripts are available as supplementary material. With this approach, we can conduct detailed quantitative studies of the task-solving process involving mental or physical rotations, rather than only the outcome reached. It is possible to measure precisely how important each part of the 3D models is for the participant in solving tasks, and thus relate the models to relevant variables such as the characteristics of the objects, cognitive abilities of the individuals, and the characteristics of human-machine interface.

MAPPING GLIOMAS: A RADIOGRAPHIC AND SPATIAL ANALYSIS ACCORDING TO THE 2021 WHO CLASSIfiCATION

Abstract AIMS The spatial positioning of gliomas within the brain significantly influences prognosis, potentially mirroring the genetic landscape of their precursor cells. Furthermore, the association between glioma subtypes and their spatial distribution underscores a complex interplay that may illuminate underlying pathogenetic mechanisms. This study explores the intricate relationship between the location of gliomas and their classification according to the 2021 WHO guidelines, aiming to unravel the spatial and genetic nuances that define glioma heterogeneity. METHOD In this study, preoperative anatomical MRI data from 242 glioma patients were meticulously analyzed to elucidate spatial distribution patterns in relation to tumor genetics. The imaging data underwent registration to the MNI-152 standard brain space, facilitating uniform analysis. Subsequent tumor segmentation and division of the brain into 84 distinct subregions allowed for a detailed examination of tumor locations. A statistical analysis was conducted to explore the stereospecific frequency of glioma occurrence across these subregions, correlating findings with tumor grade and pivotal genetic markers, including MGMT promoter methylation, IDH1 mutation, 1p/19q co-deletion, and CDKN2A/B homozygous deletion. RESULTS We developed 21 distinct tumor frequency atlases based on subtypes (Glioblastoma, Oligodendroglioma, Astrocytoma), biomarker status (IDH, 1p19q, MGMT, CDKN2A/B), and grade (2, 3, 4). Employing probability vectors for statistical analysis revealed a significant correlation among different subtypes, locations and overall survival. Furthermore, we used a Visualization tool to build a 3D heatmap for each tumor subtype, more intuitively illustrating spatial correlations. CONCLUSION Our study underscores the prognostic value of tumor location and its correlation with glioma subtypes and patient outcomes, suggesting a potential pathway to personalized treatments. Future efforts will focus on incorporating multi-omics data into spatial radiographic analyses.

Mandibular Gender Dimorphism: The Utility of Artificial Intelligence and Statistical Shape Modeling in Skeletal Facial Analysis.

In gender-affirming surgery, facial skeletal dimorphism is an important topic for every craniofacial surgeon. Few cephalometric studies have assessed this topic; however, they fall short to provide skeletal contour insights that direct surgical planning. Herein, we propose statistical shape modeling (SSM) as a novel tool for investigating mandibular dimorphism for young white individuals. A single-center, retrospective study was performed using computed tomography (CT) scans of white individuals, aged 20 to 39 years old. AI-assisted, three-dimensional (3D) mandibles were reconstructed in Materialise Mimics v25.0. We used SSM to generate average 3D models for both genders. Relevant manual anthropometric measurements were taken for the SSMs and individual mandibles. Contour disparities were then represented using 3D overlays and heatmaps. Statistical analyses were performed using unpaired student t testing or Wilcoxon signed rank testing with 95% confidence interval as deemed appropriate by population-level normality assessment. Ninety-eight patients (53 females, 45 males) were included. Male mandibles showed greater bigonial width, intercondylar width, ramus height, and body length [p<0.005]. There was no statistically significant difference in the gonial angle measurements [p=0.62]. All relevant manual individual measurements demonstrated excellent concordance to their SSM counterparts. The 3D overlays of SSMs revealed squarer male chins with more lateral but less anterior projection than their female counterparts. Also, the female mandibles showed smoother transition at the gonial angle. SSM provides a novel tool to objectively evaluate volumetric and contour dimorphisms between genders. Moreover, this method can be automated, allowing for expedited comparisons between populations of interest compared to manual assessment. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 . Bullet points about the importance of this work: Advancing Anthropometric Assessment: Statistical shape modeling (SSM) offers a cutting-edge approach to visualizing gender-specific skeletal anatomic differences for aesthetic and gender-affirming facial surgery. Expediting Comparative Analysis: The workflow established in this paper streamlines the evaluative process, enabling rapid morphologic comparisons between populations. Patient-Centered Care: This study establishes a foundation for the development of SSMs in individualized operative planning.

Cerebroventricular deformation and vector mapping, a topographic visualizer for surgical interventions in pediatric hydrocephalus.

Hydrocephalus is a challenging neurosurgical condition due to nonspecific symptoms and complex brain-fluid pressure dynamics. Typically, the assessment of hydrocephalus in children requires radiographic or invasive pressure monitoring. There is usually a qualitative focus on the ventricular spaces even though stress and shear forces extend across the brain. Here, the authors present an MRI-based vector approach for voxelwise brain and ventricular deformation visualization and analysis. Twenty pediatric patients (mean age 7.7 years, range 6 months-18 years; 14 males) with acute, newly diagnosed hydrocephalus requiring surgical intervention for symptomatic relief were randomly identified after retrospective chart review. Selection criteria included acquisition of both pre- and posttherapy paired 3D T1-weighted volumetric MRI (3D T1-MRI) performed on 3T MRI systems. Both pre- and posttherapy 3D T1-MRI pairs were aligned using image registration, and subsequently, voxelwise nonlinear transformations were performed to derive two exemplary visualizations of compliance: 1) a whole-brain vector map projecting the resulting deformation field on baseline axial imaging; and 2) a 3D heat map projecting the volumetric changes along ventricular boundaries and the brain periphery. The patients underwent the following interventions for treatment of hydrocephalus: endoscopic third ventriculostomy (n = 6); external ventricular drain placement and/or tumor resection (n = 10); or ventriculoperitoneal shunt placement (n = 4). The mean time between pre- and postoperative imaging was 36.5 days. Following intervention, the ventricular volumes decreased significantly (mean pre- and posttherapy volumes of 151.9 cm3 and 82.0 cm3, respectively; p < 0.001, paired t-test). The largest degree of deformation vector changes occurred along the lateral ventricular spaces, relative to the genu and splenium. There was a significant correlation between change in deformation vector magnitudes within the cortical layer and age (p = 0.011, Pearson), as well as between the ventricle size and age (p = 0.014, Pearson), suggesting higher compliance among infants and younger children. This study highlights an approach for deformation analysis and vector mapping that may serve as a topographic visualizer for therapeutic interventions in patients with hydrocephalus. A future study that correlates the degree of cerebroventricular deformation or compliance with intracranial pressures could clarify the potential role of this technique in noninvasive pressure monitoring or in cases of noncompliant ventricles.

Panoramic heat map for spatial distribution of necrotic lesions.

In this study, we aimed to visualize the spatial distribution characteristics of femoral head necrosis using a novel measurement method. We retrospectively collected CT imaging data of 108 hips with non-traumatic osteonecrosis of the femoral head from 76 consecutive patients (mean age 34.3 years (SD 8.1), 56.58% male (n = 43)) in two clinical centres. The femoral head was divided into 288 standard units (based on the orientation of units within the femoral head, designated as N[Superior], S[Inferior], E[Anterior], and W[Posterior]) using a new measurement system called the longitude and latitude division system (LLDS). A computer-aided design (CAD) measurement tool was also developed to visualize the measurement of the spatial location of necrotic lesions in CT images. Two orthopaedic surgeons independently performed measurements, and the results were used to draw 2D and 3D heat maps of spatial distribution of necrotic lesions in the femoral head, and for statistical analysis. The results showed that the LLDS has high inter-rater reliability. As illustrated by the heat map, the distribution of Japanese Investigation Committee (JIC) classification type C necrotic lesions exhibited clustering characteristics, with the lesions being concentrated in the northern and eastern regions, forming a hot zone (90% probability) centred on the N4-N6E2, N3-N6E units of outer ring blocks. Statistical results showed that the distribution difference between type C2 and type C1 was most significant in the E1 and E2 units and, combined with the heat map, indicated that the spatial distribution differences at N3-N6E1 and N1-N3E2 units are crucial in understanding type C1 and C2 necrotic lesions. The LLDS can be used to accurately measure the spatial location of necrotic lesions and display their distribution characteristics.

Quadrilateral plate classification program of acetabular fractures based on three-column classification: a three-dimensional fracture mapping study

BackgroundA new classification system for acetabular fractures has been proposed in recent years, which is called the 3-column classification. However, this system does not provide information regarding quadrilateral plate fractures. To address this issue, we utilized three-dimensional (3D) fracture line mapping and heat map to analyze the link between the 3-column classification and quadrilateral plate fractures.MethodsWe collected CT scan data from 177 patients who had been diagnosed with acetabular fractures. Additionally, we utilized a CT scan of a healthy adult to generate a standard acetabular model. We utilized the collected CT data of the fracture to create a 3D model and subsequently reduced it. We then matched each acetabular fracture model with the standard acetabular model and mapped all of the fracture lines to the standard model. 3D fracture lines and heat maps were created by overlapping all fracture lines. Fracture characteristics were then summarized using these maps.ResultsThis study analyzed a total of 221 acetabular fractures. The most frequently observed fracture type, based on the three-column classification, was A1.2, which corresponds to fractures of the anterior column. In contrast, the least common type of fracture was A4, which represents fractures of the central wall. It was noted that quadrilateral plate fractures were frequently observed in fractures classified as type B and C according to the three-column classification.ConclusionsAmong the three-column classification, the QLP fractures are commonly observed in type B and C. It is important to carefully identify these fractures during the diagnostic process. Therefore, based on the three-column classification, we have amalgamated quadrilateral plate fractures and formulated a classification program for acetabular fractures.

3D Acoustic Heat-Maps for Transformer Monitoring Applications

&#x0D; &#x0D; &#x0D; We present a novel approach for measuring and representing acoustic emissions of transformers. The representation of location-based acoustic emissions enables improved monitoring of transformers, e.g., to detect and predict anomalies and failure events. Here, we introduce 3D acoustic heatmaps to visualize the sound emission patterns of a transformer. We use a combined sensing approach consisting of a 3D point cloud, a microphone array, and beamforming algorithms to generate the distributed representation of acoustic emissions over the entire surface of the transformer. In a further step, we intend to apply machine learning methods to the generated data to enable early fault prevention and predictive maintenance.&#x0D; &#x0D; &#x0D;

Multi-modal 3-Dimensional Visualization of Pediatric Neuroblastoma: Aiding Surgical Planning Beyond Anatomical Information

BackgroundPatient-specific 3D models of neuroblastoma and relevant anatomy are useful tools for surgical planning. However, these models do not represent the heterogenous biology of neuroblastoma. This heterogeneity is visualized with the ADC and 123I-MIGB-SPECT-CT imaging. Combining these multi-modal data into preoperative 3D heatmaps, may allow differentiation of the areas of vital and non-vital tumor tissue. We developed a workflow to create multi-modal preoperative 3D models for neuroblastoma surgery. MethodsWe included 7 patients who underwent neuroblastoma surgery between 2022 and 2023. We developed 3D models based on the contrast enhanced T1-weighted MRI scans. Subsequently, we aligned the corresponding ADC and 123I-MIBG-SPECT-CT images using rigid transformation. We estimated registration precision using the Dice score and the target registration error (TRE). 3D heatmaps were computed based on ADC and 123I-MIBG uptake. ResultsThe registration algorithm had a median Dice score of 0.81 (0.75–0.90) for ADC and 0.77 (0.65–0.91) for 123I-MIBG-SPECT. For the ADC registration, the median TRE of renal vessels was 4.90 mm (0.86–10.18) and of the aorta 4.67 mm (1.59–12.20). For the 123I -MIBG-SPECT imaging the TRE of the renal vessels was 5.52 mm (1.71–10.97) and 5.28 mm (3.33–16.77) for the aorta. ConclusionsWe successfully developed a registration workflow to create multi-modal 3D models which allows the surgeon to visualize the tumor and its biological behavior in relation to the surrounding tissue. Future research will include linking of pathological results to imaging data, to validate these multi-modal 3D models. Level of EvidenceLevel IV. Type of StudyClinical Research.

Using a 3D heat map to explore the diverse correlations among elements and mineral species

This paper presents an enhanced 3D heat map for exploratory data analysis (EDA) of open mineral data, addressing the challenges caused by rapidly evolving datasets and ensuring scientifically meaningful data exploration. The Mindat website, a crowd-sourced database of mineral species, provides a constantly updated open data source via its newly established application programming interface (API). To illustrate the potential usage of the API, we constructed an automatic workflow to retrieve and cleanse mineral data from it, thus feeding the 3D heat map with up-to-date records of mineral species. In the 3D heat map, we developed scientifically sound operations for data selection and visualization by incorporating knowledge from existing mineral classification systems and recent studies in mineralogy. The resulting 3D heat map has been shared as an online demo system, with the source code made open on GitHub. We hope this updated 3D heat map system will serve as a valuable resource for researchers, educators, and students in geosciences, demonstrating the potential for data-intensive research in mineralogy and broader geoscience disciplines.

Morphology and novel classification of proximal humeral fractures.

Background: The morphology of proximal humeral fractures (PHFs) is complex, and the fixation and selection of implants need to be guided by the fracture type and classification, which requires an accurate understanding of the fracture line. This study had three purposes. 1) Define and analyze the fracture lines and morphological features of all types PHFs by three-dimensional (3D) mapping technology. 2) Determine the osteotomy position of the biomechanical model of the PHFs according to the fracture heat map. 3) Based on the analysis of the pathological morphology and distribution of a large number of consecutive cases of PHFs, propose a novel classification of PHFs. Methods: We retrospectively collected 220 cases of PHFs and generated a 3D fracture map and heat map based on computed tomography (CT) imaging. Through analysis of the fracture morphology of the 220 PHFs, a novel classification was proposed. The primary criterion for staging was the continuity between the humeral head and the greater tuberosity and lesser tuberosity, and the secondary criterion was the relationship between the humeral head segment and the humeral shaft. Results: The fracture line was primarily found around the metaphyseal zone of region of the surgical neck, with the most extensive distribution being below the larger tuberosity and on the posterior medial side of the epiphysis. We suggest that the osteotomy gap should be immediately (approximately 5-10mm) below the lower edge of the articular surface. The most common type of fracture was type I3 (33 cases, 15.0%), followed by type IV3 fracture (23 cases, 10.4%), and type III2 fracture (22 cases, 10.0%). Interobserver and intraobserver reliability analysis for the fracture classification revealed a k value (95% confidence interval) of 0.639 (0.57-0.71) and 0.841, P < 0.01, respectively. Conclusion: In this study, the fracture line and morphological characteristics of PHFs were clarified in detail by 3D mapping technique. In addition, a new classification method was proposed by analysis of the morphological characteristics of 220 PHFs, A two-part fracture model for PHFs is also proposed.

Cella: 3D data visualization for plant single-cell transcriptomics in Blender.

Recent advancements in single-cell transcriptomics have facilitated the possibility of acquiring vast amounts of data at single-cell resolution. This development has provided a broader and more comprehensive understanding of complex biological processes. The growing datasets require a visualization tool that transforms complex data into an intuitive representation. To address this challenge, we have utilized an open-source 3D software Blender to design Cella, a cell atlas visualization tool, which transforms data into 3D heatmaps that can be rendered into image libraries. Our tool is designed to support especially research on plant development. To validate our method, we have created a 3D model representing the Arabidopsis thaliana root meristem and mapped an existing single-cell RNA-seq dataset into the 3D model. This provided a user-friendly visual representation of the expression profiles of 21,489 genes from two perspectives (42,978 images). This approach is not limited to single-cell RNA-seq data of the Arabidopsis root meristem. We provide detailed step-by-step instructions to generate 3D models and a script that can be customized to project data onto different tissues. Our tool provides a proof-of-concept method for how increasingly complex single-cell RNA-seq datasets can be visualized in a simple and cohesive manner.

Deep Learning-Based Interpretable AI for Prostate T2W MRI Quality Evaluation

Prostate MRI quality is essential in guiding prostate biopsies. However, assessment of MRI quality is subjective with variation. Quality degradation sources exert varying impacts based on the sequence under consideration, such as T2W versus DWI. As a result, employing sequence-specific techniques for quality assessment could yield more advantageous outcomes. This study aims to develop an AI tool that offers a more consistent evaluation of T2W prostate MRI quality, efficiently identifying suboptimal scans while minimizing user bias. This retrospective study included 1046 patients from threecohorts (ProstateX [n=347], All-comer in-house [n=602], enriched bad-quality MRI in-house [n=97]) scanned between January 2011 and May 2022. An expert reader assigned T2W MRIs a quality score. A train-validation-test split of 70:15:15 was applied, ensuring equal distribution of MRI scanners and protocols across all partitions. T2W quality AI classification model was based on 3D DenseNet121 architecture using MONAI framework. In addition to multiclassification, binary classification was utilized (Classes 0/1 vs. 2). A score of 0 was given to scans considered non-diagnostic or unusable, a score of 1 was given to those with acceptable diagnostic quality with some usability but with some quality distortions present, and a score of 2 was given to those considered optimal diagnostic quality and usability. Partial occlusion sensitivity maps were generated for anatomical correlation. Three body radiologists assessed reproducibility within a subgroup of 60 test cases using weighted Cohen Kappa. The best validation multiclass accuracy of 77.1% (121/157) was achieved during training. In the test dataset, multiclassification accuracy was 73.9% (116/157), whereas binary accuracy was 84.7% (133/157). Sub-class sensitivity for binary quality distortion classification for class 0 was 100% (18/18), and sub-class specificity for T2W classification of absence/minimal quality distortions for class 2 was 90.5% (95/105). All three readers showed moderate to substantial agreement with ground truth (R1-R3 κ=0.588, κ=0.649, κ=0.487, respectively), moderate to substantial agreement with each other (R1-R2 κ=0.599, R1-R3 κ=0.612, R2-R3 κ=0.685), fair to moderate agreement with AI (R1-R3 κ=0.445, κ=0.410, κ=0.292, respectively). AI showed substantial agreement with ground truth (κ=0.704). 3D quality heatmap evaluation revealed that the most critical non-diagnostic quality imaging features from an AI perspective related to obscuration of the rectoprostatic space (94.4%, 17/18). The 3D AI model can assess T2W prostate MRI quality with moderate accuracy and translate whole sequence-level classification labels into 3D voxel-level quality heatmaps for interpretation. Image quality has a significant downstream impact on ruling out clinically significant cancers. AI may be able to help with reproducible identification of MRI sequences requiring re-acquisition with explainability.

AttentionVote: A coarse-to-fine voting network of anchor-free 6D pose estimation on point cloud for robotic bin-picking application

Current state-of-the-art pose estimation methods are almost launched on segmented RGB-D images. However, these methods may not apply to more general industrial parts due to a lack of texture information and high-occlusion of stacked objects. This article establishes an end-to-end pipeline to synchronously regress all potential object poses from an unsegmented point cloud. The point pair features (PPFs) are first extracted and then fed into a PointNet-like backbone for obtaining the point-wise features. Based on the center voting, a coarse-to-fine voting architecture is proposed to extract instance features instead of implementing instance segmentation. A lightweight three-dimensional (3D) heatmap is leveraged to cluster votes and generate center seeds. Further, an attention voting module is constructed to fuse point-wise features into instance-wise features adaptively. Ultimately, the suggested network regresses object poses with a quaternion loss to handle the symmetric puzzle. The network holds the advantage of producing the final pose prediction without any post-processing steps like non-maximum suppression (NMS) or any pose refinement modules like iterative closest point (ICP). The proposed network is evaluated on the public Fraunhofer IPA dataset, which demonstrates the robustness of the pose estimation network with much better performance. Meanwhile, the network is further validated on our synthetic and real-world datasets of industrial parts for robotic bin-picking.

Should all Small Shell Posterior Malleolar Fractures be Considered for Fixation? Results from a 3D Fracture Mapping Study

Category: Ankle; Trauma Introduction/Purpose: Approximately 10-15% of posterior malleolar fractures (PMFs) are "small shell," extra-articular fragments. Current classification systems present difficulties to perform a uniform typification of PMFs and contain no consensus on whether they should be fixed. Anatomical studies have identified two distinct components of the posterior inferior tibiofibular ligament (PITFL); the superficial band is thought to be more important than its deep counterpart in imparting syndesmotic stability. However, the involvement of one or both bands of the PITFL by small shell PMFs has not been evaluated so far. Hence, we conducted this study to perform 3D mapping of small shell PMFs and to determine whether surgeons should fix these routinely. Methods: Ankle fracture patients with a ‘small shell’ PMF (Haraguchi 3/Mason 1/Bartoníček 1 or 2) were included. Demographics, radiological features, treatment, and outcomes were recorded. 3D models of the fractured tibiae were generated from CT scans and superimposed on a statistical shape model of the right tibia, which served as a template. Fracture lines along with footprints of superficial and deep PITFL were marked on the template. 3D fracture heat maps were generated. Size of the fracture fragments and involvement of the superficial and deep PITFL footprints were quantified using a custom MATLAB script (Figure 1). Sparing of the footprint was defined as an overlap of &lt; 1% between the fracture line and the footprint areas. Odds ratios (OR) with 95% confidence intervals (CI) were determined to determine which variables correlated with sparing of the PITFL footprint; P-values of &lt; 0.05 were considered significant. Results: Thirty-nine patients were included. The superficial PITFL footprint was spared in 15 (38%), deep PITFL in 10 (26%), and both in 4 cases (10%). Males and Weber C fractures had a higher likelihood of sparing the superficial and deep PITFL footprints, respectively (P = 0.04). Supination external rotation (SER) patterns were less likely to demonstrate syndesmotic widening if either PITFL footprint was spared. Direct fixation of the PMF was done in 1 case; syndesmotic fixation in 25 cases and in 14 cases, no syndesmotic fixation was done. Of these, 11 were SER injuries where stability was achieved after fixation of medial and lateral malleoli. In 1 SER and pronation external rotation (PER) injury case, the syndesmosis was stable after fixation of a large Chaput fragment. Conclusion: This study demonstrated that 48% of small shell PMFs spare either the superficial or deep footprint of the PITFL; in 10% both PITFL footprints were spared. Hence, 58 % of small-shell PMFs may not benefit from direct fixation. Additionally, SER injuries with small shell PMFs that spare either PITFL footprint may not demonstrate radiographic instability and may not need direct or indirect fixation after addressing other components of the ankle fracture. However, given the fact that syndesmotic stability is not dictated by the PITFL alone, it remains prudent to stress the syndesmosis per-operatively to determine if syndesmotic fixation is needed.

Artificial Intelligence–Triaged 3-Dimensional Pathology to Improve Detection of Esophageal Neoplasia While Reducing Pathologist Workloads

Early detection of esophageal neoplasia via evaluation of endoscopic surveillance biopsies is the key to maximizing survival for patients with Barrett’s esophagus, but it is hampered by the sampling limitations of conventional slide-based histopathology. Comprehensive evaluation of whole biopsies with 3-dimensional (3D) pathology may improve early detection of malignancies, but large 3D pathology data sets are tedious for pathologists to analyze. Here, we present a deep learning-based method to automatically identify the most critical 2-dimensional (2D) image sections within 3D pathology data sets for pathologists to review. Our method first generates a 3D heatmap of neoplastic risk for each biopsy, then classifies all 2D image sections within the 3D data set in order of neoplastic risk. In a clinical validation study, we diagnose esophageal biopsies with artificial intelligence–triaged 3D pathology (3 images per biopsy) vs standard slide-based histopathology (16 images per biopsy) and show that our method improves detection sensitivity while reducing pathologist workloads.

Rapid assessment of 3-dimensional intra-tumor heterogeneity through cycling temperature capillary electrophoresis

ObjectiveTumors are heterogeneous three-dimensional masses populated by numerous cell types, including distinct sub-clones of cancerous cells. Various sub-clones within the same tumor mass may respond differently to cancer treatment, and intra-tumor heterogeneity contributes to acquired therapeutic resistance. Thus, one tissue biopsy will in most cases not be representative of the entire genetic landscape of a tumor mass. In this study, we aimed to establish an easily accessible, low cost method to address intra-tumor heterogeneity in three dimensions, for a limited number of DNA alterations.ResultsThis study includes analyses of the three-dimensional (3D) distribution of DNA mutations in human colon cancer and mouse mammary gland tumor tissue samples. We used laser capture microdissection for the unbiased collection of tissue in several XY-planes throughout the tumor masses. Cycling temperature capillary electrophoresis was used to determine mutant allele frequency. High-resolution distribution maps of KRAS and Trp53 mutations were generated for each XY-plane in human and mouse tumor samples, respectively. To provide a holistic interpretation of the mutation distribution, we generated interactive 3D heatmaps giving an easily interpretable understanding of the spatial distribution of the analyzed mutations. The method described herein provides an accessible way of describing intra-tumor heterogeneity for a limited number of mutations.

Enhancing Planar Inverted Perovskite Solar Cells with Innovative Dumbbell‐Shaped HTMs: A Study of Hexabenzocoronene and Pyrene‐BODIPY‐Triarylamine Derivatives

AbstractDumbbell‐shaped systems based on PAHs‐BODIPY‐triarylamine hybrids TM‐(01‐04) are designed as novel and highly efficient hole‐transporting materials for usage in planar inverted perovskite solar cells. BODIPY is employed as a bridge between the PAH units, and the effects of the conjugated π‐system's covalent attachment and size are investigated. Fluorescence quenching, 3D fluorescence heat maps, and theoretical studies support energy transfer within the moieties. The systems are extremely resistant to UVC 254 nm germicidal light sources and present remarkable thermal stability at degradation temperatures exceeding 350 °C. Integrating these systems into perovskite solar cells results in outstanding power conversion efficiency (PCE), with TM‐02‐based devices exhibiting a PCE of 20.26%. The devices base on TM‐01, TM‐03, and TM‐04 achieve PCE values of 16.98%, 17.58%, and 18.80%, respectively. The long‐term stability of these devices is measured for 600 h, with initial efficiency retention between 94% and 86%. The TM‐04‐based device presents noticeable stability of 94%, better than the reference polymer PTAA with 91%. These findings highlight the exciting potential of dumbbell‐shaped systems based on PAHs‐BODIPY‐triarylamine derivatives for next‐generation photovoltaics.

Automatic Schelling Point Detection From Meshes.

Mesh Schelling points explain how humans focus on specific regions of a 3D object. They have a large number of important applications in computer graphics and provide valuable information for perceptual psychology studies. However, detecting mesh Schelling points is time-consuming and expensive since the existing techniques are mostly based on participant observation studies. To overcome these limitations, we propose to employ powerful deep learning techniques to detect mesh Schelling points in an automatic manner, free from participant observation studies. Specifically, we utilize the mesh convolution and pooling operations to extract informative features from mesh objects, and then predict the 3D heat map of Schelling points in an end-to-end manner. In addition, we propose a Deep Schelling Network (DS-Net) to automatically detect the Schelling points, including a multi-scale fusion component and a novel region-specific loss function to improve our network for a better regression of heat maps. To the best of our knowledge, DS-Net is the first deep neural network for detecting Schelling points from 3D meshes. We evaluate DS-Net on a mesh Schelling point dataset obtained from participant observation studies. The experimental results demonstrate that DS-Net is capable of detecting mesh Schelling points effectively and outperforms various state-of-the-art mesh saliency methods and deep learning models, both qualitatively and quantitatively.

Focus on software, data acquisition, and instrumentation

Focus on software, data acquisition, and instrumentation

3-D HANet: A Flexible 3-D Heatmap Auxiliary Network for Object Detection

3D object detection is a vital part of outdoor scene perception. Learning the complete size and accurate positioning of objects from an incomplete point cloud spatial structure is essential to 3D object detection. We propose a novel flexible 3D heatmap auxiliary network for object detection (3D HANet). To obtain complete structure and location information from an incomplete point cloud structure, we propose a 3D heatmap to reflect object information. Also, we design a plug-and-play auxiliary network based on 3D heatmap, which improves the accuracy of the entire detection network without extra computation in the inference stage. We validate the 3D heatmap auxiliary network on the basis of three classic 3D object detection networks: PointPillars, SECOND and SASSD. Experimental results show that our auxiliary network augments the feature extraction ability of the backbone network, which is manifested in that the predicted boxes and the ground truth boxes are more suitable in size and more aligned in direction. Furthermore, we conducted verification experiments on the state-of-the-art detector, CasA, and made a further improvement on the official ranking of KITTI dataset.