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1st Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care, Deep-Breath 2024

作者： Pooyan, Melika Awwad, Hadeel García, Eloy Martí, Robert Institute of Computer Vision and Robotics University of Girona Girona Spain

ISBN: (纸本)9783031777882

Integrating 2D mammography with 3D magnetic resonance imaging (MRI) is crucial for improving breast cancer diagnosis and treatment planning. However, this integration is challenging due to differences in imaging modalities and the need for precise tissue segmentation and alignment. This paper addresses these challenges by enhancing biomechanical breast models in two main aspects: improving tissue identification using nnU-Net segmentation models and evaluating finite element (FE) biomechanical solvers, specifically comparing NiftySim and FEBio. We performed a detailed six-class segmentation of breast MRI data using the nnU-Net architecture, achieving Dice Coefficients of 0.94 for fat, 0.88 for glandular tissue, and 0.87 for pectoral muscle. The overall foreground segmentation reached a mean Dice Coefficient of 0.83 through an ensemble of 2D and 3D U-Net configurations, providing a solid foundation for 3D reconstruction and biomechanical modeling. The segmented data was then used to generate detailed 3D meshes and develop biomechanical models using NiftySim and FEBio, which simulate breast tissue’s physical behaviors under compression. Our results include a comparison between NiftySim and FEBio, providing insights into the accuracy and reliability of these simulations in studying breast tissue responses under compression. The findings of this study have the potential to improve the integration of 2D and 3D imaging modalities, thereby enhancing diagnostic accuracy and treatment planning for breast cancer. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tissue

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Graph Neural Networks for Modelling Breast Biomechanical Compression 1st

Graph Neural Networks for Modelling Breast Biomechanical Co...

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1st Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care, Deep-Breath 2024

作者： Awwad, Hadeel García, Eloy Martí, Robert Computer Vision and Robotics Institute University of Girona Girona Spain

ISBN: (纸本)9783031777882

Breast compression simulation is essential for accurate image registration from 3D modalities to X-ray procedures like mammography. It accounts for tissue shape and position changes due to compression, ensuring precise alignment and improved analysis. Although Finite Element Analysis (FEA) is reliable for approximating soft tissue deformation, it struggles with balancing accuracy and computational efficiency. Recent studies have used data-driven models trained on FEA results to speed up tissue deformation predictions. We propose to explore Physics-based Graph Neural Networks (PhysGNN) for breast compression simulation. PhysGNN has been used for data-driven modelling in other domains, and this work presents the first investigation of their potential in predicting breast deformation during mammographic compression. Unlike conventional data-driven models, PhysGNN, which incorporates mesh structural information and enables inductive learning on unstructured grids, is well-suited for capturing complex breast tissue geometries. Trained on deformations from incremental FEA simulations, PhysGNN’s performance is evaluated by comparing predicted nodal displacements with those from finite element (FE) simulations. This deep learning (DL) framework shows promise for accurate, rapid breast deformation approximations, offering enhanced computational efficiency for real-world scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tissue

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Comparative Analysis of Linear and Exact Control Laws in Zonotope-Based MPC 8

Comparative Analysis of Linear and Exact Control Laws in Zon...

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8th Scientific School "Dynamics of Complex Networks and their Applications", DCNA 2024

作者： Jnadi, Ali Savin, Sergei Institute of Robotics and Computer Vision Innopolis University Innopolis Russia

ISBN: (纸本)9798350390834

Zonotopes are a special type of symmetric polytopes that have been an emerging control design tool, being used for such tasks as state estimation and explicit model predictive control. Zonotopes are used to represent sets of control actions and states, encountered by trajectories of the system, originating from a set of initial conditions. However, an inherent limitation of zonotope geometry (their symmetries, for example) limits the range of control design scenarios where zonotopes can be used as an adequate set representation, while the use of linear control laws can prevent change in the desired behavior of the closed-loop system. The first issue can be remedied by the use of constrained zonotopes, which can represent arbitrary polytopes. In this paper we present an analysis of the second problem, demonstrating the effects of linear control law and exact, convex programmingbased control law. We demonstrate the efficacy of the use of the lifting-up method for constrained zonotopes as pertains to control design. © 2024 IEEE.

关键词： Constrained optimization

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Support vector machine with discriminative low-rank embedding

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CAAI Transactions on Intelligence Technology 2024年第5期9卷 1249-1262页

作者： Guangfei Liang Zhihui Lai Heng Kong Computer Vision Institute College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina Shenzhen Institute of Artificial Intelligence and Robotics for Society ShenzhenChina Department of Breast and Thyroid Surgery BaoAn Central Hospital of ShenzhenShenzhenChina

Support vector machine(SVM)is a binary classifier widely used in machine ***,neglecting the latent data structure in previous SVM can limit the performance of SVM and its *** address this issue,the authors propose a novel SVM with discriminative low-rank embedding(LRSVM)that finds a discriminative latent low-rank subspace more suitable for SVM *** extension models of LRSVM are introduced by imposing different orthogonality constraints to prevent computational inaccuracies.A detailed derivation of the authors’iterative algorithms are given that is essentially for solving the SVM on the low-rank ***,some theorems and properties of the proposed models are presented by the *** is worth mentioning that the subproblems of the proposed algorithms are equivalent to the standard or the weighted linear discriminant analysis(LDA)*** indicates that the projection subspaces obtained by the authors’algorithms are more suitable for SVM classification compared to those from the LDA *** convergence analysis for the authors proposed algorithms are also ***,the authors conduct experiments on various machine learning data sets to evaluate the *** experiment results show that the authors’algorithms perform significantly better than other algorithms,which indicates their superior abilities on classification tasks.

关键词： iterative methods machine leaning support vector machunes

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Stiffness-based Offline Toolpath Error Compensation for Robotized Incremental Forming 7

Stiffness-based Offline Toolpath Error Compensation for Robo...

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7th International Conference on Automation, Control and Robots, ICACR 2023

作者： Shaker, Walid K. Klimchik, Alexandr Innopolis University Institute of Robotics and Computer Vision Innopolis Russia Lincoln United Kingdom

ISBN: (纸本)9798350302882

This paper addresses the challenge of the poor accuracy and the toolpath deviation in Incremental Sheet Forming (ISF) caused by the forming forces applied to the workpiece. The paper proposes an offline toolpath error compensation approach based on stiffness analysis. A truncated cone was used as a test case and the numerical simulation of the ISF process was conducted on Abaqus CAE software to extract forming forces corresponding to toolpath points. The compensation algorithm is based on the Virtual Joint Method (VJM) and is implemented on a FANUC R-2000iC/165F robot using RoboDK API for MATLAB. The results show that the deviation of the robot tool due to the forming forces may reach up to 20 mm which can be handled by the compensated toolpath. Finally, the study proposes an overall process for the stiffness-based correction algorithm. © 2023 IEEE.

关键词： Stiffness

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Comparative Analysis of Springback Compensation for Various Profiles in Incremental Forming

Comparative Analysis of Springback Compensation for Various ...

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2023 International Russian Automation Conference, RusAutoCon 2023

作者： Shaker, Walid K. Klimchik, Alexandr Institute of Robotics and Computer Vision Innopolis University Innopolis Russia Lincoln United Kingdom

ISBN: (纸本)9798350345551

Incremental forming encounters a common challenge known as the springback effect, wherein the workpiece undergoes elastic deformation and deviates slightly from the desired shape once the forming tool is released. This discrepancy between the intended and obtained shape results in reduced geometric accuracy, making incremental forming less precise compared to conventional methods. This research presents a novel springback effect compensation model for sheet forming processes. The main objective is to evaluate the model's performance across various profiles, with a focus on enhancing precision and accuracy in the formed shapes. The proposed model demonstrates an impressive ability to compensate for approximately 60% of the springback effect, offering a practical solution for offline springback compensation. © 2023 IEEE.

关键词： single-point incremental forming SPIF numerical simulation SPIF trajectories springback compensation

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Explicit Model Predictive Control Design based on Constrained Zonotope Propagation 7

Explicit Model Predictive Control Design based on Constraine...

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7th Scientific School: Dynamics of Complex Networks and their Applications, DCNA 2023

作者： Jnadi, Ali Khusainov, Ramil Nedelchev, Simeon Savin, Sergei Innopolis University Institute of Robotics and Computer Vision Innopolis Russia Innopolis University Center for Technologies in Robotics and Mechatronics Components Innopolis Russia

ISBN: (纸本)9798350305746

Zonotopes have become a widely used tool in control design, demonstrating their effectiveness in various applications ranging from state estimation to explicit model predictive control. However, the traditional use of zonotopes is limited by a set certain geometric shapes that restrict the range of state sets that can be considered. This paper proposes a novel approach to control design using constrained zonotopes that can encode arbitrary polytopes. We present a propagation scheme and an affine approximation of the exact control law for this representation, and discuss issues related to center identification for constrained zonotopes. A fast method for detecting set membership is also proposed based on lifting the constrained zonotopes into a higher dimensional space. The proposed method extends the range of shapes that can be considered in control design and offers a promising alternative to the limitations of regular zonotopes. It has the potential for wide-ranging impact in various areas of control, providing a valuable tool for solving complex control problems. © 2023 IEEE.

关键词： Convex optimization

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Pose Measurement of the EndoWrist Round Tip Scissor Instrument with Optical Coherence Tomography

Pose Measurement of the EndoWrist Round Tip Scissor Instrume...

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作者： Schöne, Sandra Pol, Nirmal Kahrs, Lueder A. Medical Computer Vision and Robotics Lab University of Toronto Toronto Canada University of Stuttgart Stuttgart Germany Medical Computer Vision and Robotics Lab Institute of Biomedical Engineering University of Toronto Toronto Canada Department of Mathematical and Computational Sciences University of Toronto Mississauga Mississauga Canada

To automate surgical (sub-)tasks in robotic surgery, the knowledge of the exact pose of the instrument is mandatory. The application of Optical Coherence Tomography (OCT) to the problem of pose measurement appears promising due to its advantages of 3D imaging and micron-scale resolution. To investigate this, 175 image sequences of the EndoWrist Round Tip Scissor Tool were acquired with an OCT system. The images differ in the opening angles of the scissor blades and the rotation angles of the entire instrument about its central axis. These image sequences were further processed through computer vision methods of the individual images followed by point cloud generation. For pose estimation, an Iterative Closest Point algorithm was implemented to register the acquired point clouds to reference point clouds created from the instrument CAD file. The implemented algorithm was able to determine the opening angle with an overall error of 2 ± 1.3 and the rotation angle with a standard deviation between several runs of 0.6 ±2.8. However, the overall processing time of (39 ± 17)s on a standard PC leaves room for further investigations. © 2024 by Walter de Gruyter Berlin/Boston.

关键词： Optical coherence tomography

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Simulation Study for Robot-based Single Point Incremental Forming

Simulation Study for Robot-based Single Point Incremental Fo...

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2023 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2023

作者： Shaker, Walid K. Klimchick, Alexandr Innopolis University Institute of Robotics and Computer Vision Innopolis Russia University of Lincoln School of Computer Science Lincoln United Kingdom

ISBN: (纸本)9781665475952

The present study investigates the toolpath definition for the numerical simulation of the Single Point Incremental Forming (SPIF). Since the toolpath definition greatly influences the geometrical accuracy of the formed part, substantial research has been taken to properly generate the toolpath. In this paper, two different toolpath strategies were defined: Z-level, and spiral toolpath. A robot program was created using RoboDK to simulate the SPIF process and to test the generated toolpath over a designed workpiece of a truncated cone. The paper also shows how the SPIF numerical simulation was conducted on Abaqus/CAE software. The results demonstrated that the toolpath was properly designed;however, 0.5% relative error was found between the desired and the simulated cone depth due to the SPIF geometric error. To improve the overall accuracy of the SPIF, a combined stiffness-based and closed loop control algorithm is proposed. © 2023 IEEE.

关键词： Numerical models

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Using deep learning for triple-negative breast cancer classification in DCE-MRI 16

Using deep learning for triple-negative breast cancer classi...

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16th International Workshop on Breast Imaging, IWBI 2022

作者： Vidal, Joel Martí, Robert Computer Vision and Robotics Institute University of Girona Girona Spain

ISBN: (纸本)9781510655843

Triple-negative is one of the most aggressive type of breast cancer for which is also difficult to find an effective treatment. An early diagnosis and a fast and specific treatment are shown to be key aspects for a better prognosis. Current diagnosis of these cases are based on performing a biopsy. This study proposes a non-invasive medical imaging predication method, based on a deep learning architecture, to automatically classify triple-negative tumors in DCE-MRI images. Results are evaluated on an extensive public dataset for different normalizations, data augmentations, learning rates and batch sizes, reaching a state-of-the-art AUC of 0.68. © 2022 SPIE.

关键词： Diseases

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