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Enhanced Data Augmentation Using Synthetic Data for Brain Tumour Segmentation 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： Ferreira, André Solak, Naida Li, Jianning Dammann, Philipp Kleesiek, Jens Alves, Victor Egger, Jan Center Algoritmi/LASI University of Minho Braga4710-057 Portugal Computer Algorithms for Medicine Laboratory Graz Austria University Medicine Essen Girardetstraße 2 Essen45131 Germany University Medicine Essen Hufelandstraße 55 Essen45147 Germany Partner Site Essen Hufelandstraße 55 Essen45147 Germany Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 Graz8010 Austria Department of Neurosurgery and Spine Surgery University Hospital Essen Essen Germany

ISBN: (数字)9783031761638

ISBN: (纸本)9783031761621

Deep Learning is the state-of-the-art technology for segmenting brain tumours. However, this requires a lot of high-quality data, which is difficult to obtain, especially in the medical field. Therefore, our solutions address this problem by using unconventional mechanisms for data augmentation. Generative adversarial networks and registration are used to massively increase the amount of available samples for training three different deep learning models for adult glioma segmentation, the Task 1 of the BraTS 2023 challenge. The first model is the standard nnU-Net, the second is the Swin UNETR and the third is the winning solution of the BraTS 2021 Challenge. The entire pipeline is built on the nnU-Net implementation, except for the generation of the synthetic data. The use of convolutional algorithms and transformers is able to fill each other’s knowledge gaps. Our solution achieves the lesion-wise Dice metric of 0.8851, 0.8719, 0.8685 and lesion-wise HD95 of 22.838, 22.974, 16.713 (whole tumour, tumour core and enhancing tumour) on the testing cohort. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Generative adversarial networks

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BLESSEMFLOOD21: ADVANCING FLOOD ANALYSIS WITH A HIGH-RESOLUTION GEOREFERENCED DATASET FOR HUMANITARIAN AID SUPPORT

arXiv

引用

arXiv 2024年

作者： Polushko, Vladyslav Jenal, Alexander Bongartz, Jens Weber, Immanuel Hatic, Damjan Rösch, Ronald März, Thomas Rauhut, Markus Weinmann, Andreas Image Processing Department Fraunhofer ITWM Kaiserslautern Germany Working Group Algorithms for Computer Vision Imaging and Data Analysis Hochschule Darmstadt Darmstadt Germany Center for Machine Learning and Sensor Technology Hochschule Koblenz Remagen Germany

Floods are an increasingly common global threat, causing emergencies and severe damage to infrastructure. During crises, organisations such as the World Food Programme use remotely sensed imagery, typically obtained through drones, for rapid situational analysis to plan life-saving actions. computer vision tools are needed to support task force experts on-site in the evaluation of the imagery to improve their efficiency and to allocate resources strategically. We introduce the BlessemFlood21 dataset to stimulate research on efficient flood detection tools. The imagery was acquired during the 2021 Erftstadt-Blessem flooding event and consists of high-resolution and georeferenced RGB-NIR images. In the resulting RGB dataset, the images are supplemented with detailed water masks, obtained via a semi-supervised human-in-the-loop technique, where in particular the NIR information is leveraged to classify pixels as either water or non-water. We evaluate our dataset by training and testing established Deep Learning models for semantic segmentation. With BlessemFlood21 we provide labeled high-resolution RGB data and a baseline for further development of algorithmic solutions tailored to flood detection in RGB imagery. © 2024, CC BY-NC-ND.

关键词： Floods

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Dynamic Semantic-Based Spatial Graph Convolution Network for Skeleton-Based Human Action Recognition 38

Dynamic Semantic-Based Spatial Graph Convolution Network for...

引用

38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Xie, Jianyang Meng, Yanda Zhao, Yitian Nguyen, Anh Yang, Xiaoyun Zheng, Yalin CDT in Distributed Algorithms School of EEECS University of Liverpool United Kingdom Department of Eye and Vision Sciences University of Liverpool Liverpool United Kingdom Department of Computer Sciences University of Liverpool Liverpool United Kingdom Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and Engineering CAS Cixi China Remark AI UK Limited London United Kingdom Liverpool Centre for Cardiovascular Science Liverpool United Kingdom

ISBN: (纸本)1577358872

Graph convolutional networks (GCNs) have attracted great attention and achieved remarkable performance in skeleton-based action recognition. However, most of the previous works are designed to refine skeleton topology without considering the types of different joints and edges, making them infeasible to represent the semantic information. In this paper, we proposed a dynamic semantic-based graph convolution network (DS-GCN) for skeleton-based human action recognition, where the joints and edge types were encoded in the skeleton topology in an implicit way. Specifically, two semantic modules, the joints type-aware adaptive topology and the edge type-aware adaptive topology, were proposed. Combining proposed semantics modules with temporal convolution, a powerful framework named DS-GCN was developed for skeleton-based action recognition. Extensive experiments in two datasets, NTU-RGB+D and Kinetics-400 show that the proposed semantic modules were generalized enough to be utilized in various backbones for boosting recognition accuracy. Meanwhile, the proposed DS-GCN notably outperformed state-of-the-art methods. The code is released here https://***/davelailai/DS-GCN. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Convolution

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CNN-based Game State Detection for a Foosball Table

arXiv

引用

arXiv 2024年

作者： Hagens, David Knaup, Jan M. Hergenröther, Elke Weinmann, Andreas ORDIX AG Karl-Schurz-Straße 19a Paderborn33100 Germany Darmstadt University of Applied Sciences Schöfferstraße 3 Darmstadt64295 Germany Algorithms for Computer Vision Imaging and Data Analysis Group Hochschule Darmstadt Germany

The automation of games using Deep Reinforcement Learning Strategies (DRL) is a well-known challenge in AI research. While for feature extraction in a video game typically the whole image is used, this is hardly practical for many real world games. Instead, using a smaller game state reducing the dimension of the parameter space to include essential parameters only seems to be a promising approach. In the game of Foosball, a compact and comprehensive game state description consists of the positional shifts and rotations of the figures and the position of the ball over time. In particular, velocities and accelerations can be derived from consecutive time samples of the game state. In this paper, a figure detection system to determine the game state in Foosball is presented. We capture a dataset containing the rotations of the rods which were measured using accelerometers and the positional shifts were derived using traditional computer vision techniques (in a laboratory setting). This dataset is utilized to train Convolutional Neural Network (CNN) based end-to-end regression models to predict the rotations and shifts of each rod. We present an evaluation of our system using different state-of-the-art CNNs as base architectures for the regression model. We show that our system is able to predict the game state with high accuracy. By providing data for both black and white teams, the presented system is intended to provide the required data for future developments of Imitation Learning techniques w.r.t. to observing human players. © 2024, CC BY-NC-SA.

关键词： computer vision

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Automated Bioacoustic Monitoring for South African Bird Species on Unlabeled Data

arXiv

引用

arXiv 2024年

作者： Doell, Michael Kuehn, Dominik Suessle, Vanessa Burnett, Matthew J. Downs, Colleen T. Weinmann, Andreas Hergenroether, Elke Department of Computer Science University of Applied Sciences Darmstadt Schoefferstrasse 3 Darmstadt Germany Centre for Functional Biodiversity School of Life Sciences University of KwaZulu-Natal P/Bag X01 Scottsville Pietermaritzburg3209 South Africa Algorithms for Computer Vision Imaging and Data Analysis Group University of Applied Sciences Darmstadt Schoefferstrasse 3 Darmstadt Germany

Analyses for biodiversity monitoring based on passive acoustic monitoring (PAM) recordings is time-consuming and challenged by the presence of background noise in recordings. Existing models for sound event detection (SED) worked only on certain avian species and the development of further models required labeled data. The developed framework automatically extracted labeled data from available platforms for selected avian species. The labeled data were embedded into recordings, including environmental sounds and noise, and were used to train convolutional recurrent neural network (CRNN) models. The models were evaluated on unprocessed real world data recorded in urban KwaZulu-Natal habitats. The Adapted SED-CRNN model reached a F1 score of 0.73, demonstrating its efficiency under noisy, real-world conditions. The proposed approach to automatically extract labeled data for chosen avian species enables an easy adaption of PAM to other species and habitats for future conservation projects. © 2024, CC BY-NC-ND.

关键词： Biodiversity

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Remote Sensing Imagery for Flood Detection: Exploration of Augmentation Strategies

arXiv

引用

arXiv 2025年

作者： Polushko, Vladyslav Hatic, Damjan Rösch, Ronald März, Thomas Rauhut, Markus Weinmann, Andreas Image Processing Department Fraunhofer ITWM Kaiserslautern Germany Algorithms for Computer Vision Imaging and Data Analysis Group Hochschule Darmstadt Darmstadt Germany

Floods cause serious problems around the world. Responding quickly and effectively requires accurate and timely information about the affected areas. The effective use of Remote Sensing images for accurate flood detection requires specific detection methods. Typically, Deep Neural Networks are employed, which are trained on specific datasets. For the purpose of river flood detection in RGB imagery, we use the BlessemFlood21 dataset. We here explore the use of different augmentation strategies, ranging from basic approaches to more complex techniques, including optical distortion. By identifying effective strategies, we aim to refine the training process of state-of-the-art Deep Learning segmentation networks. © 2025, CC BY-NC-ND.

关键词： Deep neural networks

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CNN-Based Flank Predictor for Quadruped Animal Species

引用

IET computer vision 2025年第1期19卷

作者： Suessle, Vanessa Heurich, Marco Downs, Colleen T. Weinmann, Andreas Hergenroether, Elke Department of Computer Science University of Applied Sciences Darmstadt Darmstadt Germany School of Life Sciences University of KwaZulu-Natal Pietermaritzburg South Africa Algorithms for Computer Vision Imaging and Data Analysis Group University of Applied Sciences Darmstadt Darmstadt Germany Data Science Institute European University of Technology European Union Netherlands Department of National Park Monitoring and Animal Management Bavarian Forest National Park Grafenau Germany Department of Wildlife Ecology and Management Faculty of Environment and Natural Resources University of Freiburg Freiburg Germany Faculty of Applied Ecology Agricultural Sciences and Biotechnology University of Inland Norway Evenstad Norway

The bilateral asymmetry of flanks, where the sides of an animal with unique visual markings are independently patterned, complicates tasks such as individual identification. Automatically generating additional information on the visible side of the animal would improve the accuracy of individual identification. In this study, we used transfer learning on popular convolutional neural network (CNN) image classification architectures to train a flank predictor that predicted the visible flank of quadruped mammalian species in images. We automatically derived the data labels from existing datasets initially labelled for animal pose estimation. The developed models were evaluated across various scenarios involving unseen quadruped species in familiar and unfamiliar habitats. As a real-world scenario, we used a dataset of manually labelled Eurasian lynx (Lynx lynx) from camera traps in the Bavarian Forest National Park, Germany, to evaluate the model. The best model on data obtained in the field was trained on a MobileNetV2 architecture. It achieved an accuracy of 91.7% for the unseen/untrained species lynx in a complex unseen/untrained habitat with challenging light conditions. The developed flank predictor was designed to be embedded as a preprocessing step for automated analysis of camera trap datasets to enhance tasks such as individual identification. © 2025 The Author(s). IET computer vision published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Convolutional neural networks

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How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation

arXiv

引用

arXiv 2024年

作者： Ferreira, André Solak, Naida Li, Jianning Dammann, Philipp Kleesiek, Jens Alves, Victor Egger, Jan Center ALGORITMI/LASI University of Minho Braga4710-057 Portugal Computer Algorithms for Medicine Laboratory Graz Austria University Medicine Essen Girardetstraße 2 Essen45131 Germany University Medicine Essen Hufelandstraße 55 Essen45147 Germany Partner Site Essen Hufelandstraße 55 Essen45147 Germany Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 Graz8010 Austria Department of Neurosurgery and Spine Surgery University Hospital Essen Essen Germany

Deep Learning is the state-of-the-art technology for segmenting brain tumours. However, this requires a lot of high-quality data, which is difficult to obtain, especially in the medical field. Therefore, our solutions address this problem by using unconventional mechanisms for data augmentation. Generative adversarial networks and registration are used to massively increase the amount of available samples for training three different deep learning models for brain tumour segmentation, the first task of the BraTS2023 challenge. The first model is the standard nnU-Net, the second is the Swin UNETR and the third is the winning solution of the BraTS 2021 Challenge. The entire pipeline is built on the nnU-Net implementation, except for the generation of the synthetic data. The use of convolutional algorithms and transformers is able to fill each other's knowledge gaps. Using the new metric, our best solution achieves the dice results 0.9005, 0.8673, 0.8509 and HD95 14.940, 14.467, 17.699 (whole tumour, tumour core and enhancing tumour) in the validation set. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Design and Development of a Web-based Tool for Inpainting of Dissected Aortae in Angiography Images

arXiv

引用

arXiv 2020年

作者： Prutsch, Alexander Pepe, Antonio Egger, Jan Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Computer Algorithms for Medicine Lab Graz Austria

Medical imaging is an important tool for the diagnosis and the evaluation of an aortic dissection (AD);a serious condition of the aorta, which could lead to a life-threatening aortic rupture. AD patients need life-long medical monitoring of the aortic enlargement and of the disease progression, subsequent to the diagnosis of the aortic dissection. Since there is a lack of healthy-dissected image pairs from medical studies, the application of inpainting techniques offers an alternative source for generating them by doing a virtual regression from dissected aortae to healthy aortae;an indirect way to study the origin of the disease. The proposed inpainting tool combines a neural network, which was trained on the task of inpainting aortic dissections, with an easy-to-use user interface. To achieve this goal, the inpainting tool has been integrated within the 3D medical image viewer of StudierFenster (***). By designing the tool as a web application, we simplify the usage of the neural network and reduce the initial learning curve. Copyright © 2020, The Authors. All rights reserved.

关键词： Blood vessels

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Tumor likelihood estimation on MRI prostate data by utilizing k-Space information

arXiv

引用

arXiv 2024年

作者： Rempe, Moritz Hörst, Fabian Seibold, Constantin Hadaschik, Boris Schlimbach, Marco Egger, Jan Kröninger, Kevin Breuer, Felix Blaimer, Martin Kleesiek, Jens University Hospital Essen Girardetstraße 2 Essen45131 Germany University Medicine Essen Hufelandstraße 55 Essen45147 Germany Fraunhofer Institute for Integrated Circuits IIS Am Hubland Würzburg97074 Germany Department of Urology University Hospital Essen Hufelandstraße 55 Essen45147 Germany Department of Physics The Technical University Dortmund Otto-Hahn-Straße 4a Dortmund44227 Germany Computer Algorithms for Medicine Laboratory Graz8010 Austria Institute of Computer Graphics and Vision Graz Austria University of Technology Inffeldgasse 16 Graz8010 Austria Partner Site Essen Hufelandstraße 55 Essen45147 Germany

We present a novel preprocessing and prediction pipeline for the classification of magnetic resonance imaging (MRI) that takes advantage of the information rich complex valued k-Space. Using a publicly available MRI raw dataset with 312 subject and a total of 9508 slices, we show the advantage of utilizing the k-Space for better prostate cancer likelihood estimation in comparison to just using the magnitudinal information in the image domain, with an AUROC of 86.1%±1.8%. Additionally, by using high undersampling rates and a simple principal component analysis (PCA) for coil compression, we reduce the time needed for reconstruction by avoiding the time intensive GRAPPA reconstruction algorithm. By using digital undersampling for our experiments, we show that scanning and reconstruction time could be reduced. Even with an undersampling factor of 16, our approach achieves meaningful results, with an AUROC of 71.4%±2.9%, using the PCA coil combination and taking into account the k-Space information. With this study, we were able to show the feasibility of preserving phase and k-Space information, with consistent results. Besides preserving valuable information for further diagnostics, this approach can work without the time intensive ADC and reconstruction calculations, greatly reducing the post processing, as well as potential scanning time, increasing patient comfort and allowing a close to real-time prediction. © 2024, CC BY.

关键词： Magnetic resonance imaging

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