3D Information Research Articles

Three-dimensional direct lithography of stable quantum dots in hybrid glass

Abstract Semiconductor quantum dots (QDs), as high-performance materials, play an essential role in contemporary industry, mainly due to their high photoluminescent quantum yield, wide absorption characteristics, and size-dependent light emission. It’s essential to construct well-defined micro-/nano- structures using QDs as building blocks for micro-optics applications. However, the fabrication of stable QDs with designed functional structures has long been challenging. Here, we propose a strategy for three-dimensional direct lithography of desired QDs within a hybrid medium with specific protection properties. The acrylate-functionalized hybrid precursors enable local crosslinking through ultrafast laser-induced multiphoton absorption, achieving sub-100 nm resolution surpassing the diffraction limit. The printed micro-/nano- structures possess thermal stability up to 600°C, which can be transformed to inorganic architectures with a volume shrinkage. Due to the encapsulated QDs within the densely silicon-oxygen molecular networks, the functional structures demonstrate good stability against ultraviolet irradiation, corrosive solutions, and elevated temperatures. Based on hybrid 3D nanolithography, bicolor multilayer micro-/nano- structures are manufactured for applications in 3D data storage and optical information encryption. This research presents an effective strategy for the fabrication of desired QD micro-/nano- structures, supporting the development of stable functional device applications.

Reconstruction of Local Three-dimensional Temperature Field of Tumor Cells with Low-toxic Nanoscale Quantum-dot Thermometer and Cepstrum Spatial Localization Algorithm.

The optimal method for three-dimensional thermal imaging within cells involves collecting intracellular temperature responses while simultaneously obtaining corresponding 3D positional information. Current temperature measurement techniques based on the photothermal properties of quantum dots face several limitations, including high cytotoxicity and low fluorescence quantum yields. These issues affect the normal metabolic processes of tumor cells. This study synthesizes a low-toxicity cell membrane-targeted quantum dot temperature sensor by optimizing the synthesis method of CdTe/CdS/ZnS core-shell structured quantum dots. Compared to CdTe-targeted quantum dot temperature sensors, the cytotoxicity of CdTe/CdS/ZnS-targeted quantum dot temperature sensors is reduced by 40.79%. Additionally, a novel cepstrum-based spatial localization algorithm is proposed to achieve rapidly compute the three-dimensional positions of densely distributed quantum dot temperature sensors. Ultimately, both targeted and non-targeted CdTe/CdS/ZnS quantum dot temperature sensors were used simultaneously to label the internal and external regions of human osteosarcoma cells to obtain temperature data at these labeling positions. By combining this with the cepstrum-based spatial localization algorithm, the spatial coordinates of the quantum dot temperature sensors were obtained. Three-dimensional temperature field reconstruction of three local regions was achieved within a 12 μm axial range in living cells. The method described in this paper can be widely applied to the quantitative study of intracellular thermal responses.

Fragmenstein: predicting protein–ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding–based methodology

Current strategies centred on either merging or linking initial hits from fragment-based drug design (FBDD) crystallographic screens generally do not fully leaverage 3D structural information. We show that an algorithmic approach (Fragmenstein) that ‘stitches’ the ligand atoms from this structural information together can provide more accurate and reliable predictions for protein–ligand complex conformation than general methods such as pharmacophore-constrained docking. This approach works under the assumption of conserved binding: when a larger molecule is designed containing the initial fragment hit, the common substructure between the two will adopt the same binding mode. Fragmenstein either takes the atomic coordinates of ligands from a experimental fragment screen and combines the atoms together to produce a novel merged virtual compound, or uses them to predict the bound complex for a provided molecule. The molecule is then energy minimised under strong constraints to obtain a structurally plausible conformer. The code is available at https://github.com/oxpig/Fragmenstein.Scientific contributionThis work shows the importance of using the coordinates of known binders when predicting the conformation of derivative molecules through a retrospective analysis of the COVID Moonshot data. This method has had a prior real-world application in hit-to-lead screening, yielding a sub-micromolar merger from parent hits in a single round. It is therefore likely to further benefit future drug design campaigns and be integrated in future pipelines.Graphical

G-SET-DCL: a guided sequential episodic training with dual contrastive learning approach for colon segmentation.

This article introduces a novel deep learning approach to substantially improve the accuracy of colon segmentation even with limited data annotation, which enhances the overall effectiveness of the CT colonography pipeline in clinical settings. The proposed approach integrates 3D contextual information via guided sequential episodic training in which a query CT slice is segmented by exploiting its previous labeled CT slice (i.e., support). Segmentation starts by detecting the rectum using a Markov Random Field-based algorithm. Then, supervised sequential episodic training is applied to the remaining slices, while contrastive learning is employed to enhance feature discriminability, thereby improving segmentation accuracy. The proposed method, evaluated on 98 abdominal scans of prepped patients, achieved a Dice coefficient of 97.3% and a polyp information preservation accuracy of 98.28%. Statistical analysis, including 95% confidence intervals, underscores the method's robustness and reliability. Clinically, this high level of accuracy is vital for ensuring the preservation of critical polyp details, which are essential for accurate automatic diagnostic evaluation. The proposed method performs reliably in scenarios with limited annotated data. This is demonstrated by achieving a Dice coefficient of 97.15% when the model was trained on a smaller number of annotated CT scans (e.g., 10 scans) than the testing dataset (e.g., 88 scans). The proposed sequential segmentation approach achieves promising results in colon segmentation. A key strength of the method is its ability to generalize effectively, even with limited annotated datasets-a common challenge in medical imaging.

Transcription factor prediction using protein 3D secondary structures.

Transcription factors (TFs) are DNA-binding proteins that regulate gene expression. Traditional methods predict a protein as a TF if the protein contains any DNA-binding domains (DBDs) of known TFs. However, this approach fails to identify a novel TF that does not contain any known DBDs. Recently proposed TF prediction methods do not rely on DBDs. Such methods use features of protein sequences to train a machine learning model, and then use the trained model to predict whether a protein is a TF or not. Because the 3-dimensional (3D) structure of a protein captures more information than its sequence, using 3D protein structures will likely allow for more accurate prediction of novel TFs. We propose a deep learning-based TF prediction method (StrucTFactor), which is the first method to utilize 3D secondary structural information of proteins. We compare StrucTFactor with recent state-of-the-art TF prediction methods based on ∼525 000 proteins across 12 datasets, capturing different aspects of data bias (including sequence redundancy) possibly influencing a method's performance. We find that StrucTFactor significantly (p-value<0.001) outperforms the existing TF prediction methods, improving the performance over its closest competitor by up to 17% based on Matthews correlation coefficient. Data and source code are available at https://github.com/lieboldj/StrucTFactor and on our website at https://apps.cosy.bio/StrucTFactor/. Included.

The vehicle routing problem as applied to residential solid waste collection operations: Systematic literature review

The accelerated growth of cities, population increase and economic development have leveraged waste generation globally. This trend is expected to continue, with a significant increase projected in the coming years. Therefore, efficient waste management has become a crucial concern for local, national and international authorities. Transportation plays a key role in waste collection and disposal, being directly related to traffic congestion, fuel consumption and environmental pollution. Despite the existing studies on household waste collection, there is a gap in the literature regarding routing for residential waste collection in medium-sized cities, especially in emerging and frontier developing countries. Therefore, this study seeks through the science tree metaphor and PRISMA methodology, to find studies focused on the vehicle routing problem in waste collection operations, considering aspects such as Modeling approaches and solution techniques, applied Vehicle Routing Problems variants, objective functions, decision variables and constraints, applications in real environments, applied algorithms, and studies considering uncertainty and real conditions. A methodological outline of Vehicle Routing Problems in waste collection operations is presented, where central research topics are identified such as processes developed with Geographic Information System and their integration with exact methods, time windows, multi-objective capacitated vehicle routing problems, the application of stochastic models consider the uncertainty in waste collection, which has allowed including future prediction and optimization as prediction models, based on neural networks, to foresee uncertain conditions of the operations. This article analyzes the evolution in the optimization of municipal solid waste collection routes since 1964, highlighting the transition from iterative models to advanced technologies and multi-objective approaches. The importance of tools such as 3D Geographic Information System and heuristic/metaheuristic algorithms in improving planning and efficiency, despite limitations in the face of uncertainty, is emphasized. The systematic review shows a trend towards sustainable and efficient solutions, indicating future directions for research in urban waste management.

Learning‐Based Progression Detection of Alzheimer’s Disease Using 3D MRI Images

Alzheimer’s disease (AD) is an irreversible brain disease. In addition to the functional deterioration of memory and cognition, patients with severe conditions lose their self‐care ability. Patients exhibiting symptoms are often attributed to aging and thus lack proper medical care. If it can be diagnosed early, the doctor can provide adequate treatments to mitigate the symptoms. Magnetic resonance imaging (MRI) can reflect the characteristics of different human tissues and organs, and is a common tool implemented in clinical examinations. In this study, we tested learning‐based approaches to detect disease progression in AD patients using MRI. Specifically, each patient is categorized as one of the following four classes: cognitive normal, early mild cognitive impairment, late mild cognitive impairment, and AD. To extract 3D information in MRI, we proposed a 3D convolutional neural network structure based on ResNet3D‐18. We designed various multiclass classification frameworks. Moreover, we implemented ensemble classification combining these frameworks. Experiments demonstrated great potential for learning‐based approaches on the Alzheimer’s Disease Neuroimaging Initiative dataset. The ensemble approach performed the best with an accuracy of 0.950, which is competitive with neurologists in diagnosing AD progression in clinical practice. With precise detection, patients can understand their conditions early and seek proper treatments.

Ensemble learning approach for accurate virtual borehole prediction in 3D geological modeling

ABSTRACT The use of virtual drilling technology can effectively accelerate the establishment process of 3D geological models and improve grid structure and visual performance. However, most existing virtual drilling prediction techniques mainly rely on traditional interpolation methods, which not only increase computational overhead but also lack sufficient automation. To address this problem, this study introduces an innovative virtual borehole prediction technology combined with a machine learning stacking strategy. This technology integrates Random Forest (RF), XGBoost, CatBoost, and LightGBM algorithms as basic models and improves prediction accuracy through stacked generalization technology. This study adopted a method of adding zero-thickness layers to unify stratigraphic sequences. The 3D position information of boreholes is used as model input, and the bottom height of boreholes at stratigraphic boundaries is used as the prediction target. Through a hierarchical training method using 85 % of borehole data for training and 15 % for verification, a regression prediction model for stratigraphic boundaries is established. The model is able to predict detailed stratigraphic sequences containing information on stratigraphic type and thickness to accurately simulate virtual boreholes. Research results show that the integrated model has excellent prediction performance, achieves efficient automated prediction, and provides a new solution for virtual drilling prediction.

Inferring 3D finger bone shapes from 2D images – a detailed analysis of shape accuracy

ABSTRACT 3D visualisation and modelling of anatomical structures of the human body play a significant role in diagnosis, computer-aided surgery, surgical planning, and patient follow-up. However, 2D X-ray images are often used in clinical routine. We propose and validate a method for reconstructing 3D shapes from 2D X-ray scans. This method comprises automatic segmentation and labelling, automated construction of 3D statistical shape models (SSM), and automatic fitting of the SSM to standard 2D X-ray images. This workflow is applied to finger bone shape reconstruction and validated for each finger bone using a set of five synthetic reference configurations and 34 CT/X-ray data pairs. We reached submillimetre accuracy for 91.59% of the synthetic data, while 79.65% of the clinical cases show surface errors below 2 mm. Thus, applying the proposed method can add valuable 3D information where 3D imaging is not indicated. Moreover, 3D imaging can be avoided if the 2D-3D reconstruction accuracy is sufficient.

Permeability and pore pressure prediction from well logs using machine learning: A study in the Niger delta

Machine learning provides a robust method for characterizing reservoirs in the Niger Delta. This study applied machine learning techniques to well log data to predict permeability and pore pressure. Feature selection identified depth, density, velocity, and porosity as critical variables, while resistivity and neutron porosity (NPHI) showed strong correlations with pore pressure (correlation coefficients: 0.5–1.0). Random forest and gradient boosting emerged as the most effective models, achieving R-squared scores above 0.99 for both permeability and pore pressure predictions. This corresponded to a root mean squared error (RMSE) under 20,000, indicating a precise fit between predicted and actual values. Although the Decision Tree model also performed well (R-squared &gt; 0.99), further optimization could improve its RMSE and generalization. These results highlight the potential of machine learning to enhance reservoir characterization and inform decision-making in oil and gas exploration and production. Accurate predictions of reservoir properties can optimize operations and reduce uncertainties. Future work could expand these findings by integrating additional data, such as 3D seismic information, and applying the models to diverse geological settings. This would improve the robustness and transferability of predictions, enabling more comprehensive reservoir analysis.

A Compilation of Optical Starlight Polarization Catalogs

Polarimetry of stars at optical and near-infrared wavelengths is an invaluable tool for tracing interstellar dust and magnetic fields. Recent studies have demonstrated the power of combining stellar polarimetry with distances from the Gaia mission, in order to gain accurate, 3D information on the properties of the interstellar magnetic field and the dust distribution. However, access to optical polarization data is limited, as observations are conducted by different investigators, with different instruments, and are made available in many separate publications. To enable a more widespread accessibility of optical polarimetry for studies of the interstellar medium, we compile a new catalog of stellar polarization measurements. The data are gathered from 81 separate publications spanning two decades since the previous, widely used agglomeration of catalogs by C. Heiles. The compilation contains a total of 55,742 measurements of stellar polarization. We combine this database with stellar distances based on the Gaia Early Data Release 3, thereby providing polarization and distance data for 42,482 unique stars. We provide two separate data products: an extended catalog (containing all polarization measurements) and a unique source catalog (containing a subset of sources excluding duplicate measurements). We propose the use of a common tabular format for the publication of stellar polarization catalogs to facilitate accessibility and increase discoverability in the future.

Reliable Disparity Estimation Using Multiocular Vision with Adjustable Baseline.

Accurate estimation of three-dimensional (3D) information from captured images is essential in numerous computer vision applications. Although binocular stereo vision has been extensively investigated for this task, its reliability is conditioned by the baseline between cameras. A larger baseline improves the resolution of disparity estimation but increases the probability of matching errors. This research presents a reliable method for disparity estimation through progressive baseline increases in multiocular vision. First, a robust rectification method for multiocular images is introduced, satisfying epipolar constraints and minimizing induced distortion. This method can improve rectification error by 25% for binocular images and 80% for multiocular images compared to well-known existing methods. Next, a dense disparity map is estimated by stereo matching from the rectified images with the shortest baseline. Afterwards, the disparity map for the subsequent images with an extended baseline is estimated within a short optimized interval, minimizing the probability of matching errors and further error propagation. This process is iterated until the disparity map for the images with the longest baseline is obtained. The proposed method increases disparity estimation accuracy by 20% for multiocular images compared to a similar existing method. The proposed approach enables accurate scene characterization and spatial point computation from disparity maps with improved resolution. The effectiveness of the proposed method is verified through exhaustive evaluations using well-known multiocular image datasets and physical scenes, achieving superior performance over similar existing methods in terms of objective measures.

Circularly Polarized Luminescence in Cellulose-Based Assemblies: Synthesis, Regulation, and Application.

Currently, circularly polarized luminescence (CPL) has drawn wide interest in 3D display, information storage, and optical sensing. However, traditional synthetic paths are often accompanied by low chiral optical intensity and complex processes. Cellulose nanocrystals (CNCs), with the properties of liquid crystals, can spontaneously arrange into the left-handed layered nanofilm, which enables them candidates in the construction of CPL materials. Following this approach, this work reviews the synthesis of cellulose-based chiral luminescent materials. The co-assembly technique, in situ intercalation strategy, and defect destruction design are efficient in encapsulating the luminophores into the CNC organization. Next, various strategies on the CPL regulation, including the matching of the photonic bandgap, optical pathway design, and tailored helical structure, are summarized. These offer new sights in the CPL control, mainly focusing on the amplification and inversion of optical signals. Multimodal and convertible chiroptical signals enable the photonic films with practical values in anti-counterfeit, sensing, and handedness induction. Overall, this timely overview summarizes the synthesis, regulation, and application of cellulose-based CPL materials, and aims to inspire the development of the chiral optical materials.

THE USE OF INFORMATION TECHNOLOGY IN PRESERVING THE CULTURAL HERITAGE OF KAZAKHSTAN

In the modern world, the issues of preserving and popularizing the cultural heritage of a country and region remain relevant. This issue became particularly acute during and after the COVID-19 pandemic, when museums and galleries existed without active visitor attendance for an extended period. Modern technologies play a significant role in addressing this problem, both by facilitating and simplifying daily routine processes and by globally impacting the conceptual challenges of attracting visitors and expanding the functionality of cultural institutions. This article presents a project to develop a virtual museum in the form of a mobile application. The application will recognize exhibits in real-time using the device's camera and display their 3D models and contextual information in augmented reality mode. The work reviews the use of virtual and augmented reality technologies in museum applications, as well as machine learning algorithms for various purposes. The authors report preliminary results of recognizing some exhibits from a partner museum, describing the applied methodology and analyzing the effectiveness of the approaches used. Additionally, the results of testing a mobile application with real-time recognition capabilities under museum conditions are presented.

Comparison of Low-Brilliance X-Ray Phase-Contrast Tomography and Contrast-Enhanced Attenuation-Contrast Micro-Computed Tomography of Rat Kidneys.

Structural analysis of soft biological tissues is conventionally done with destructive 2D histology. 3D information can be accessed with non-invasive imaging methods, such as X-ray micro-computed tomography (micro-CT). While attenuation-based X-ray imaging alone does not provide reasonable contrast with soft-tissue samples, the combination with contrast-enhancing staining has proven effective. The staining process, however, comes with several disadvantages, such as tissue alterations and laboriousness. A novel X-ray imaging method known as phase-contrast imaging has emerged as an interesting alternative to contrast-enhanced micro-CT. Our objective is to show the feasibility of laboratory-based phase-contrast imaging in (murine) kidney research. X-ray phase-contrast images of male rat kidneys were acquired with a Talbot-Lau interferometer. Moreover, attenuation-based X-ray images of the same unstained kidneys were acquired with a regular micro-CT device. Afterwards, the kidneys were stained with phosphotungstic acid for several months. Attenuation-based micro-CT images were re-acquired after the staining. Contrast-to-noise ratio was evaluated for all three cases. For unstained kidneys, the phase-contrast images show significantly improved contrast in comparison with attenuation images. Several key features, including the cortex, inner and outer medulla, papilla, as well as the main blood vessels can be identified. While the contrast in attenuation images improves significantly after staining, the benefit is deteriorated by sample areas that the contrast agent did not reach properly; even after 206 days. Our results indicate that X-ray phase-contrast imaging is a viable option for kidney imaging in a laboratory setting providing comparable or better results than contrast-enhanced micro-CT. With imaging setups optimized for image resolution and faster imaging times, the advantages of phase-contrast imaging will be even greater.

Depth Segmentation Approach for Egocentric 3D Human Pose Estimation with a Fisheye Camera

In this paper, we propose a novel approach for egocentric 3D human pose estimation using fisheye images captured by a head-mounted display (HMD). Most studies on 3D pose estimation focused on heatmap regression and lifting 2D information to 3D space. This paper addresses the issue of depth ambiguity with highly distorted 2D fisheye images by proposing the SegDepth module, which jointly regresses segmentation and depth maps from the image. The SegDepth module distinguishes the human silhouette, which is directly related to pose estimation through segmentation, and simultaneously estimates depth to resolve the depth ambiguity. The extracted segmentation and depth information are transformed into embeddings and used for 3D joint estimation. In the evaluation, the SegDepth module improves the performance of existing methods, demonstrating its effectiveness and general applicability in improving 3D pose estimation. This suggests that the SegDepth module can be integrated into well-established methods such as Mo2Cap2 and xR-EgoPose to improve 3D pose estimation and provide a general performance improvement.

Rapid large-scale building damage level classification after earthquakes using deep learning with Lidar and satellite optical data

ABSTRACT In post-earthquake scenarios, the swift assessment of building damage levels is pivotal for efficient emergency response and recovery planning. Nevertheless, conventional in-situ damage evaluations consume time. Current satellite-based deep learning methods save time but often lack detail, usually classifying damage as either collapsed or intact. This two-level information is not enough for rescue or recovery planning. Light Detection and Ranging (Lidar)-based deep learning methods, which provide three-dimensional (3D) information, could address this issue of damage details. Therefore, this paper proposes a deep learning-based building damage level classification method using both Lidar and satellite data. The proposed method classifies damage into four levels, including no/minor damage, partially collapsed, totally collapsed, and story failure. The developed network builds upon RandLA-Net, incorporating surface normal vectors to enhance accuracy. A colourised Lidar dataset was created for the network. The network underscores the advantage of incorporating surface normal information. A framework is also proposed based on the damage level outcomes of the developed network, which aids in emergency response efforts. Consequently, this paper demonstrates the practical utility of deep learning networks in rapidly assessing detailed building damage levels after earthquakes. Its practical contribution is guiding decision-making during the critical phases of post-earthquake response and recovery.

Near-Infrared Luminescent Imaging-Based 3D QR Cube Platform for Spatial Information Storage and Security.

The growing significance of information technology requires advanced information storage and security solutions. While extending traditional 2Dcodes with additional parameters has led to promising 3D codes, increasing information capacity and security remains challenging. Herein, a 3D quick response (QR) cube platform that utilizes near-infrared (NIR)-to-NIR upconversion nanoparticles as light-emitting probes, benefiting from their photostability and low scattering properties. These features enable precise reconstruction of the 3D QR cube. The platform employs volumetric space for information encoding by leveraging spatial information in a 3D environment, demonstrating potential to significantly increase information capacity and facilitate access from all three spatial dimensions (x, y, z), while enhancing security. This study develops a platform for analyzing and reconstructing 3D QR cubes using NIR imaging and employs a convolutional neural network model to determine the 3D structure from image intensity variations, achieving 99.9% accuracy in predicting cube configurations. By leveraging 3D spatial information and logical circuits, the encryption method has the potential to significantly surpass the encryption strength of traditional 2D codes. The findings demonstrate high prediction accuracy and introduce new possibilities for multi-level encryption with spatial security keys in 3D space, offering a robust solution for advanced information storage and security.

Trait-focused low-cost and stereo-based 3D plant modeling for phenotyping via deep neural detection

Abstract. Phenotyping, the measurement of plants physical traits, plays a pivotal role in advancing sustainable agricultural practices. Therefore, developing efficient, low-cost, means to generate such measures is vital. Though image based 2D-driven methods are commonly applied for that purpose due to their processing simplicity, it is clear that only 3D information can offer the necessary plant details. Notwithstanding, the generation of such data is challenged by the requirement to acquire a large set of images or the use of active sensors, which exhibit sensitivity to illumination and require lengthy acquisition campaigns. Consequently, 3D plant phenotyping is presently limited to controlled laboratory conditions and is hardly applied in actual growth setups. To address this shortcoming, this paper argues that by focusing on relevant plant traits, modeling can be simplified and the need for detailed plant reconstruction can be relieved. Accordingly, only a minimal set of images, specifically a stereo pair, can suffice for the reconstruction, thereby providing a low-cost sensing solution. To facilitate the reconstruction, we adapt an anchor-free detection deep neural network and integrate low- and high-level features to accurately detect our plant traits of interest. As the paper demonstrates our adapted network facilitates a robust 3D reconstruction of the entities of interest. Performance analysis demonstrates how our detection is reliable and accurate compared to standard anchor-free frameworks, translating to accurate reconstruction, as we validate against 3D plant scans.

Volumetric medical image segmentation via fully 3D adaptation of Segment Anything Model

The Segment Anything Model (SAM) exhibits exceptional generalization capabilities in diverse domains, owing to its interactive learning mechanism designed for precise image segmentation. However, applying SAM to out-of-distribution domains, especially in 3D medical image segmentation, poses challenges. Existing methods for adapting 2D segmentation models to 3D medical data treat 3D volumes as a mere stack of 2D slices. The essential inter-slice information, which is pivotal to faithful 3D medical image segmentation tasks, is unfortunately neglected. In this work, we present the 3D Medical SAM-Adapter (3DMedSAM), a pioneer cross-dimensional adaptation, leveraging SAM’s pre-trained knowledge while accommodating the unique characteristics of 3D medical data. Firstly, to bridge the dimensional gap from 2D to 3D, we design a novel module to replace SAM’s patch embedding, ensuring a seamless transition into 3D image processing and recognition. Besides, we incorporate a 3D Adapter while maintaining the majority of pre-training parameters frozen, enriching deep features with abundant 3D spatial information and achieving efficient fine-tuning. Given the diverse scales of anomalies present in medical images, we also devised a multi-scale 3D mask decoder to elevate the network’s proficiency in medical image segmentation. Through various experiments, we showcase the effectiveness of 3DMedSAM in achieving accurate and robust 3D segmentation on both single-target segmentation and multi-organ segmentation tasks, surpassing the limitations of current methods.