Proposal of a domain model for 3D representation of buildings in Ecuador.

The accelerated urban sprawl of cities around the world presents major challenges for urban planning and land resource management. In this context, it is crucial to have a detailed 3D representation of buildings enriched with accurate alphanumeric information. A distinctive aspect of this proposal is its specific focus on the spatial unit corresponding to buildings. In order to propose a domain model for the 3D representation of buildings, the national standard of Ecuador and the international standard (ISO 19152:2012 LADM) were considered. The proposal includes a detailed specification of attributes, both for the general subclass of buildings and for their infrastructure. The application of the domain model proposal was crucial in a study area located in the Riobamba canton, due to the characteristics of the buildings in that area. For this purpose, a geodatabase was created in pgAdmin4 with official information, taking into account the structure of the proposed model and linking it with geospatial data for an adequate management and 3D representation of the buildings in an open-source Geographic Information System. This application improves cadastral management in the study region and has wider implications. This model is intended to serve as a benchmark for other countries facing similar challenges in cadastral management and 3D representation of buildings, promote efficient urban development and contribute to global sustainable development.

Axially swept open-top light-sheet microscopy for densely labeled clinical specimens.

Open-top light-sheet (OTLS) microscopy offers rapid 3D imaging of large optically cleared specimens. This enables nondestructive 3D pathology, which provides key advantages over conventional slide-based histology including comprehensive sampling without tissue sectioning/destruction and visualization of diagnostically important 3D structures. With 3D pathology, clinical specimens are often labeled with small-molecule stains that broadly target nucleic acids and proteins, mimicking conventional hematoxylin and eosin (H&E) dyes. Tight optical sectioning helps to minimize out-of-focus fluorescence for high-contrast imaging in these densely labeled tissues but has been challenging to achieve in OTLS systems due to trade-offs between optical sectioning and field of view. Here we present an OTLS microscope with voice-coil-based axial sweeping to circumvent this trade-off, achieving 2 µm axial resolution over a 750 × 375 µm field of view. We implement our design in a non-orthogonal dual-objective (NODO) architecture, which enables a 10-mm working distance with minimal sensitivity to refractive index mismatches, for high-contrast 3D imaging of clinical specimens.

The best of two worlds: reprojecting 2D image annotations onto 3D models.

Imagery has become one of the main data sources for investigating seascape spatial patterns. This is particularly true in deep-sea environments, which are only accessible with underwater vehicles. On the one hand, using collaborative web-based tools and machine learning algorithms, biological and geological features can now be massively annotated on 2D images with the support of experts. On the other hand, geomorphometrics such as slope or rugosity derived from 3D models built with structure from motion (sfm) methodology can then be used to answer spatial distribution questions. However, precise georeferencing of 2D annotations on 3D models has proven challenging for deep-sea images, due to a large mismatch between navigation obtained from underwater vehicles and the reprojected navigation computed in the process of building 3D models. In addition, although 3D models can be directly annotated, the process becomes challenging due to the low resolution of textures and the large size of the models. In this article, we propose a streamlined, open-access processing pipeline to reproject 2D image annotations onto 3D models using ray tracing. Using four underwater image datasets, we assessed the accuracy of annotation reprojection on 3D models and achieved successful georeferencing to centimetric accuracy. The combination of photogrammetric 3D models and accurate 2D annotations would allow the construction of a 3D representation of the landscape and could provide new insights into understanding species microdistribution and biotic interactions.

Optimizing immunofluorescence with high-dynamic-range imaging to enhance PD-L1 expression evaluation for 3D pathology assessment from NSCLC tumor tissue.

Assessing programmed death ligand 1 (PD-L1) expression through immunohistochemistry (IHC) is the golden standard in predicting immunotherapy response of non-small cell lung cancer (NSCLC). However, observation of heterogeneous PD-L1 distribution in tumor space is a challenge using IHC only. Meanwhile, immunofluorescence (IF) could support both planar and three-dimensional (3D) histological analyses by combining tissue optical clearing with confocal microscopy. We optimized clinical tissue preparation for the IF assay focusing on staining, imaging, and post-processing to achieve quality identical to traditional IHC assay. To overcome limited dynamic range of the fluorescence microscope's detection system, we incorporated a high dynamic range (HDR) algorithm to restore the post imaging IF expression pattern and further 3D IF images. Following HDR processing, a noticeable improvement in the accuracy of diagnosis (85.7%) was achieved using IF images by pathologists. Moreover, 3D IF images revealed a 25% change in tumor proportion score for PD-L1 expression at various depths within tumors. We have established an optimal and reproducible process for PD-L1 IF images in NSCLC, yielding high quality data comparable to traditional IHC assays. The ability to discern accurate spatial PD-L1 distribution through 3D pathology analysis could provide more precise evaluation and prediction for immunotherapy targeting advanced NSCLC.

Common double-lumen tube selection methods overestimate adequate tube sizes in individual patients - a 3D reconstruction study.

BACKGROUND: Appropriate selection of double-lumen tube sizes for one-lung ventilation is crucial to prevent airway damage. Current selection methods rely on demographic factors or 2D radiography. Prediction of left bronchial diameter is indispensable for choosing the adequate tube size. This prospective observational study investigates if current selection methods sufficiently predict individuals' left bronchial diameters for DLT selection compared to the 3D reconstruction. METHODS: 100 patients necessitating thoracic surgery with one-lung ventilation and left-sided double-lumen tubes, ≥ 18 years of age, and a set of chest X-rays and 2D thorax CT scans for 3D reconstruction of the left main bronchus were included between 07/2021 and 06/2023. The cross-validated prediction error and the width of the 95%-prediction intervals of the 3D left main bronchial diameter utilizing linear prediction models were based on current selection methods. RESULTS: The mean bronchial diameter in 3D reconstruction was 13.6 ± 2.1 mm. The ranges of the 95%-prediction intervals for the bronchial diameter were 6.4 mm for demographic variables, 8.3 mm for the tracheal diameter from the X-ray, and 5.9 mm for bronchial diameter from the 2D-CT scans. Current methods violated the suggested '≥1 mm' safety criterion in up to 7% (men) and 42% (women). Particularly, 2D radiography overestimated women's left bronchial diameter. Current methods even allowed the selection of double-lumen tubes with bronchial tube sections greater than the bronchial diameter in women. CONCLUSIONS: Neither demographic nor 2D-radiographic methods sufficiently account for the variability of the bronchial diameter. Wide 95%-prediction intervals for the bronchial diameter hamper accurate individual double-lumen tube selection. This increases women's risk of bronchial damage, particularly if they have other predisposing factors. These patients may benefit from 3D reconstruction of the left main bronchus. TRIAL REGISTRATION: Not applicable.

Effectiveness of different intrusion modes of maxillary anterior teeth with mini-implants in clear aligner treatment: a three-dimensional finite element analysis.

BACKGROUND: The intrusion of maxillary anterior teeth is often required and there are various intrusion modes with mini-implants in clear aligner treatment. The objective of this study was to evaluate the effectiveness of maxillary anterior teeth intrusion with different intrusion modes, aiming to provide references for precise and safe intrusion movements in clinical practice. METHODS: Cone-beam computed tomography and intraoral optical scanning data of a patient were collected. Finite element models of the maxilla, maxillary dentition, periodontal ligaments (PDLs), clear aligner (CA), attachments, and mini-implants were established. Different intrusion modes of the maxillary anterior teeth were simulated by changing the mini-implant site (between central incisors, between central and lateral incisor, between lateral incisor and canine), loading site (between central incisors, on central incisor, between central and lateral incisor, between lateral incisor and canine), and loading mode (labial loading and labiolingual loading). Ten conditions were generated and intrusive forces of 100 g were applied totally. Then displacement tendency of the maxillary anterior teeth and CA, and stress of the PDLs were analyzed. RESULTS: For the central incisor under condition L14 and for the canine under conditions L11, L13, L23, and L33, the intrusion amount was negative. Under other conditions, the intrusion amount was positive. The labiolingual angulation of maxillary anterior teeth exhibited positive changes under all conditions, with greater changes under linguoincisal loading. The mesiodistal angulation of canine exhibited positive changes under labial loading, while negative changes under linguoincisal loading except for condition L14. CONCLUSIONS: The intrusion amount, labiolingual and mesiodistal angulations of the maxillary anterior teeth were affected by the mini-implant site, loading site, and loading mode. Labial and linguoincisal loading may have opposite effects on the intrusion amount of maxillary anterior teeth and the mesiodistal angulation of canine. The labiolingual angulation of the maxillary incisors would increase under all intrusion modes, with greater increases under linguoincisal loading.

Double vision: 2D and 3D mosquito trajectories can be as valuable for behaviour analysis via machine learning.

BACKGROUND: Mosquitoes are carriers of tropical diseases, thus demanding a comprehensive understanding of their behaviour to devise effective disease control strategies. In this article we show that machine learning can provide a performance assessment of 2D and 3D machine vision techniques and thereby guide entomologists towards appropriate experimental approaches for behaviour assessment. Behaviours are best characterised via tracking-giving a full time series of information. However, tracking systems vary in complexity. Single-camera imaging yields two-component position data which generally are a function of all three orthogonal components due to perspective; however, a telecentric imaging setup gives constant magnification with respect to depth and thereby measures two orthogonal position components. Multi-camera or holographic techniques quantify all three components. METHODS: In this study a 3D mosquito mating swarm dataset was used to generate equivalent 2D data via telecentric imaging and a single camera at various imaging distances. The performance of the tracking systems was assessed through an established machine learning classifier that differentiates male and non-male mosquito tracks. SHAPs analysis has been used to explore the trajectory feature values for each model. RESULTS: The results reveal that both telecentric and single-camera models, when placed at large distances from the flying mosquitoes, can produce equivalent accuracy from a classifier as well as preserve characteristic features without resorting to more complex 3D tracking techniques. CONCLUSIONS: Caution should be exercised when employing a single camera at short distances as classifier balanced accuracy is reduced compared to that from 3D or telecentric imaging; the trajectory features also deviate compared to those from the other datasets. It is postulated that measurement of two orthogonal motion components is necessary to optimise the accuracy of machine learning classifiers based on trajectory data. The study increases the evidence base for using machine learning to determine behaviours from insect trajectory data.

Automation of Wilms' tumor segmentation by artificial intelligence.

BACKGROUND: 3D reconstruction of Wilms' tumor provides several advantages but are not systematically performed because manual segmentation is extremely time-consuming. The objective of our study was to develop an artificial intelligence tool to automate the segmentation of tumors and kidneys in children. METHODS: A manual segmentation was carried out by two experts on 14 CT scans. Then, the segmentation of Wilms' tumor and neoplastic kidney was automatically performed using the CNN U-Net and the same CNN U-Net trained according to the OV2ASSION method. The time saving for the expert was estimated depending on the number of sections automatically segmented. RESULTS: When segmentations were performed manually by two experts, the inter-individual variability resulted in a Dice index of 0.95 for tumor and 0.87 for kidney. Fully automatic segmentation with the CNN U-Net yielded a poor Dice index of 0.69 for Wilms' tumor and 0.27 for kidney. With the OV2ASSION method, the Dice index varied depending on the number of manually segmented sections. For the segmentation of the Wilms' tumor and neoplastic kidney, it varied respectively from 0.97 to 0.94 for a gap of 1 (2 out of 3 sections performed manually) to 0.94 and 0.86 for a gap of 10 (1 section out of 6 performed manually). CONCLUSION: Fully automated segmentation remains a challenge in the field of medical image processing. Although it is possible to use already developed neural networks, such as U-Net, we found that the results obtained were not satisfactory for segmentation of neoplastic kidneys or Wilms' tumors in children. We developed an innovative CNN U-Net training method that makes it possible to segment the kidney and its tumor with the same precision as an expert while reducing their intervention time by 80%.

Dynamic computed tomography angiography for noninvasive diagnosis of bow Hunter's syndrome: a case report.

The focus of this case report is to technically describe a noninvasive diagnostic evaluation of bow Hunter's syndrome using a dynamic computed tomography angiography protocol and discuss its advantages. In addition, we aimed to exemplify the quality of the study by presenting images of a 3D-printed model generated to help plan the surgical treatment for the patient. The dynamic computed tomography angiography protocol consisted of a first image acquisition with the patient in the anatomic position of the head and neck. This was followed by a second acquisition with the head and neck rotated to the side that triggered the symptoms, with technical parameters similar to the first acquisition. The acquired images were used to print a 3D model to better depict the findings for the surgical team. The dynamic computed tomography angiography protocol developed in this study helped visualize the vertebrobasilar arterial anatomy, detect vertebral artery stenosis produced by head and neck rotation, depict the structure responsible for artery stenosis (e.g., bony structure or membranes), and study possible complications of the disease (e.g., posterior cerebral circulation infarction). Additionally, the 3D-printed model better illustrated the findings of stenosis, aiding in surgical planning. In conclusion, dynamic computed tomography angiography for the evaluation of bow Hunter's syndrome is a feasible noninvasive technique that can be used as an alternative to traditional diagnostic methods.

3D airway geometry analysis of factors in airway navigation failure for lung nodules.

BACKGROUND: This study aimed to quantitatively reveal contributing factors to airway navigation failure during radial probe endobronchial ultrasound (R-EBUS) by using geometric analysis in a three-dimensional (3D) space and to investigate the clinical feasibility of prediction models for airway navigation failure. METHODS: We retrospectively reviewed patients who underwent R-EBUS between January 2017 and December 2018. Geometric quantification was analyzed using in-house software built with open-source python libraries including the Vascular Modeling Toolkit ( http://www.vmtk.org ), simple insight toolkit ( https://sitk.org ), and sci-kit image ( https://scikit-image.org ). We used a machine learning-based approach to explore the utility of these significant factors. RESULTS: Of the 491 patients who were eligible for analysis (mean age, 65 years +/- 11 [standard deviation]; 274 men), the target lesion was reached in 434 and was not reached in 57. Twenty-seven patients in the failure group were matched with 27 patients in the success group based on propensity scores. Bifurcation angle at the target branch, the least diameter of the last section, and the curvature of the last section are the most significant and stable factors for airway navigation failure. The support vector machine can predict airway navigation failure with an average area under the curve of 0.803. CONCLUSIONS: Geometric analysis in 3D space revealed that a large bifurcation angle and a narrow and tortuous structure of the closest bronchus from the lesion are associated with airway navigation failure during R-EBUS. The models developed using quantitative computer tomography scan imaging show the potential to predict airway navigation failure.

The contribution of E3D imaging integrated with robotic navigation: analysis of the first 80 consecutive posterior spinal fusion cases.

Eighty consecutive complex spinal robotic cases utilizing intraoperative 3D CT imaging (E3D, Group 2) were compared to 80 age-matched controls using the Excelsius robot alone with C-arm Fluoroscopic registration (Robot Only, Group 1). The demographics between the two groups were similar-severity of deformity, ASA Score for general anesthesia, patient age, gender, number of spinal levels instrumented, number of patients with prior spinal surgery, and amount of neurologic compression. The intraoperative CT scanning added several objective factors improving patient safety. There were significantly fewer complications in the E3D group with only 3 of 80 (4%) patients requiring a return to the operating room compared to 11 of 80 (14%) patients in the Robot Only Group requiring repeat surgery for implant related problems (Chi squared analysis = 5.00, p = 0.025). There was a significant reduction the amount of fluoroscopy time in the E3D Group (36 s, range 4-102 s) compared to Robot only group (51 s, range 15-160 s) (p = 0.0001). There was also shorter mean operative time in the E3D group (257 ± 59.5 min) compared to the robot only group (306 ± 73.8 min) due to much faster registration time (45 s). A longer registration time was required in the Robot only group to register each vertebral level with AP and Lateral fluoroscopy shots. The estimated blood loss was also significantly lower in Group 2 (mean 345 ± 225 ml) vs Group 1 (474 ± 397 ml) (p = 0.012). The mean hospital length of stay was also significantly shorter for Group 2 (3.77 ± 1.86 days) compared to Group 1 (5.16 ± 3.40) (p = 0.022). There was no significant difference in the number of interbody implants nor corrective osteotomies in both groups-Robot only 52 cases vs. 42 cases in E3D group.Level of evidence: IV, Retrospective review.

Development and clinical validation of a deep learning-based knee CT image segmentation method for robotic-assisted total knee arthroplasty.

BACKGROUND: This study aimed to develop a novel deep convolutional neural network called Dual-path Double Attention Transformer (DDA-Transformer) designed to achieve precise and fast knee joint CT image segmentation and to validate it in robotic-assisted total knee arthroplasty (TKA). METHODS: The femoral, tibial, patellar, and fibular segmentation performance and speed were evaluated and the accuracy of component sizing, bone resection and alignment of the robotic-assisted TKA system constructed using this deep learning network was clinically validated. RESULTS: Overall, DDA-Transformer outperformed six other networks in terms of the Dice coefficient, intersection over union, average surface distance, and Hausdorff distance. DDA-Transformer exhibited significantly faster segmentation speeds than nnUnet, TransUnet and 3D-Unet (p < 0.01). Furthermore, the robotic-assisted TKA system outperforms the manual group in surgical accuracy. CONCLUSIONS: DDA-Transformer exhibited significantly improved accuracy and robustness in knee joint segmentation, and this convenient and stable knee joint CT image segmentation network significantly improved the accuracy of the TKA procedure.

Confocal Imaging of Seeds.

In flowering plants, proper seed development is achieved through the constant interplay of fertilization products, embryo and endosperm, and maternal tissues. Understanding such a complex biological process requires microscopy techniques able to unveil the seed internal morphological structure. Seed thickness and relatively low permeability make conventional tissue staining techniques impractical unless combined with time-consuming dissecting methods. Here, we describe two techniques to imaging the three-dimensional structure of Arabidopsis seeds by confocal laser scanning microscopy. Both procedures, while differing in their time of execution and resolution, are based on cell wall staining of seed tissues with fluorescent dyes.

Efficient musculoskeletal annotation using free-form deformation.

Traditionally, constructing training datasets for automatic muscle segmentation from medical images involved skilled operators, leading to high labor costs and limited scalability. To address this issue, we developed a tool that enables efficient annotation by non-experts and assessed its effectiveness for training an automatic segmentation network. Our system allows users to deform a template three-dimensional (3D) anatomical model to fit a target magnetic-resonance image using free-form deformation with independent control points for axial, sagittal, and coronal directions. This method simplifies the annotation process by allowing non-experts to intuitively adjust the model, enabling simultaneous annotation of all muscles in the template. We evaluated the quality of the tool-assisted segmentation performed by non-experts, which achieved a Dice coefficient greater than 0.75 compared to expert segmentation, without significant errors such as mislabeling adjacent muscles or omitting musculature. An automatic segmentation network trained with datasets created using this tool demonstrated performance comparable to or superior to that of networks trained with expert-generated datasets. This innovative tool significantly reduces the time and labor costs associated with dataset creation for automatic muscle segmentation, potentially revolutionizing medical image annotation and accelerating the development of deep learning-based segmentation networks in various clinical applications.

Virtual tissue microstructure reconstruction across species using generative deep learning.

Analyzing tissue microstructure is essential for understanding complex biological systems in different species. Tissue functions largely depend on their intrinsic tissue architecture. Therefore, studying the three-dimensional (3D) microstructure of tissues, such as the liver, is particularly fascinating due to its conserved essential roles in metabolic processes and detoxification. Here, we present TiMiGNet, a novel deep learning approach for virtual 3D tissue microstructure reconstruction using Generative Adversarial Networks and fluorescence microscopy. TiMiGNet overcomes challenges such as poor antibody penetration and time-intensive procedures by generating accurate, high-resolution predictions of tissue components across large volumes without the need of paired images as input. We applied TiMiGNet to analyze tissue microstructure in mouse and human liver tissue. TiMiGNet shows high performance in predicting structures like bile canaliculi, sinusoids, and Kupffer cell shapes from actin meshwork images. Remarkably, using TiMiGNet we were able to computationally reconstruct tissue structures that cannot be directly imaged due experimental limitations in deep dense tissues, a significant advancement in deep tissue imaging. Our open-source virtual prediction tool facilitates accessible and efficient multi-species tissue microstructure analysis, accommodating researchers with varying expertise levels. Overall, our method represents a powerful approach for studying tissue microstructure, with far-reaching applications in diverse biological contexts and species.

3D models for mohs micrographic surgery: a review on its use in patient education.

The use of a 3D model for patient education has shown encouraging results in surgical specialties like plastic surgery and neurosurgery, amongst many others; however, there is limited research on the clinical application of 3D models for Mohs Micrographic Surgery. This study delves into the utilization of 3D models for patient education in Mohs Surgery by juxtaposing different 3D modalities, highlighting their differences, and exploring potential avenues for future integration of 3D models into clinical practice. A literature search in the scientific database MEDLINE through PubMed and OVID and on the ProQuest Health & Medical Collection database was performed on the use of a 3D model for patient education. We limited the search to articles available in English and considered those mentioning the educational use of 3D models, especially for patient education, after excluding duplicate titles. We did not exclude articles based on publication year due to limited availability of literature. Utilizing 3D models for patient education within the framework of Mohs Micrographic surgery, including a 3D multicolored clay model and a 3D model accompanied by an educational video intervention, presents substantial advantages. 3D models offer a visual and tactile means to improve patients' comprehension of the Mohs procedure, the affected area, and possible outcomes. They hold the potential to reduce patient anxiety and improve decision-making. Currently, literature on the use of 3D models for patient education in Mohs Micrographic Surgery is limited, warranting further research in this area.

Deep 3D histology powered by tissue clearing, omics and AI.

To comprehensively understand tissue and organism physiology and pathophysiology, it is essential to create complete three-dimensional (3D) cellular maps. These maps require structural data, such as the 3D configuration and positioning of tissues and cells, and molecular data on the constitution of each cell, spanning from the DNA sequence to protein expression. While single-cell transcriptomics is illuminating the cellular and molecular diversity across species and tissues, the 3D spatial context of these molecular data is often overlooked. Here, I discuss emerging 3D tissue histology techniques that add the missing third spatial dimension to biomedical research. Through innovations in tissue-clearing chemistry, labeling and volumetric imaging that enhance 3D reconstructions and their synergy with molecular techniques, these technologies will provide detailed blueprints of entire organs or organisms at the cellular level. Machine learning, especially deep learning, will be essential for extracting meaningful insights from the vast data. Further development of integrated structural, molecular and computational methods will unlock the full potential of next-generation 3D histology.

An Automatic Method for Elbow Joint Recognition, Segmentation and Reconstruction.

Elbow computerized tomography (CT) scans have been widely applied for describing elbow morphology. To enhance the objectivity and efficiency of clinical diagnosis, an automatic method to recognize, segment, and reconstruct elbow joint bones is proposed in this study. The method involves three steps: initially, the humerus, ulna, and radius are automatically recognized based on the anatomical features of the elbow joint, and the prompt boxes are generated. Subsequently, elbow MedSAM is obtained through transfer learning, which accurately segments the CT images by integrating the prompt boxes. After that, hole-filling and object reclassification steps are executed to refine the mask. Finally, three-dimensional (3D) reconstruction is conducted seamlessly using the marching cube algorithm. To validate the reliability and accuracy of the method, the images were compared to the masks labeled by senior surgeons. Quantitative evaluation of segmentation results revealed median intersection over union (IoU) values of 0.963, 0.959, and 0.950 for the humerus, ulna, and radius, respectively. Additionally, the reconstructed surface errors were measured at 1.127, 1.523, and 2.062 mm, respectively. Consequently, the automatic elbow reconstruction method demonstrates promising capabilities in clinical diagnosis, preoperative planning, and intraoperative navigation for elbow joint diseases.