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A client/server based online environment for manual segmentation of medical images

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arXiv 2019年

作者： Wild, Daniel Weber, Maximilian Egger, Jan Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Computer Algorithms for Medicine Lab Graz Austria

Segmentation is a key step in analyzing and processing medical images. Due to the low fault tolerance in medical imaging, manual segmentation remains the de facto standard in this domain. Besides, efforts to automate the segmentation process often rely on large amounts of manually labeled data. While existing software supporting manual segmentation is rich in features and delivers accurate results, the necessary time to set it up and get comfortable using it can pose a hurdle for the collection of large datasets. This work introduces a client/server based online environment, referred to as Studierfenster, that can be used to perform manual segmentations directly in a web browser. The aim of providing this functionality in the form of a web application is to ease the collection of ground truth segmentation datasets. Providing a tool that is quickly accessible and usable on a broad range of devices, offers the potential to accelerate this process. The manual segmentation workflow of Studierfenster consists of dragging and dropping the input file into the browser window and sliceby- slice outlining the object under consideration. The final segmentation can then be exported as a file storing its contours and as a binary segmentation mask. In order to evaluate the usability of Studierfenster, a user study was performed. The user study resulted in a mean of 6.3 out of 7.0 possible points given by users, when asked about their overall impression of the tool. The evaluation also provides insights into the results achievable with the tool in practice, by presenting two ground truth segmentations performed by physicians. Copyright © 2019, The Authors. All rights reserved.

关键词： Large dataset

Multicenter aortic vessel tree extraction using deep learning

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Multicenter aortic vessel tree extraction using deep learnin...

Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Scharinger, Bernhard Pepe, Antonio Jin, Yuan Gsaxner, Christina Li, Jianning Egger, Jan Graz University of Technology Institute for Computer Graphics and Vision Inffeldgasse 16c/II GrazA-8010 Austria Computer Algorithms for Medicine Laboratory Graz Austria Girardetstrasse 2 Essen45131 Germany Research Center for Connected Healthcare Big Data Zhejiang Lab Zhejiang Hangzhou311121 China

ISBN: (纸本)9781510660410

The aorta is the largest vessel of the human body and its pathological degenerations, such as dissections and aneurysms, can be life threatening. An automatic and fast segmentation of the aorta can therefore be a helpful tool to quickly identify an abnormal anatomy. The segmentation of the aortic vessel tree (AVT) typically requires extensive manual labor, but, in recent years, progress in deep learning techniques made the automation of this process viable. For this purpose, we tested different deep learning networks to segment the aortic vessel tree from computed tomography angiography (CTA) scans with a deep neural network consisting of an encoder-decoder architecture with skip connections and an optional self-attention block. The networks were trained on a dataset of 56 CTA scans from three different sources and resulted in Dice score similarities between 0.043-0.897. Generally, the classical U-Nets performed better than the ones containing a self-attention block, indicating that they might diminish performance for AVT segmentation. The quality of the resulting segmentations was highly dependent on the CTA image quality, especially on the contrast between the aorta and the surrounding tissues. However, the trained deep neural network can segment CTA scans well with limited computational resources and training data. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Deep neural networks

Deep learning - A first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact

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arXiv 2020年

作者： Egger, Jan Pepe, Antonio Gsaxner, Christina Jin, Yuan Li, Jianning Kern, Roman Institute of Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Computer Algorithms for Medicine Laboratory Graz Austria Department of Oral and Maxillofacial Surgery Medical University of Graz Graz Austria University Medicine Essen Essen Germany Research Center for Connected Healthcare Big Data Zhejiang Lab Zhejiang Hangzhou China Research Unit Experimental Neurotraumatology Department of Neurosurgery Medical University of Graz Graz Austria Knowledge Discovery Know-Center Graz Austria Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria

Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term 'deep learning', and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines and outline the research impact that they already have during a short period of time. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image,

关键词： Surveys

Bridging the Gap: Generalising State-of-the-Art U-Net Models to Sub-Saharan African Populations 1

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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： Amod, Alyssa R. Smith, Alexandra Joubert, Pearly Raymond, Confidence Zhang, Dong Anazodo, Udunna C. Motchon, Dodzi Mutsvangwa, Tinashe E. M. Quetin, Sébastien Brain Behaviour Unit Neuroscience Institute University of Cape Town Cape Town South Africa ShockLab Department of Mathematics and Applied Mathematics University of Cape Town Cape Town South Africa Applied Mathematics Division Stellenbosch University Stellenbosch South Africa Division of Biomedical Engineering Neuroscience Institute University of Cape Town Cape Town South Africa Medical Artificial Intelligence Lab Lagos Nigeria Lawson Health Research Institute LondonON Canada Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada Montreal Neurological Institute McGill University Montréal Canada Department of Medicine and Department of Clinical and Radiation Oncology University of Cape Town Cape Town South Africa Medical Physics Unit Department of Oncology McGill University Montréal Canada Montreal Institute for Learning Algorithms Montréal Canada

ISBN: (数字)9783031761638

ISBN: (纸本)9783031761621

A critical challenge for tumour segmentation models is the ability to adapt to diverse clinical settings, particularly when applied to poor quality neuroimaging data. The uncertainty surrounding this adaptation stems from the lack of representative datasets, leaving top-performing models without exposure to common artefacts found in MRI data throughout Sub-Saharan Africa (SSA). We replicated a framework that secured the 2nd position in the 2022 BraTS competition to investigate the impact of dataset composition on model performance and pursued four distinct approaches through training a model with various combinations of the BraTS-Africa and BraTS-Adult Glioma datasets. Notably, training on the smaller low-quality BraTS-Africa dataset alone yielded subpar results, and training on the larger high-quality BraTS-Adult Glioma dataset alone struggled to delineate oedematous tissue in the low-quality validation set. The most promising approach involved pre-training a model on high-quality neuroimages and then fine-tuning it on the smaller low-quality dataset. This approach took second place in the MICCAI BraTS Africa global challenge external testing phase. These findings underscore the significance of larger sample sizes and broad exposure to data in improving segmentation performance. Furthermore, we demonstrated there is potential for improving such models by fine-tuning them with a wider range of data locally. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Magnetic resonance imaging

Bridging the Gap: Generalising State-of-the-Art U-Net Models to Sub-Saharan African Populations

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arXiv 2023年

作者： Amod, Alyssa R. Smith, Alexandra Joubert, Pearly Raymond, Confidence Zhang, Dong Anazodo, Udunna C. Motchon, Dodzi Mutsvangwa, Tinashe E.M. Quetin, Sébastien Brain Behaviour Unit Neuroscience Institute University of Cape Town Cape Town South Africa ShockLab Dept. of Mathematics & Applied Mathematics UCT CT South Africa Applied Mathematics Division Stellenbosch University Stellenbosch South Africa Division of Biomedical Engineering Neuroscience Institute UCT CT South Africa Medical Artificial Intelligence Lab Lagos Nigeria Lawson Health Research Institute LondonON Canada Dept. of Electrical & Computer Engineering University of British Columbia Canada Montreal Neurological Institute McGill University Montréal Canada Dept. of Medicine Dept. Clinical & Radiation Oncology University of Cape Town Cape Town South Africa Medical Physics Unit Dept. of Oncology McGill University Montréal Canada Montreal Institute for Learning Algorithms Montréal Canada

A critical challenge for tumour segmentation models is the ability to adapt to diverse clinical settings, particularly when applied to poor quality neuroimaging data. The uncertainty surrounding this adaptation stems from the lack of representative datasets, leaving top-performing models without expo-sure to common artefacts found in MRI data throughout Sub-Saharan Africa (SSA). We replicated a framework that secured the 2nd position in the 2022 BraTS competition to investigate the impact of dataset composition on mod-el performance and pursued four distinct approaches through training a model with: BraTS-Africa data only (train_SSA, N=60), 2) BraTS-Adult Glioma data only (train_GLI, N=1251), 3) both datasets together (train_ALL, N=1311), and 4) through further training the train_GLI model with BraTS-Africa data (train_ftSSA). Notably, training on a smaller low-quality dataset alone (train_SSA) yielded subpar results, and training on a larger high-quality dataset alone (train_GLI) struggled to delineate oedematous tissue in the low-quality validation set. The most promising approach (train_ftSSA) involved pre-training a model on high-quality neuroimages and then fine-tuning it on the smaller low-quality dataset. This approach outperformed the others, ranking 2nd in the MICCAI BraTS Africa global challenge external testing phase. These findings underscore the significance of larger sample sizes and broad exposure to data in improving segmentation performance. Furthermore, we demonstrated there is potential for improving such models by fine-tuning them with a wider range of data locally. © 2023, CC BY.

关键词： Neuroimaging

The importance of transparency and reproducibility in artificial intelligence research

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arXiv 2020年

作者： Haibe-Kains, Benjamin Adam, George Alexandru Hosny, Ahmed Khodakarami, Farnoosh Waldron, Levi Wang, Bo McIntosh, Chris Kundaje, Anshul Greene, Casey S. Hoffman, Michael M. Leek, Jeffrey T. Huber, Wolfgang Brazma, Alvis Pineau, Joelle Tibshirani, Robert Hastie, Trevor Ioannidis, John P.A. Quackenbush, John Aerts, Hugo J.W.L. Princess Margaret Cancer Centre University Health Network TorontoON Canada Department of Medical Biophysics University of Toronto TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada Ontario Institute for Cancer Research TorontoON Canada Vector Institute for Artificial Intelligence TorontoON Canada Program Brigham and Women's Hospital Harvard Medical School BostonMA United States Radiation Oncology and Radiology Dana-Farber Cancer Institute Brigham and Women's Department of Epidemiology and Biostatistics Institute for Implementation Science in Population Health CUNY Graduate School of Public Health and Health Policy New YorkNY United States Peter Munk Cardiac Centre University Health Network TorontoON Canada Hospital Harvard Medical School BostonMA United States Department of Genetics Stanford University School of Medicine StanfordCA United States Dept. of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Childhood Cancer Data Lab Alex's Lemonade Stand Foundation PhiladelphiaPA United States Department of Biostatistics Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany European Molecular Biology Laboratory European Bioinformatics Institute EMBL-EBI Hinxton United Kingdom McGill University MontrealQC Canada Montreal Institute for Learning Algorithms QC Canada StanfordCA United States Department of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Departments of Medicine of Health Research and Policy and of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Department of Statistics Stanford University School of Humanities and Sciences StanfordCA United States Department of Biostatistics Harvard T.H Chan School of Public Health BostonMA Un

In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening. However, the lack of detailed methods and computer code undermines its scientific value. We identify obstacles hindering transparent and reproducible AI research as faced by McKinney et al and provide solutions with implications for the broader field. Copyright © 2020, The Authors. All rights reserved.

关键词： Artificial intelligence

Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo

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ACM Computing Surveys 1000年

作者： Yuan Jin Antonio Pepe Jianning Li Christina Gsaxner Yuxuan Chen Behrus Puladi Fen-hua Zhao Kelsey Pomykala Jens Kleesiek Alejandro Frangi Jan Egger Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Research Centre for Frontier Fundamental Studies Zhejiang Lab Hangzhou China Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Institute for Artificial Intelligence in Medicine Essen University Hospital (AöR) Essen Germany Zhejiang Laboratory of Philosophy and Social Sciences - Laboratory of Intelligent Society and Governance Zhejiang Lab Hangzhou China Institute of Medical Informatics RWTH Aachen University Aachen Germany Wenzhou Medical University Affiliated Dongyang Hospital Dongyang China Cancer Research Center Cologne Essen Essen Germany German Cancer Consortium Heidelberg Germany Christabel Pankhurst Institute The University of Manchester Manchester United Kingdom of Great Britain and Northern Ireland School of Health Sciences The University of Manchester Division of Informatics Imaging and Data Sciences Manchester United Kingdom of Great Britain and Northern Ireland The University of Manchester School of Computer Science Manchester United Kingdom of Great Britain and Northern Ireland Manchester University NHS Foundation Trust Manchester United Kingdom of Great Britain and Northern Ireland Insitute of Artificial Intelligence in Medicine Essen University Hospital (AöR) Essen Germany Computer Algorithms for Medicine Laboratory Graz Austria Cancer Research Center Cologne Essen (CCCE) Essen Germany

The aortic vessel tree, composed of the aorta and its branches, is crucial for blood supply to the body. Aortic diseases, such as aneurysms and dissections, can lead to life-threatening ruptures, often requiring open surgery. Therefore, patients commonly undergo treatment under constant monitoring, which requires regular inspections of the vessels through medical imaging techniques. Overlapping and comparing aortic vessel tree geometries from consecutive images allows for tracking changes in both the aorta and its branches. Manual reconstruction of the vessel tree is time-consuming and impractical in clinical settings. In contrast, automatic or semi-automatic segmentation algorithms can perform this task much faster, making them suitable for routine clinical use. This paper systematically reviews methods for the automatic and semi-automatic segmentation of the aortic vessel tree, concluding with a discussion on their clinical applicability, the current research landscape, and ongoing challenges.

关键词： Aorta Vessel Tree Machine Learning Deep Learning Segmentation Analysis Cardiovascular Disease Aneurysm Dissection Computed Tomography.