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nnUNet for Brain Tumor Segmentation in Sub-Saharan Africa Patient Population 1

Challenge on Brain Tumor Segmentation, BraTS 2023, International Challenge on Cross-Modality Domain Adaptation for Medical Image Segmentation, CrossMoDA 2023, held in conjunction with the Medical Image Computing for computer Assisted Intervention Conference, MICCAI 2023

作者： Abramova, Valeriia Lal-Trehan Estrada, Uma M. Yalçın, Cansu Hamadache, Rachika E. Clèrigues, Albert Tovar Sáez, Francisco Aarón Guirao, Marc Salvi, Joaquim Oliver, Arnau Lladó, Xavier Computer Vision and Robotics Group University of Girona Girona Spain

ISBN: (数字)9783031761638

ISBN: (纸本)9783031761621

Glioma is one of the most dangerous and aggressive brain tumor types. These tumors are difficult to diagnose, and they require careful and precise identification and delineation of different tumor subtypes. Currently, magnetic resonance imaging (MRI) is the gold standard for glioma detection. However, not all the territories are equipped with up-to-date medical devices and highly skilled professionals, and it is important to develop glioma segmentation methods for the images acquired in such low-resource settings. Therefore, BraTS-Africa challenge was announced in 2023 to provide researchers the opportunity to develop segmentation algorithms using brain MRI glioma cases from Sub-Saharan Africa population. In this paper, we present our submission to this challenge. We based our approach on the well-known nnUNet model, which won original BraTS 2020 and 2021 challenges. Within our work, we studied the impact of using pretrained and fine-tuned models, different image input modalities, ensemble of different models, and the application of specific region based strategies. Obtained results on the unseen testing data showed promising results, having good Dice values for all 3 classes and a small HD95 distance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Magnetic resonance imaging

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MRI Breast Tissue Segmentation Using nnU-Net for Biomechanical Modeling 1st

MRI Breast Tissue Segmentation Using nnU-Net for Biomechani...

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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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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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Real-Time Multi-object Tracking Using YOLOv8 and SORT on a SoC FPGA 21st

Real-Time Multi-object Tracking Using YOLOv8 and SORT on a...

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21st International Symposium on Applied Reconfigurable Computing, ARC 2025

作者： Danilowicz, Michal Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031879944

Multi-object tracking (MOT) is one of the most important problems in computer vision and a key component of any vision-based perception system used in advanced autonomous mobile robotics. Therefore, its implementation on low-power and real-time embedded platforms is highly desirable. Modern MOT algorithms should be able to track objects of a given class (e.g. people or vehicles). In addition, the number of objects to be tracked is not known in advance, and they may appear and disappear at any time, as well as be obscured. For these reasons, the most popular and successful approaches have recently been based on the tracking paradigm. Therefore, the presence of a high quality object detector is essential, which in practice accounts for the vast majority of the computational and memory complexity of the whole MOT system. In this paper, we propose an FPGA (Field-Programmable Gate Array) implementation of an embedded MOT system based on a quantized YOLOv8 detector and the SORT (Simple Online Realtime Tracker) tracker. We use a modified version of the FINN framework to utilize external memory for model parameters and to support operations necessary required by YOLOv8. We discuss the evaluation of detection and tracking performance using the COCO and MOT15 datasets, where we achieve 0.21 mAP and 38.9 MOTA respectively. As the computational platform, we use an MPSoC system (Zynq UltraScale+ device from AMD/Xilinx) where the detector is deployed in reprogrammable logic and the tracking algorithm is implemented in the processor system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： System-on-chip

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Going Smaller: Attention-based models for automated melanoma diagnosis

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computers in Biology and Medicine 2025年 185卷 109492-109492页

作者： Nazari, Sana Garcia, Rafael Computer Vision and Robotics Group University of Girona Plaça de Sant Domènec 3 Girona17004 Spain

Computational approaches offer a valuable tool to aid with the early diagnosis of melanoma by increasing both the speed and accuracy of doctors’ decisions. The latest and best-performing approaches often rely on large ensemble models, with the number of trained parameters exceeding 600 million. However, this large parameter count presents considerable challenges in terms of computational demands and practical application. Addressing this gap, our work introduces a suite of attention-based convolutional neural network (CNN) architectures tailored to the nuanced classification of melanoma. These innovative models, founded on the EfficientNet-B3 backbone, are characterized by their significantly reduced size. This study highlights the feasibility of deploying powerful, yet compact, diagnostic models in practical settings, such as smartphone-based dermoscopy, and in doing so revolutionizing point-of-care diagnostics and extending the reach of advanced medical technologies to remote and under-resourced areas. It presents a comparative analysis of these novel models with the top three prize winners of the International Skin Imaging Collaboration (ISIC) 2020 challenge using two independent test sets. The results for our architectures outperformed the second and third-placed winners and achieved comparable results to the first-placed winner. These models demonstrated a delicate balance between efficiency and accuracy, holding their ground against larger models in performance metrics while operating on up to 98% less number of parameters and showcasing their potential for real-time application in resource-limited environments. © 2024 The Authors

关键词： Dermatology

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An End-to-End Deep Learning Framework for Predicting Hematoma Expansion in Hemorrhagic Stroke Patients from CT Images

SSRN

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SSRN 2024年

作者： Abramova, Valeriia Oliver, Arnau Salvi, Joaquim Terceño, Mikel Silva, Yolanda Lladó, Xavier Computer Vision and Robotics Group University of Girona Catalonia Spain Catalonia Girona Spain

Background and Objectives: Hematoma expansion (HE) occurs in 20% of patients with hemorrhagic stroke within 24 hours of onset and it is associated with a poorer patient outcome. From a clinical point of view, predicting the HE from the initial patient computed tomography (CT) image would be very useful, not only to be able to act faster but to improve therapeutic decisions. Recently, some works have started to study the prediction of the HE as a classification problem to point out if the hematoma will grow or not. However, there are not many works aiming to predict the final extent and appearance of the ***: In this work, we propose an end-to-end deep learning framework for predicting the final hematoma expansion and its corresponding lesion mask. We also explore the problem of having limited data and propose to augment the available dataset with the generation of synthetic images. The proposed approach is evaluated in different scenarios such as training using only the original CT images, using standard data augmentation techniques and using the image synthesis. We perform our experiments in a leave-one-out manner on an in-house dataset consisting of 27 hemorrhagic stroke patients with HE, where longitudinal CT images together with manual lesion annotations were ***: The obtained results showed a promising HE prediction, improving the performance when incorporating the use of synthetic images into the model. Our best model obtained a mean Dice score of the HE growth area of 0.506 and an average prediction error of hematoma volume of -3.44 *** The proposed approach showed a great potential for robust prediction of HE, highlighting also the importance of further investigations in this research line that could improve the management of hemorrhagic stroke patients in clinical practice. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Graph Neural Networks for modelling breast biomechanical compression

arXiv

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arXiv 2024年

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

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. Copyright © 2024, The Authors. All rights reserved.

关键词： Tissue

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Improving brain atrophy quantification with deep learning from automated labels using tissue similarity priors

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computers in Biology and Medicine 2024年 179卷 108811-108811页

作者： Clèrigues, Albert Valverde, Sergi Oliver, Arnau Lladó, Xavier Institute of Computer Vision and Robotics University of Girona Spain Tensor Medical Girona Spain

Brain atrophy measurements derived from magnetic resonance imaging (MRI) are a promising marker for the diagnosis and prognosis of neurodegenerative pathologies such as Alzheimer's disease or multiple sclerosis. However, its use in individualized assessments is currently discouraged due to a series of technical and biological issues. In this work, we present a deep learning pipeline for segmentation-based brain atrophy quantification that improves upon the automated labels of the reference method from which it learns. This goal is achieved through tissue similarity regularization that exploits the a priori knowledge that scans from the same subject made within a short interval must have similar tissue volumes. To train the presented pipeline, we use unlabeled pairs of T1-weighted MRI scans having a tissue similarity prior, and generate the target brain tissue segmentations in a fully automated manner using the fsl_anat pipeline implemented in the FMRIB Software Library (FSL). Tissue similarity regularization is enforced during training through a weighted loss term that penalizes tissue volume differences between short-interval scan pairs from the same subject. In inference, the pipeline performs end-to-end skull stripping and brain tissue segmentation from a single T1-weighted MRI scan in its native space, i.e., without performing image interpolation. For longitudinal evaluation, each image is independently segmented first, and then measures of change are computed. We evaluate the presented pipeline in two different MRI datasets, MIRIAD and ADNI1, which have longitudinal and short-interval imaging from healthy controls (HC) and Alzheimer's disease (AD) subjects. In short-interval scan pairs, tissue similarity regularization reduces the quantification error and improves the consistency of measured tissue volumes. In the longitudinal case, the proposed pipeline shows reduced variability of atrophy measures and higher effect sizes of differences in annualized rates bet

关键词： Pipelines

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MRI Breast tissue segmentation using nnU-Net for biomechanical modeling

arXiv

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arXiv 2024年

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

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. © 2024, CC BY-SA.

关键词： Magnetic resonance imaging

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