3D Reconstruction Approach Research Articles

3D Imaging Reconstruction and Laparoscopic Robotic Surgery Approach to Disseminated Peritoneal Leiomyomatosis

ObjectivesThis video article explores the synergistic approach of 3D imaging reconstruction and laparoscopic robotic surgery for the management of a complex case of disseminated peritoneal leiomyomatosis (1). The primary focus lies in the capability of the reconstruction model to provide diagnostic support to identify fibroids during surgical procedures, potentially enhancing surgical precision, reducing operating times, minimizing uterine incisions, and limiting blood loss. 3D imaging reconstruction techniques were used to facilitate the identification of multiple parasitic and non-serosal myomas, which is particularly challenging when operating with a robotic surgical platform that lacks haptic feedback. SettingTertiary referral center. ParticipantsA case report design was employed, focusing on a 43-year-old nulliparous infertile woman with multiple symptomatic uterine myomas. Our institution has made a further diagnosis of disseminated peritoneal leiomyomatosis(4-5). InterventionsDue to the widespread nature of peritoneal leiomyomatosis and numerous uterine fibroids, robotic surgery was considered a preferable option based on our experience to operate within confined anatomical spaces. 3D imaging reconstruction technology was utilized for preoperative and intraoperative planning, enabling precise determination of the fibroids' location, size, and volume obtained through MRI imaging. Real-time 3D imaging guided rapid myoma localization and surgical strategy adjustment (2-3). The procedure resulted in the removal of 15 fibroids, with minimal blood loss (250 mL) and a total operative time of 120 minutes. Multilayer running hysterorraphy was performed using a barbed monofilament suture to ensure effective hemostasis, incorporating serosal introflection to reduce the risk of post-operative adhesion development. ConclusionsThe combined approach of 3D imaging reconstruction and laparoscopic robotic surgery holds significant potential for the management of disseminated peritoneal leiomyomatosis. This approach can overcome some robotic surgery limitations, particularly the absence of haptic feedback, providing accurate preoperative planning and real-time intraoperative guidance, facilitating efficient fibroid localization, minimizing uterine incisions, and reducing blood loss. Further research is needed to fully evaluate the clinical impact of this promising technology.

The Potential of Neural Radiance Fields and 3D Gaussian Splatting for 3D Reconstruction from Aerial Imagery

Abstract. In this paper, we focus on investigating the potential of advanced Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting for 3D scene reconstruction from aerial imagery obtained via sensor platforms with an almost nadir-looking camera. Such a setting for image acquisition is convenient for capturing large-scale urban scenes, yet it poses particular challenges arising from imagery with large overlap, very short baselines, similar viewing direction and almost the same but large distance to the scene, and it therefore differs from the usual object-centric scene capture. We apply a traditional approach for image-based 3D reconstruction (COLMAP), a modern NeRF-based approach (Nerfacto) and a representative for the recently introduced 3D Gaussian Splatting approaches (Splatfacto), where the latter two are provided in the Nerfstudio framework. We analyze results achieved on the recently released UseGeo dataset both quantitatively and qualitatively. The achieved results reveal that the traditional COLMAP approach still outperforms Nerfacto and Splatfacto approaches for various scene characteristics, such as less-textured areas, areas with high vegetation, shadowed areas and areas observed from only very few views.

Breaking the limitations with sparse inputs by variational frameworks (BLIss) in terahertz super-resolution 3D reconstruction

Data acquisition, image processing, and image quality are the long-lasting issues for terahertz (THz) 3D reconstructed imaging. Existing methods are primarily designed for 2D scenarios, given the challenges associated with obtaining super-resolution (SR) data and the absence of an efficient SR 3D reconstruction framework in conventional computed tomography (CT). Here, we demonstrate BLIss, a new approach for THz SR 3D reconstruction with sparse 2D data input. BLIss seamlessly integrates conventional CT techniques and variational framework with the core of the adapted Euler-Elastica-based model. The quantitative 3D image evaluation metrics, including the standard deviation of Gaussian, mean curvatures, and the multi-scale structural similarity index measure (MS-SSIM), validate the superior smoothness and fidelity achieved with our variational framework approach compared with conventional THz CT modal. Beyond its contributions to advancing THz SR 3D reconstruction, BLIss demonstrates potential applicability in other imaging modalities, such as X-ray and MRI. This suggests extensive impacts on the broader field of imaging applications.

A deep-learning approach to the 3D reconstruction of dust density and temperature in star-forming regions

Aims. We introduce a new deep-learning approach for the reconstruction of 3D dust density and temperature distributions from multi-wavelength dust emission observations on the scale of individual star-forming cloud cores (<0.2 pc). Methods. We constructed a training data set by processing cloud cores from the Cloud Factory simulations with the POLARIS radiative transfer code to produce synthetic dust emission observations at 23 wavelengths between 12 and 1300 µm. We simplified the task by reconstructing the cloud structure along individual lines of sight (LoSs) and trained a conditional invertible neural network (cINN) for this purpose. The cINN belongs to the group of normalising flow methods and it is able to predict full posterior distributions for the target dust properties. We tested different cINN setups, ranging from a scenario that includes all 23 wavelengths down to a more realistically limited case with observations at only seven wavelengths. We evaluated the predictive performance of these models on synthetic test data. Results. We report an excellent reconstruction performance for the 23-wavelength cINN model, achieving median absolute relative errors of about 1.8% in log(n/m−3) and 1% in log(Tdust/K), respectively. We identify trends towards an overestimation at the low end of the density range and towards an underestimation at the high end of both the density and temperature values, which may be related to a bias in the training data. After limiting our coverage to a combination of only seven wavelengths, we still find a satisfactory performance with average absolute relative errors of about 2.8% and 1.7% in log(n/m−3) and log(Tdust/K). Conclusions. This proof-of-concept study shows that the cINN-based approach for 3D reconstruction of dust density and temperature is very promising and it is even compatible with a more realistically constrained wavelength coverage.

Detection of normal and abnormal brain tumor MRI images using machine learning approaches

<p>Without image processing, the modern technological world we live in today would be completely different. Different applications can be found in many different areas, including as medicine, remote sensing, computer vision, and more. Brain tumours occur because of the abnormal growth of brain tissue. Therefore, it is crucial to surgically remove the tumor’s component that has spread throughout the brain. The creation of images of the human body’s internal organs and structures depends on numerous factors, one of which is the use of Magnetic Resonance Imaging (MRI), one of the many medical imaging techniques. Segmenting images is a challenging task in the modern medical field. The tumor’s location and sizes can be detected with more accuracy with the help of a segmentation performed on MRI slices. In this paper, we propose the Adaptive Convex Region Contour (ACRC) algorithm, a novel method for achieving such requirements. Here, we use the Support Vector Machine, often known as SVM, to identify slices and determine whether they are normal or abnormal. Once the SVM results are in, the off-kilter slices will be included into the analysis. Since the human body already has a complex anatomy in 3D. It’s disappointing because MRI slices can only provide images in a plain, 2D plane. Because of its 3D structure, the tumor’s actual shape cannot be detected from a surface image. This means that the transformation from 2D to 3D is important, as it provides necessary data to the surgeons doing the operation. The Rapid Mode Image Matching (RMIM) algorithm should be used when constructing a 3D reconstruction model. Immediately after the 3D model is completed being created, a rough estimate of the tumor’s original volume is generated. The methodological consideration was conducted in the simulated MATLAB software. It was determined from the data that the proposed method is better to the current establishment methodologies in terms of accuracy. <strong>Objective:</strong> This paper aims to address the need for accurate segmentation of brain tumors in Magnetic Resonance Imaging (MRI) slices. The primary research question revolves around developing a novel segmentation method, the Adaptive Convex Region Contour (ACRC) algorithm, to improve the precision of tumor detection by incorporating a Support Vector Machine (SVM) for identifying abnormal slices.<strong> </strong><strong>Methods: </strong>The proposed approach involves utilizing the SVM to classify MRI slices as either normal or abnormal. The abnormal slices, detected by SVM, are subjected to further analysis. To overcome the limitation of MRI producing 2D images for the inherently 3D human anatomy, a transformation from 2D to 3D is crucial. The Rapid Mode Image Matching (RMIM) algorithm is adopted to construct a 3D reconstruction model of the brain and its tumor. This model allows surgeons to obtain a more accurate understanding of the tumor’s actual 3D shape. <strong>Results: </strong>The methodology was implemented and evaluated using simulated MATLAB software. The results demonstrate that the proposed ACRC algorithm, combined with SVM-based abnormal slice identification and subsequent 3D reconstruction using RMIM, outperforms existing methodologies in terms of accuracy. The 3D reconstruction provides valuable insights into the tumor’s shape, aiding surgical planning. <strong>Conclusions: </strong>In conclusion, the research contributes to the field of medical image processing by presenting a comprehensive approach for brain tumor segmentation and 3D reconstruction. The ACRC algorithm, in conjunction with SVM and RMIM, enhances the accuracy of tumor detection and provides critical 3D information for surgical procedures. This advancement holds the potential to improve the efficacy of brain tumor surgeries, underscoring the significance of innovative image processing techniques in modern medical applications.</p>

Joint shape/texture representation learning for cardiovascular disease diagnosis from magnetic resonance imaging.

Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide. Cardiac image and mesh are two primary modalities to present the shape and structure of the heart and have been demonstrated to be efficient in CVD prediction and diagnosis. However, previous research has been generally focussed on a single modality (image or mesh), and few of them have tried to jointly consider the image and mesh representations of heart. To obtain efficient and explainable biomarkers for CVD prediction and diagnosis, it is needed to jointly consider both representations. We design a novel multi-channel variational auto-encoder, mesh-image variational auto-encoder, to learn joint representation of paired mesh and image. After training, the shape-aware image representation (SAIR) can be learned directly from the raw images and applied for further CVD prediction and diagnosis. We demonstrate our method on data from UK Biobank study and two other datasets via extensive experiments. In acute myocardial infarction prediction, SAIR achieves 81.43% accuracy, significantly higher than traditional biomarkers like metadata and clinical indices (left ventricle and right ventricle clinical indices of cardiac function like chamber volume, mass, and ejection fraction). Our mesh-image variational auto-encoder provides a novel approach for 3D cardiac mesh reconstruction from images. The extraction of SAIR is fast and without need of segmentation masks, and its focussing can be visualized in the corresponding cardiac meshes. SAIR archives better performance than traditional biomarkers and can be applied as an efficient supplement to them, which is of significant potential in CVD analysis.

SSDL-an automated semi-supervised deep learning approach for patient-specific 3D reconstruction of proximal femur from QCT images.

Deep Learning (DL) techniques have recently been used in medical image segmentation and the reconstruction of 3D anatomies of a human body. In this work, we propose a semi-supervised DL (SSDL) approach utilizing a CNN-based 3D U-Net model for femur segmentation from sparsely annotated quantitative computed tomography (QCT) slices. Specifically, QCT slices at the proximal end of the femur forming ball and socket joint with acetabulum were annotated for precise segmentation, where a segmenting binary mask was generated using a3D U-Net model to segment the femur accurately. A total of 5474 QCT slices were considered for training among which 2316 slices were annotated. 3D femurs were further reconstructed from segmented slices employing polynomial spline interpolation. Both qualitative and quantitative performance of segmentation and 3D reconstruction were satisfactory with more than 90% accuracy achieved for allof the standardperformance metrics considered. The spatial overlap index and reproducibility validation metric for segmentation-Dice Similarity Coefficient was 91.8% for unseen patients and 99.2% for validated patients. An average relative error of 12.02% and 10.75% for volume and surface area, respectively, were computed for 3D reconstructed femurs. The proposed approach demonstrates its effectiveness in accurately segmenting and reconstructing 3D femur from QCT slices.

Articulated Motion-Aware NeRF for 3D Dynamic Appearance and Geometry Reconstruction by Implicit Motion States.

We propose a self-supervised approach for 3D dynamic reconstruction of articulated motions based on Generative Adversarial Networks and Neural Radiance Fields. Our method reconstructs articulated objects and recover their continuous motions and attributes from an unordered, discontinuous image set. Notably, we treat motion states as time-independent, recognizing that articulated objects can exhibit identical motions at different times. The key insight of our approach utilizes generative adversarial networks to create a continuous implicit motion state space. Initially, we employ a motion network extracts discrete motion states from images as anchors. These anchors are then expanded across the latent space using generative adversarial networks. Subsequently, motion state latent codes are input into motion-aware neural radiance fields for dynamic appearance and geometry reconstruction. To deduce motion attributes from the continuously generated motions, we adopt a cluster-based strategy. We thoroughly evaluate and validate our method on both synthesized and real data, demonstrating superior fidelity in appearances, geometries, and motion attributes of articulated objects compared to state-of-the-art methods.

Leveraging 360° Camera in 3D Reconstruction: A Vision-Based Approach

In this paper, we present a novel vision-based approach for 3D reconstruction using a single 360° camera, aiming to offer a simplified and accessible solution for various consumer-oriented applications. Consumer-grade 360° cameras have gained significant popularity due to their affordability and ease of use. However, traditional methods for 3D reconstruction often require complex setups with multiple cameras or expensive hardware such as Light Detection and Ranging (LiDAR). Our approach addresses the challenges associated with 360° cameras by converting the distorted Equirectangular Projection (ERP) into four perspective views, allowing compatibility with deep learning models trained on undistorted perspective images. We leverage Visual Simultaneous Localization and Mapping (VSLAM) techniques for camera pose estimation and employ a standard 3D reconstruction pipeline for generating detailed 3D mesh representations of the indoor environment. Through experimental evaluation, we compare the performance of 360° cameras with traditional perspective cameras in 3D reconstruction and analyze the accuracy and performance of our vision-based approach. Our findings demonstrate the potential of using 360° cameras for constructing high-quality models and facilitating efficient data collection for 3D reconstructions, opening up new possibilities for various consumer-oriented applications in multiple fields.

Fast 3D reconstruction via event-based structured light with spatio-temporal coding.

Event-based structured light (SL) systems leverage bio-inspired event cameras, which are renowned for their low latency and high dynamics, to drive progress in high-speed structured light systems. However, existing event-based structured light methods concentrate on the independent construction of either time-domain or space-domain features for stereo matching, ignoring the spatio-temporal consistency towards depth. In this work, we build an event-based SL system that consists of a laser point projector and an event camera, and we devise a spatial-temporal coding strategy that realizes depth encoding in dual domains through a single shot. To exploit the spatio-temporal synergy, we further present STEM, a novel Spatio-Temporal Enhanced Matching approach for 3D reconstruction. STEM is comprised of two parts, the spatio-temporal enhancing (STE) algorithm and the spatio-temporal matching (STM) algorithm. Specifically, STE integrates the dual-domain information to increase the saliency of the temporal coding, providing a more robust basis for matching. STM is a stereo matching algorithm explicitly tailored to the unique characteristics of event data modality, which computes the disparity via a meticulously designed hybrid cost function. Experimental results demonstrate the superior performance of our proposed method, achieving a reconstruction rate of 16 fps and a low root mean square error of 0.56 mm at a distance of 0.72 m.

Robust Fusion of Multi-Source Images for Accurate 3D Reconstruction of Complex Urban Scenes

Integrated reconstruction is crucial for 3D modeling urban scenes using multi-source images. However, large viewpoint and illumination variations pose challenges to existing solutions. A novel approach for accurate 3D reconstruction of complex urban scenes based on robust fusion of multi-source images is proposed. Firstly, georeferenced sparse models are reconstructed from the terrestrial and aerial images using GNSS-aided incremental SfM, respectively. Then, cross-platform match pairs are selected based on point-on-image observability. The terrestrial and aerial images are robustly matched based on the selected match pairs to generate cross-platform tie points. Thirdly, the tie points are triangulated to derive cross-platform 3D correspondences. The 3D correspondences are refined using a novel outlier detection method. Finally, the terrestrial and aerial sparse models are merged based on the refined correspondences, and the integrated model is globally optimized to obtain an accurate reconstruction of the scene. The proposed methodology is evaluated on five benchmark datasets, and extensive experiments are performed. The proposed pipeline is compared with a state-of-the-art methodology and three widely used software packages. Experimental results demonstrate that the proposed methodology outperforms the other pipelines in terms of robustness and accuracy.

A multi-stage neural network approach for coronary 3D reconstruction from uncalibrated X-ray angiography images

We present a multi-stage neural network approach for 3D reconstruction of coronary artery trees from uncalibrated 2D X-ray angiography images. This method uses several binarized images from different angles to reconstruct a 3D coronary tree without any knowledge of image acquisition parameters. The method consists of a single backbone network and separate stages for vessel centerline and radius reconstruction. The output is an analytical matrix representation of the coronary tree suitable for downstream applications such as hemodynamic modeling of local vessel narrowing (i.e., stenosis). The network was trained using a dataset of synthetic coronary trees from a vessel generator informed by both clinical image data and literature values on coronary anatomy. Our multi-stage network achieved sub-pixel accuracy in reconstructing vessel radius (RMSE = 0.16 ± 0.07 mm) and stenosis radius (MAE = 0.27 ± 0.18 mm), the most important feature used to inform diagnostic decisions. The network also led to 52% and 38% reduction in vessel centerline reconstruction errors compared to a single-stage network and projective geometry-based methods, respectively. Our method demonstrated robustness to overcome challenges such as vessel foreshortening or overlap in the input images. This work is an important step towards automated analysis of anatomic and functional disease severity in the coronary arteries.

A Family of Approaches for Full 3D Reconstruction of Objects with Complex Surface Reflectance

3D reconstruction of general scenes remains an open challenge with current techniques often reliant on assumptions on the scene’s surface reflectance, which restrict the range of objects that can be modelled. Helmholtz Stereopsis offers an appealing framework to make the modelling process agnostic to surface reflectance. However, previous formulations have been almost exclusively limited to 2.5D modelling. To address this gap, this paper introduces a family of reconstruction approaches that exploit Helmholtz reciprocity to produce complete 3D models of objects with arbitrary unknown reflectance. This includes an approach based on the fusion of (orthographic or perspective) view-dependent reconstructions, a volumetric approach optimising surface location within a voxel grid, and a mesh-based formulation optimising vertices positions of a given mesh topology. The contributed approaches are evaluated on synthetic and real datasets, including novel full 3D datasets publicly released with this paper, with experimental comparison against a wide range of competing methods. Results demonstrate the benefits of the different approaches and their abilities to achieve high quality full 3D reconstructions of complex objects.

GEMVS: a novel approach for automatic 3D reconstruction from uncalibrated multi-view Google Earth images using multi-view stereo and projective to metric 3D homography transformation

ABSTRACT This paper proposes a novel approach for automatic 3D surface reconstruction from uncalibrated and multi-view Google Earth images by using a multi-view stereo method and 3D projective to metric transformation. Without the Rational Polynomial Coefficients, it is impossible to obtain the metric reconstruction of the 3D surface from multi-view satellite images. We solve the uncalibrated multi-view satellite image problem by employing a multi-view stereo vision technique followed by a projective to metric transformation. The virtual pose parameters of the satellite images are obtained by using COLMAP, and the virtual 3D projective reconstruction is done by using EnSoft3D. For projective to metric transformation, we propose to employ 3D homography transformation. Eight 3D correspondence pairs on the viewing frustums between the virtual reference camera and the ideal nadir camera are used to derive a 3D homography matrix. Using the 3D homography matrix, we finally obtain the metric reconstruction of 3D surface up to an unknown height scale in a reference coordinate system of the Google Earth desktop software. Experiments are done in several world locations on Google Earth including building and vegetation areas. Reconstruction error analysis with the Data Fusion Contest 19 dataset is also presented. The average of MAE and RMSE of five tile regions in the dataset are 1.596 m and 2.083 m, respectively.

CF3DNet: A learning-based approach for single-shot 3D reconstruction from circular fringes

3D reconstruction by fringe projection is an ill-posed problem that is of significance in many practical applications. Conventional algorithms employ multiple steps like fringe analysis, phase unwrapping, and calibration to reconstruct the 3D profile. Recent deep learning (DL) methods circumvent this multi-step approach by directly mapping the deformations in linear fringes caused by objects to the absolute phase shifts. However, this mapping is not one-to-one for the discontinuous objects due to 2π periodicity of linear fringes, and hence it is impossible to reconstruct using a single deformed fringe. In this work, we propose Circular Fringe-to-3D reconstruction Network (CF3DNet), a learning-based approach for 3D reconstruction from circular fringes, that can establish a one-to-one mapping between the phase deformations and the absolute phase shifts. This is viable because the radially symmetric circular fringes can distinctly record the lateral shifts caused by objects, and the learning-based approach can uniquely map them to absolute phase shifts. Thus, the proposed CF3DNet can reconstruct discontinuous object profiles, which is infeasible with linear fringe-based methods. Further, to evade the tedious process of real data collection and to bring generalization, the model is trained with synthetically generated smooth, discontinuous and Computer-Aided Design (CAD) object profiles. The results demonstrate that the proposed method led to tenfold improved performance over linear fringe-based methods in 3D reconstruction of various synthetic objects, and accurately estimate the real object profile compared to the other state-of-art methods. The extensive analysis presented in this work shows that the proposed method can work with carrier frequencies as low as 10 cycles/row to reconstruct dynamic ranges up to 100 radians under high noise, paving a way for the development of a cost-effective fringe projection system.

Error analysis and 3D reconstruction using airborne array InSAR images

Multi-temporal synthetic aperture radar interferometry (MT-InSAR) is able to detect surface deformation and reconstruct a 3D surface model with high precision but requires a long observation period to accumulate the multi-baseline SAR images. The airborne array InSAR system is able to acquire a stack of multi-baseline SAR images in a single acquisition, which significantly improves the 3D modeling capability. However, processing the images obtained by the low-altitude platform using the conventional model will lead to geometric approximation (GA) errors, such as flattened phase error and reference error, which degrade the precision of the 3D reconstruction. In this paper, we quantitatively analyze the error sources of the array-InSAR interferograms and design a hybrid 3D phase unwrapping approach for 3D reconstruction. A hypothesis test is developed to identify the phase ambiguity by comparing the initial solution of the least-square with that of the beam-forming. Three indicators are proposed to identify the reliable arcs, achieving a reliable phase unwrapping. The L1 norm approach is adopted to detect the unwrapping errors during the spatial unwrapping and a two-tie network strategy is used to process the data in the individual blocks from the perspective of global optimization. Furthermore, an iterative scheme is recommended to compensate the geometric approximation errors. The main advantage of the proposed algorithm is the compensation of the GA error and reliable phase unwrapping. The experimental results by both simulated and real SAR data show that the proposed algorithm can eliminate the GA error and provide a viable solution to rapid 3D SAR imaging with an airborne platform.

AN EXAMPLE OF 3D RECONSTRUCTION ENVIRONMENT FROM RGB-D CAMERA

3D environment reconstruction is a very important research direction in robotics and computer vision. This helps the robot to locate and find directions in a real environment or to help build support systems for the blind and visually impaired people. In this paper, we introduce a simple and real-time approach for 3D environment reconstruction from data obtained from cheap cameras. The implementation is detailed step by step and illustrated with source code. Simultaneously, cameras that support reconstructing 3D environments in this approach are also presented and introduced. The unorganized point cloud datais also presented and visualized in the available figures .

A novel approach of efficient 3D reconstruction for real scene using unmanned aerial vehicle oblique photogrammetry with five cameras

In recent years, unmanned aerial vehicle (UAV) oblique photogrammetry is a new and advanced technology in the field of surveying and mapping. In this paper, an oblique photogrammeter with five high-precise measuring cameras is developed, which can be controlled by a microcontroller, positioned by Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS), and accessed through narrowband internet of things (NB-IoT) module interface. Meanwhile, distributed parallel computing and graphics processing unit (GPU) are used to improve the efficiency of 3D modeling for the real scene. Finally, the urban spatial information collected by UAV oblique photogrammetry and the real scene 3D modeling results are verified by experiments. The experiment results demonstrate that the method proposed in this paper can efficiently complete the collection of urban spatial information and improve the efficiency 3D modeling for the real scene. The proposed method has about 30% improved efficiency compared to the conventional methods.

Convincing 3D Face Reconstruction from a Single Color Image under Occluded Scenes

The last few years have witnessed the great success of generative adversarial networks (GANs) in synthesizing high-quality photorealistic face images. Many recent 3D facial texture reconstruction works often pursue higher resolutions and ignore occlusion. We study the problem of detailed 3D facial reconstruction under occluded scenes. This is a challenging problem; currently, the collection of such a large scale high resolution 3D face dataset is still very costly. In this work, we propose a deep learning based approach for detailed 3D face reconstruction that does not require large-scale 3D datasets. Motivated by generative face image inpainting and weakly-supervised 3D deep reconstruction, we propose a complete 3D face model generation method guided by the contour. In our work, the 3D reconstruction framework based on weak supervision can generate convincing 3D models. We further test our method on the MICC, Florence and LFW datasets, showing its strong generalization capacity and superior performance.

A0470 - A novel approach for 3D reconstruction of the prostate gland that allows tumour location, volume estimation, and Gleason characterization

A0470 - A novel approach for 3D reconstruction of the prostate gland that allows tumour location, volume estimation, and Gleason characterization