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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.

Internal erosion is a phenomenon that fine particles migrate through the channels within coarse matrix under seepage flow. Previous studies mainly focused on the internal erosion under steady hydraulic gradient, while the studies on the behavior and mesoscopic mechanism of internal erosion under fluctuating hydraulic condition were rarely reported. In this study, a setup of transparent soil seepage test was developed. Seepage tests on two types of soils with different internal stability were conducted under steady and fluctuating hydraulic conditions respectively to investigate the internal erosion behaviors under fluctuating hydraulic condition. The macroscopic experimental phenomenon showed that when the hydraulic gradient exceeded the critical value, the hydraulic conductivity under fluctuating hydraulic condition increased faster than that under steady hydraulic condition for internally unstable soil, manifesting that hydraulic fluctuation aggravated the migration of fine particles. In order to further reveal the mesoscopic mechanism, a three-dimensional (3D) reconstruction method was employed to rebuild the mesoscopic fabric of soil based on two-dimensional cross-sectional images obtained by the planar laser scanning; and a 3D visualization digital model including coarse matrix, inter-granular pore channels and fine particles was established. By observing the distribution of fine particles in pore channels, it was found that fine particles might clog and accumulate at narrow pore throats under steady seepage. While the perturbation caused by hydraulic fluctuation could break these weak stable structures of clog and accumulation, and then restart the migration process of fine particles.

BackgroundIn addition to CT images and pathological features, many other molecular characteristics remain unknown about multiple primary lung cancer (MPLC) from intrapulmonary metastatic lung cancer.Case presentationIn this study, we reported a patient with an early-stage MPLC with both adenocarcinoma in situ (AIS) subtype and minimally invasive adenocarcinoma (MIA) subtype. The patient was diagnosed with more than 10 nodules and underwent precise surgery assisted by three-dimensional (3D) reconstruction at the left upper lung lobe. Whole-exome sequencing (WES) and multiple immunohistochemistry (mIHC) were performed to reveal the genomic profiling and tumor microenvironments of multiple nodules in this patient with MPLC. Based on 3D reconstruction location information, we found that the genomic and pathological results of adjacent lymph nodes were quite different. On the other hand, PD-L1 expression and the proportion of infiltrating lymphocytes in tumor microenvironments were all at a low status and did not vary in adjacent lymph nodes. Additionally, maximum diameter and tumor mutational burden levels were found to be significantly associated with CD8+ T cell proportion (p

The three-dimensional (3D) pore structures and permeability of shale are critical for forecasting gas production capacity and guiding pressure differential control in practical reservoir extraction. However, few investigations have analyzed the effects of microscopic organic matter (OM) morphology and 3D pore nanostructures on the stress sensitivity, which are precisely the most unique and controlling factors of reservoir quality in shales. In this study, ultra-high nanoscale-resolution imaging experiments, i.e. focused ion beam-scanning electron microscopy (FIB-SEMs), were conducted on two organic-rich shale samples from Longmaxi and Wufeng Formations in northern Guizhou Depression, China. Pore morphology, porosity of 3D pore nanostructures, pore size distribution, and connectivity of the six selected regions of interest (including clump-shaped OMs, interstitial OMs, framboidal pyrite, and microfractures) were qualitatively and quantitatively characterized. Pulse decay permeability (PDP) measurement was used to investigate the variation patterns of stress-dependent permeability and stress sensitivity of shales under different confining pressures and pore pressures, and the results were then used to calculate the Biot coefficients for the two shale formations. The results showed that the samples have high OM porosity and 85% of the OM pores have the radius of less than 40 nm. The OM morphology and pore structure characteristics of the Longmaxi and Wufeng Formations were distinctly different. In particular, the OM in the Wufeng Formation samples developed some OM pores with radius larger than 500 nm, which significantly improved the connectivity. The macroscopic permeability strongly depends on the permeability of OM pores. The stress sensitivity of permeability of Wufeng Formation was significantly lower than that of Longmaxi Formation, due to the differences in OM morphology and pore structures. The Biot coefficients of 0.729 and 0.697 were obtained for the Longmaxi and Wufeng Formations, respectively.

Abstract It is difficult to estimate the effective rotation velocity for non‐triaxial stabilized space targets, which leads to deviations in the estimation of imaging plane vectors and azimuth resolution. This makes it hard to reconstruct the three‐dimensional (3D) of space targets based on inverse synthetic aperture radar (ISAR) image sequence. To solve this problem, a joint estimation method of imaging plane vector and 3D structure based on ISAR image sequences is proposed. First, a compact form of imaging plane vector is defined. Then, the 3D structure of a target is characterized by a fully connected deep network. The volume rendering for ISAR images is redesigned with the analysis of projection formula for ISAR imaging. The network is trained using the discrepancy between the modulus of rendered and observed ISAR images in a self‐supervised manner without 3D supervision. And the 3D structure can be obtained by inquiring all points in the 3D space. Therefore, it can optimize imaging planes and produce more complete and accurate results for complex space targets. Adequate simulation experiments verify its superiority.

High-speed railway in China has undergone rapid development in recent years. Technology for structure measurement is recognized as an important aspect of steel rail quality inspection. The shape of the rail welding base is mainly gauged using mechanical contact measurement technology. Matching this technology with the escalating demands for quality inspection of steel rails is a challenging task. In this paper, a structured light measurement approach is proposed and employed which intersects the rail through the structural light plane projected by inner and outer laser sensors. These sensors form a laser light stripe curve containing the profile information of the rail surface. By processing laser light bars using image analysis and contour reduction techniques, 3-D profiles of the rails are generated. The laser plane is fitted using the coordinate points of the laser light bars converted between the image and world coordinate systems. A new calibration method based on the parallel moving target for line-structured light is proposed to improve the calibration and address the issue caused by the limited number of the extracted calibration points during free target calibration. The rail profile, including the geometric dimension, straightness, and twist of the rail, are then measured. By comparing the measured profile with the standard contour of the rails, the rail wear can then be quantitatively assessed. The experimental results show that the proposed measurement system outperforms the traditional single index detection.

An efficient and accurate forest sample plot survey is of great significance to understand the current status of forest resources at the stand or regional scale and the basis of scientific forest management. Close-range photogrammetry (CRP) technology can easily and quickly collect sequence images with high overlapping to reconstruct the 3D model of forest scenes and extract the individual tree parameters automatically and, therefore, can greatly improve the efficiency of forest investigation and has great application potential in forestry visualization management. However, it has some issues in practical forestry applications. First, the imaging quality is affected by the illumination in the forest, resulting in difficulty in feature matching and low accuracy of parameter extraction. Second, the efficiency of 3D forest model reconstruction is limited under complex understory vegetation or the topographic situation in the forest. In addition, the density of point clouds by dense matching directly affects the accuracy of individual tree parameter extraction. This research collected the sequence images of sample plots of four tree species by smartphones in Gaofeng Forest Farm in Guangxi and Wangyedian Forest Farm in Mongolia to analyze the effects of image enhancement, feature detection and dense point cloud algorithms on the efficiency of 3D forest reconstruction and accuracy of individual tree parameter extraction, then proposed a strategy of 3D reconstruction and parameter extraction suitable for different forest scenes. First, we compared the image enhancement effects of median–Gaussian (MG) filtering, single-scale retinex (SSR) and multi-scale retinex (MSR) filtering algorithms. Then, an improved algorithm combining Harris corner detection with speeded-up robust features (SURF) feature detection (Harris+SURF) is proposed, and the feature matching effect is compared with that of a scale invariant feature transform (SIFT) operator. Third, according to the morphological characteristics of the trees in the sequence images, we used the iterative interpolation algorithm of a planar triangulation network based on geometric constraints (GC-based IIPTN) to increase the density of point clouds and reconstruct the 3D forest model, and then extract the position and DBH of the individual trees. The results show that MSR image enhancement can significantly increase the number of matched point pairs. The improved Harris+SURF method can reduce the reconstruction time of the 3D forest model, and the GC-based IIPTN algorithm can improve the accuracy of individual tree parameter extraction. The extracted position of the individual tree is the same as the measured position with the bias within 0.2 m. The accuracy of extracted DBH of Eucalyptus grandis, Taxus chinensis, Larix gmelinii and Pinus tabuliformis is 94%, 95%, 96% and 90%, respectively, which proves that the proposed 3D model reconstruction method based on image enhancement has great potential for tree position and DBH extraction, and also provides effective support for forest resource investigation and visualization management in the future.