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Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Pepe, Antonio Schussnig, Richard Li, Jianning Gsaxner, Christina Chen, Xiaojun Fries, Thomas-Peter Egger, Jan Stanford University School of Medicine Department of Radiology 300 Pasteur Dr StanfordCA United States Graz University of Technology Institute of Computer Graphics and Vision Inffeldgasse 16c/II GrazA-8010 Austria Computer Algorithms for Medicine Laboratory GrazA-8010 Austria Graz University of Technology Institute of Structural Analysis Lessingstraße 25/II GrazA-8010 Austria Medical University of Graz Department of Oral and Maxillofacial Surgery Auenbruggerplatz 5/1 GrazA-8036 Austria Shanghai Jiao Tong University School of Mechanical Engineering 800 Dong Chuan Road Shanghai200240 China

ISBN: (纸本)9781510634015

Volumetric examinations of the aorta are nowadays of crucial importance for the management of critical pathologies such as aortic dissection, aortic aneurism, and other pathologies, which affect the morphology of the artery. These examinations usually begin with the acquisition of a Computed Tomography Angiography (CTA) scan from the patient, which is later on postprocessed to reconstruct the 3D geometry of the aorta. The first postprocessing step is referred to as segmentation. Different algorithms have been suggested for the segmentation of the aorta;including interactive methods, as well as fully automatic methods. Interactive methods need to be fine-tuned on each single CTA scan and result in longer duration of the process, whereas fully automatic methods require the possession of a large amount of labeled training data. In this work, we introduce a hybrid approach by combining a deep learning method with a consolidated interaction technique. In particular, we trained a 2D and a 3D U-Net on a limited number of patches extracted from 25 labeled CTA scans. Afterwards, we use an interactive approach, which consists in defining a region of interest (ROI) by just placing a seed point. This seed point is later used as the center of a 2D or 3D patch to be fed to the 2D or 3D U-Net, respectively. Due to the low content variation of these patches, this method allows to correctly segment the ROIs without the need for parameter tuning for each dataset and with a smaller training dataset, requiring the same minimal interaction as state-of-the-art interactive methods. Later on, the new segmented CTA scans can be further used to train a convolutional network for a fully automatic approach. © 2020 SPIE

关键词： Angiography

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Why is the Winner the Best?

Why is the Winner the Best?

引用

Conference on computer vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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arXiv

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arXiv 2023年

作者： Bilgic, Berkin Costagli, Mauro Chan, Kwok-Shing Duyn, Jeff Langkammer, Christian Lee, Jongho Li, Xu Liu, Chunlei Marques, José P. Milovic, Carlos Robinson, Simon Schweser, Ferdinand Shmueli, Karin Spincemaille, Pascal Straub, Sina van Zijl, Peter Wang, Yi Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School CharlestownMA United States University of Genoa Genoa Italy Laboratory of Medical Physics and Magnetic Resonance IRCCS Stella Maris Pisa Italy Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Netherlands Advanced MRI Section NINDS National Institutes of Health BethesdaMD United States Department of Neurology Medical University of Graz Graz Austria Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States F.M. Kirby Research Center for Functional Brain Imaging Kennedy Krieger Institute BaltimoreMD United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA United States Helen Wills Neuroscience Institute University of California BerkeleyCA United States Pontificia Universidad Catolica de Valparaiso Valparaiso Chile High Field MR Centre Department of Biomedical Imaging and Image-Guided Therapy Medical University of Vienna Austria Buffalo Neuroimaging Analysis Center Department of Neurology Jacobs School of Medicine and Biomedical Sciences the University at Buffalo BuffaloNY United States Center for Biomedical Imaging Clinical and Translational Science Institute the University at Buffalo BuffaloNY United States Department of Medical Physics and Biomedical Engineering University College London London United Kingdom MRI Research Institute Department of Radiology Weill Cornell Medicine New YorkNY United States Department of Radiology Mayo Clinic JacksonvilleFL United States MRI Research Institute Departments of Radiology and Biomedical Engineering Cornell University New YorkNY United States

This article provides recommendations for implementing quantitative susceptibility mapping (QSM) for clinical brain research. It is a consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available give rise to the need in the neuroimaging community for guidelines on implementation. This article describes relevant considerations and provides specific implementation recommendations for all steps in QSM data acquisition, processing, analysis, and presentation in scientific publications. We recommend that data be acquired using a monopolar 3D multi-echo GRE sequence, that phase images be saved and exported in DICOM format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields should be removed within the brain mask using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of whole brain as a region of interest in the analysis, and QSM results should be reported with – as a minimum – the acquisition and processing specifications listed in the last section of the article. These recommendations should facilitate clinical QSM research and lead to increased harmonization in data acquisition, analysis, and reporting. © 2023, CC BY.

关键词： Magnetic resonance imaging

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Fetal Brain Tissue Annotation and Segmentation Challenge Results

arXiv

引用

arXiv 2022年

作者： Payette, Kelly Li, Hongwei De Dumast, Priscille Licandro, Roxane Ji, Hui Siddiquee, Md Mahfuzur Rahman Xu, Daguang Myronenko, Andriy Liu, Hao Pei, Yuchen Wang, Lisheng Peng, Ying Xie, Juanying Zhang, Huiquan Dong, Guiming Fu, Hao Wang, Guotai Rieu, ZunHyan Kim, Donghyeon Kim, Hyun Gi Karimi, Davood Gholipour, Ali Torres, Helena R. Oliveira, Bruno Vilaça, João L. Lin, Yang Avisdris, Netanell Ben-Zvi, Ori Bashat, Dafna Ben Fidon, Lucas Aertsen, Michael Vercauteren, Tom Sobotka, Daniel Langs, Georg Alenyà, Mireia Villanueva, Maria Inmaculada Camara, Oscar Fadida, Bella Specktor Joskowicz, Leo Weibin, Liao Yi, Lv Xuesong, Li Mazher, Moona Qayyum, Abdul Puig, Domenec Kebiri, Hamza Zhang, Zelin Xu, Xinyi Wu, Dan Liao, KuanLun Wu, YiXuan Chen, JinTai Xu, Yunzhi Zhao, Li Vasung, Lana Menze, Bjoern Cuadra, Meritxell Bach Jakab, Andras Center for MR Research University Children's Hospital Zurich University of Zurich Zurich Switzerland Neuroscience Center Zurich University of Zurich Zurich Switzerland Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University of Munich Munich Germany Medical Image Analysis Laboratory Department of Diagnostic and Interventional Radiology Lausanne University Hospital University of Lausanne Lausanne Switzerland CIBM Center for Biomedical Imaging Lausanne Switzerland Laboratory for Computational Neuroimaging Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School CharlestownMA United States Medical University of Vienna Vienna Austria Arizona State University United States NVIDIA United States Shanghai Jiaotong University China School of Computer Science Shaanxi Normal University Xi'An710119 China Research Institute NEUROPHET Inc. Seoul06247 Korea Republic of Department of Radiology The Catholic University of Korea Eunpyeong St. Mary's Hospital Seoul06247 Korea Republic of Boston Children's Hospital Harvard Medical School BostonMA United States 2Ai - School of Technology IPCA Barcelos Portugal Algoritmi Center School of Engineering University of Minho Guimarães Portugal School of Medicine University of Minho Braga Portugal ICVS 3B's - PT Government Associate Laboratory Guimarães Braga Portugal School of Computer Science and Engineering The Hebrew University of Jerusalem Israel Sagol Brain Institute Tel Aviv Sourasky Medical Center Israel Sagol School of Neuroscience Tel Aviv University Israel Sackler Faculty of Medicine Tel Aviv University Israel School of Biomedical Engineering & Imaging Sciences King's College London LondonSE1 7EU United Kingdom Department of Radiology University Hospitals Leuven Leuven3000 Belgium Computational Imaging Research Lab Department of Biomedical Imaging and Image-guided Therapy Medical

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero. © 2022, CC BY.

关键词： Magnetic resonance imaging

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NTIRE 2021 depth guided image relighting challenge

arXiv

引用

arXiv 2021年

作者： Helou, Majed El Zhou, Ruofan Süsstrunk, Sabine Timofte, Radu Suin, Maitreya Rajagopalan, A.N. Wang, Yuanzhi Lu, Tao Zhang, Yanduo Wu, Yuntao Yang, Hao-Hsiang Chen, Wei-Ting Kuo, Sy-Yen Luo, Hao-Lun Zhang, Zhiguang Luo, Zhipeng He, Jianye Zhu, Zuo-Liang Li, Zhen Qiu, Jia-Xiong Kuang, Zeng-Sheng Lu, Cheng-Ze Cheng, Ming-Ming Shao, Xiu-Li Li, Chenghua Ding, Bosong Qian, Wanli Li, Fangya Li, Fu Deng, Ruifeng Lin, Tianwei Liu, Songhua Li, Xin He, Dongliang Yazdani, Amirsaeed Guo, Tiantong Monga, Vishal Nsampi, Ntumba Elie Hu, Zhongyun Wang, Qing Nathan, Sabari Kansal, Priya Zhao, Tongtong Zhao, Shanshan EPFL Switzerland ETH Zürich Switzerland ASUS Intelligent Cloud Services ASUSTeK Computer Inc Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Institute of Automation Chinese Academy of Sciences China Couger Inc Co. Ltd China United States Indian Institute of Technology Madras India Nankai University China Computer Vision and Computational Photography Group School of Computer science Northwestern Polytechnical University China Dalian Maritime University China China Everbright Bank China Baidu Inc China Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology China

image relighting is attracting increasing interest due to its various applications. From a research perspective, image relighting can be exploited to conduct both image normalization for domain adaptation, and also for data augmentation. It also has multiple direct uses for photo montage and aesthetic enhancement. In this paper, we review the NTIRE 2021 depth guided image relighting challenge. We rely on the VIDIT dataset for each of our two challenge tracks, including depth information. The first track is on one-to-one relighting where the goal is to transform the illumination setup of an input image (color temperature and light source position) to the target illumination setup. In the second track, the any-to-any relighting challenge, the objective is to transform the illumination settings of the input image to match those of another guide image, similar to style transfer. In both tracks, participants were given depth information about the captured scenes. We had nearly 250 registered participants, leading to 18 confirmed team submissions in the final competition stage. The competitions, methods, and final results are presented in this paper. Copyright © 2021, The Authors. All rights reserved.

关键词： Light sources

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Common and Rare Fundus Diseases Identification Using vision-Language Foundation Model with Knowledge of Over 400 Diseases

arXiv

引用

arXiv 2024年

作者： Wang, Meng Lin, Tian Lin, Aidi Yu, Kai Peng, Yuanyuan Wang, Lianyu Chen, Cheng Zou, Ke Liang, Huiyu Chen, Man Yao, Xue Zhang, Meiqin Huang, Binwei Zheng, Chaoxin Zhang, Peixin Chen, Wei Luo, Yilong Chen, Yifan Xia, Honghe Shi, Tingkun Zhang, Qi Guo, Jinming Chen, Xiaolin Wang, Jingcheng Chung Tham, Yih Liu, Dianbo Wong, Wendy Thakur, Sahil Fenner, Beau Fang, Danqi Liu, Siying Liu, Qingyun Huang, Yuqiang Zeng, Hongqiang Meng, Yanda Zhou, Yukun Jiang, Zehua Qiu, Minghui Zhang, Changqing Chen, Xinjian Wang, Sophia Y. Lee, Cecilia S. Sobrin, Lucia Cheung, Carol Y. Pang, Chi Pui Keane, Pearse A. Cheng, Ching-Yu Chen, Haoyu Fu, Huazhu Centre for Innovation & Precision Eye Health Yong Loo Lin School of Medicine National University of Singapore Singapore117549 Singapore Department of Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore117549 Singapore Joint Shantou International Eye Center Shantou University Chinese University of Hong Kong Guangdong Shantou515041 China Department of Radiology University of Pennsylvania PhiladelphiaPA19104 United States School of Biomedical Engineering Anhui Medical University Anhui Hefei230032 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing211100 China Center of Advanced Medical Computing and Analysis Massachusetts General Hospital Harvard Medical School BostonMA02114 United States National Key Laboratory of Fundamental Science on Synthetic Vision College of Computer Science Sichuan University Sichuan Chengdu610065 China Big Vision Medical Technology Ltd. Suzhou China Singapore Eye Research Institute Singapore National Eye Centre Singapore Duke-NUS Medical School Singapore Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong 999077 Hong Kong Shenzhen Longgang E.N.T Hospital Guangdong Shenzhen518172 China Dongguan Songshan Lake Central Hospital Guangdong Dongguan523326 China Department of Computer Science University of Exeter ExeterEX4 4RN United Kingdom Centre for Medical Image Computing University College London London United Kingdom NIHR Biomedical Research Centre Moorfields Eye Hospital NHS Foundation Trust London United Kingdom Institute of Ophthalmology University College London London United Kingdom Tsinghua Medicine of Tsinghua University Beijing100084 China School of Clinical Medicine Beijing Tsinghua Changgung Hospital Beijing102218 China Foshan Aier Eye Hospital Guangdong Foshan528000 China College of Intelligence and Computing Tianjin University Tianjin300350 China School of Electr

Previous foundation models for retinal images were pre-trained with limited disease categories and knowledge base. Here we introduce RetiZero, a vision-language foundation model that leverages knowledge from over 400 fundus diseases. For RetiZero's pre-training, we compiled 341,896 fundus images paired with text descriptions, sourced from public datasets, ophthalmic literature, and online resources, encompassing a diverse range of diseases across multiple ethnicities and countries. RetiZero exhibits superior performance in several downstream tasks, including zero-shot disease recognition, image-to-image retrieval, and internal- and cross-domain disease identification. In zero-shot scenarios, RetiZero achieves Top5 accuracy scores of 0.8430 for 15 fundus diseases and 0.7561 for 52 fundus diseases. For image retrieval, it achieves Top5 scores of 0.9500 and 0.8860 for the same disease sets, respectively. Clinical evaluations show that RetiZero's Top3 zero-shot performance surpasses the average of 19 ophthalmologists from Singapore, China, and the United States. Furthermore, RetiZero significantly enhances clinicians' accuracy in diagnosing fundus disease. These findings underscore the value of integrating the RetiZero foundation model into clinical settings, where a variety of fundus diseases are encountered. © 2024, CC0.

关键词： Ophthalmology

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

引用

arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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Random-valued impulse denoising using a fast l 1-minimization-based image inpainting technique

Random-valued impulse denoising using a fast l 1-minimizatio...

引用

International Conference on Emerging Trends and Advances in electrical engineering and Renewable Energy, ETAEERE 2016

作者： Kalita, Mayuri Deka, Bhabesh Computer Vision and Image Processing Laboratory Department of Electronics and Communication Engineering Tezpur University TezpurAssam784028 India

ISBN: (纸本)9789811047640

In this chapter, an image inpainting approach based on l 1-norm regularization is presented for the estimation of pixels corrupted by the random-valued impulse noise. It is a two-stage reconstruction scheme. First, a reasonably accurate random-valued impulse detection scheme is applied to detect the corrupted pixels. Next, the corrupted pixels are treated as missing pixels and replaced by using an image inpainting technique. The inpainting method is based on the fast iterative shrinkage thresholding algorithm (FISTA). The proposed method is fast and experimental results show that it is robust to non-Gaussian and nonadditive degradations like the random-valued impulse noise. It also outperforms similar random-valued impulse denoising schemes in terms of computational complexity while preserving the image quality. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Pixels

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Weighted wavelet tree sparsity regularization for compressed sensing magnetic resonance image reconstruction

Weighted wavelet tree sparsity regularization for compressed...

引用

International Conference on Emerging Trends and Advances in electrical engineering and Renewable Energy, ETAEERE 2016

作者： Deka, Bhabesh Datta, Sumit Computer Vision and Image Processing Laboratory Department of Electronics and Communication Engineering Tezpur University TezpurAssam784028 India

ISBN: (纸本)9789811047640

Compressed sensing in magnetic resonance imaging (CS-MRI) improves the MRI scan time by acquiring only a few k-space samples and then reconstructs the image using a nonlinear procedure from the highly undersampled measurements. Besides the standard wavelet sparsity, MR images are also found to exhibit tree sparsity across various scales of the wavelet decomposition which are generally modeled as overlapping group sparsity. In this chapter, we propose a novel iteratively weighted wavelet tree sparsity based CS-MRI reconstruction technique to estimate MR images from highly undersampled Fourier measurements. Simulations on various real MR images show that the proposed technique offers significant improvements compared to the state-of-the-art either in terms of visual quality or k-space measurements with the same reconstruction time. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Magnetic resonance imaging

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HoVer-Trans: Anatomy-aware HoVer-Transformer for ROI-free Breast Cancer Diagnosis in Ultrasound images

arXiv

引用

arXiv 2022年

作者： Mo, Yuhao Han, Chu Liu, Yu Liu, Min Shi, Zhenwei Lin, Jiatai Zhao, Bingchao Huang, Chunwang Qiu, Bingjiang Cui, Yanfen Wu, Lei Pan, Xipeng Xu, Zeyan Huang, Xiaomei Liu, Zaiyi Wang, Ying Liang, Changhong The School of Medicine South China University of Technology Guangdong Guangzhou510006 China The Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Guangdong Prov. Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The Department of Ultrasound Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China The Department of Medical Ultrasonics The First Hospital of Guangzhou Medical University Guangdong Guangzhou China The Department of Ultrasound Sun Yat-sen University Cancer Center State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangzhou510060 China

Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiationfree and low-cost properties. However, the diagnostic accuracy of breast cancer is still limited due to its inherent limitations. It would be a tremendous success if we can precisely diagnose breast cancer by breast ultrasound images (BUS). Many learningbased computer-aided diagnostic methods have been proposed to achieve breast cancer diagnosis/lesion classification. However, most of them require a pre-define ROI and then classify the lesion inside the ROI. Conventional classification backbones, such as VGG16 and ResNet50, can achieve promising classification results with no ROI requirement. But these models lack interpretability, thus restricting their use in clinical practice. In this study, we propose a novel ROI-free model for breast cancer diagnosis in ultrasound images with interpretable feature representations. We leverage the anatomical prior knowledge that malignant and benign tumors have different spatial relationships between different tissue layers, and propose a HoVer-Transformer to formulate this prior knowledge. The proposed HoVer-Trans block extracts the inter-and intra-layer spatial information horizontally and vertically. We conduct and release an open dataset GDPH&SYSUCC for breast cancer diagnosis in BUS. The proposed model is evaluated in three datasets by comparing with four CNN-based models and two vision transformer models via five-fold cross validation. It achieves state-of-the-art classification performance with the best model interpretability. In the mean-while, our proposed model outperforms two senior sonographers on the breast cancer diagnosis when only one BUS image is given. © 2022, CC BY.

关键词： Diseases

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