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A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

Perovskite superlattices with efficient carrier dynamics

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Research Square

Research Square 2021年

作者： Xu, Sheng Lei, Yusheng Li, Yuheng Lu, Chengchangfeng Yan, Qizhang Gong, Huaxin Zhang, Song Zhou, Jiayun Zhang, Ruiqi Chen, Yimu Tsai, Hsinhan Gu, Yue Hu, Hongjie Lo, Yu-Hwa Nie, Wanyi Lee, Taeyoon Luo, Jian Yang, Kesong Jang, Kyung-In Department of Nanoengineering University of California San Diego La Jolla CA92093-0448 United States Department of Chemical Engineering Stanford University StanfordCA94305 United States Department of Electrical and Computer Engineering University of California San Diego La Jolla CA92093 United States Material Science and Engineering Program University of California San Diego La Jolla CA92093-0418 United States Los Alamos National Laboratory Los AlamosNM87545 United States School of Electrical and Electronic Engineering Yonsei University Seoul03722 Korea Republic of Department of Bio and Brain Engineering Korea Institute of Science and Technology Korea Republic of Department of Robotics Engineering Daegu Gyeongbuk Institute of Science and Technology Daegu42988 Korea Republic of Department of Bioengineering University of California San Diego La Jolla CA92093-0412 United States

Compared with their three-dimensional counterparts, low-dimensional metal halide perovskites with periodic inorganic/organic structures have shown promising stability and hysteresis-free electrical performance, which paves the way for next-generation optoelectronic devices. However, when integrated in devices, they have relatively limited efficiencies because devices usually require carrier transport through the film thickness direction. In conventionally grown single crystals, the carrier transport in the thickness direction is hindered by the insulating organic spacers. In addition, the strong quantum confinement from the organic spacers limits the generation and transport of free carriers. The carrier dynamics is further compromised by the presence of grain boundaries in polycrystals. Here, we report a low-dimensional metal halide perovskite superlattice with efficient carrier transport in three dimensions by epitaxial growth. Epitaxy on a slightly lattice-mismatched substrate compresses the organic spacers in the superlattice, which weakens the quantum confinement and further improves carrier dynamics. The performance of a low-dimensional perovskite superlattice solar cell has been certified under the quasi-steady state for the first time. Moreover, the device shows an unusually high open-circuit voltage, due to a unique intra-band exciton relaxation mechanism. © 2021, CC BY.

关键词： Grain boundaries

Large language models for diabetes training:a prospective study

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Science Bulletin 2025年第6期70卷 934-942页

作者： Haoxuan Li Zehua Jiang Zhouyu Guan Yuqian Bao Yuexing Liu Tingting Hu Jiajia Li Ruhan Liu Liang Wu Di Cheng Hongwei Ji Yong Wang Ya-Xing Wang Carol Y.Cheung Yingfeng Zheng Jihong Wang Zhen Li Weibing Wu Cynthia Ciwei Lim Yong Mong Bee Hong Chang Tan Elif I.Ekinci David C.Klonoff Justin B.Echouffo-Tcheugui Nestoras Mathioudakis Leonor Corsino Rafael Simó Charumathi Sabanayagam Gavin Siew Wei Tan Ching-Yu Cheng Tien Yin Wong Huating Li Chun Cai Lijuan Mao Lee-Ling Lim Yih-Chung Tham Bin Sheng Weiping Jia Shanghai University of Sport Shanghai 200438China School of Clinical Medicine Beijing Tsinghua Changgung HospitalTsinghua MedicineTsinghua UniversityBeijing 100084China Beijing Visual Science and Translational Eye Research Institute(BERI) Tsinghua MedicineTsinghua UniversityBeijing 100084China Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and EngineeringSchool of ElectronicInformationand Electrical EngineeringShanghai Jiao Tong UniversityDepartment of Endocrinology and MetabolismShanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for DiabetesShanghai 200240China MoE Key Lab of Artificial Intelligence Artificial Intelligence InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Cardiology the Affiliated Hospital of Qingdao UniversityQingdao 266011China CEMS NCMISHCMSMADISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematics University of Chinese Academy of SciencesChinese Academy of SciencesBeijing 100049China Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital Medical UniversityBeijing Ophthalmology and Visual Sciences Key LaboratoryBeijing 100730China k.Department of Ophthalmology and Visual Sciences The Chinese University of Hong KongHong Kong 999077China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular DiseasesGuangzhou 510060China Department of Renal Medicine Singapore General HospitalSingHealth-Duke Academic Medical CentreSingapore169

Diabetes poses a considerable global health challenge,with varying levels of diabetes knowledge among healthcare professionals,highlighting the importance of diabetes *** Language Models(LLMs)provide new insights into diabetes training,but their performance in diabetes-related queries remains uncertain,especially outside the English language like *** first evaluated the performance of ten LLMs:ChatGPT-3.5,ChatGPT-4.0,Google Bard,LlaMA-7B,LlaMA2-7B,Baidu ERNIE Bot,Ali Tongyi Qianwen,MedGPT,HuatuoGPT,and Chinese LlaMA2-7B on diabetes-related queries,based on the Chinese National Certificate Examination for Primary Diabetes Care in China(NCE-CPDC)and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United ***,we assessed the training of primary care physicians(PCPs)without and with the assistance of ChatGPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical *** found that ChatGPT-4.0 outperformed other LLMs in the English examination,achieving a passing accuracy of 62.50%,which was significantly higher than that of Google Bard,LlaMA-7B,and *** the NCE-CPFC examination,ChatGPT-4.0,Ali Tongyi Qianwen,Baidu ERNIE Bot,Google Bard,MedGPT,and ChatGPT-3.5 successfully passed,whereas LlaMA2-7B,HuatuoGPT,Chinese LLaMA2-7B,and LlaMA-7B ***-4.0(84.82%)surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination(improving by 1%–6.13%).In summary,LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language,and hold great potential to assist future diabetes training for physicians globally.

关键词： Diabetes Diabetestraining Largelanguagemodels Primarydiabetescare Prospectivestudy

metasnf: Meta Clustering with Similarity Network Fusion in R

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arXiv 2024年

作者： Velayudhan, Prashanth S. Xu, Xiaoqiao Kallurkar, Prajkta Balbon, Ana Patricia Secara, Maria T. Taback, Adam Sabac, Denise Chan, Nicholas Ma, Shihao Wang, Bo Felsky, Daniel Ameis, Stephanie H. Cox, Brian Hawco, Colin Erdman, Lauren Wheeler, Anne L. The Hospital for Sick Children TorontoON Canada Department of Physiology The University of Toronto TorontoON Canada Centre for Computational Medicine Hospital for Sick Children TorontoON Canada Department of Obstetrics and Gynecology University of Toronto TorontoON Canada Campbell Family Mental Health Research Institute Centre for Addiction and Mental Health TorontoON Canada Institute of Medical Science University of Toronto TorontoON Canada The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoON Canada Krembil Centre for Neuroinformatics Centre for Addiction and Mental Health TorontoON Canada Division of Rheumatology Hospital for Sick Children TorontoON Canada Vector Institute TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Medical Biophysics University of Toronto TorontoON Canada Division of Biostatistics Dalla Lana School of Public Health University of Toronto TorontoON Canada Department of Psychiatry University of Toronto TorontoON Canada Department of Anthropology University of Toronto TorontoON Canada Department of Psychiatry Temerty Faculty of Medicine University of Toronto TorontoON Canada Department of Psychiatry The Hospital for Sick Children TorontoON Canada Division of Gastroenterology Department of Pediatrics Cincinnati Children’s Hospital Medical Center University of Cincinnati College of Medicine CincinnatiOH United States James M. Anderson Center for Health Systems Excellence Cincinnati Children’s Hospital Medical Center CincinnatiOH United States Department of Pediatrics University of Cincinnati College of Medicine CincinnatiOH United States

metasnf is an R package that enables users to apply meta clustering, a method for efficiently searching a broad space of cluster solutions by clustering the solutions themselves, to clustering workflows based on similarity network fusion (SNF). SNF is a multi-modal data integration algorithm commonly used for biomedical subtype discovery. The package also contains functions to assist with cluster visualization, characterization, and validation. This package can help researchers identify SNF-derived cluster solutions that are guided by context-specific utility over context-agnostic measures of quality. Copyright © 2024, The Authors. All rights reserved.

关键词： Cluster analysis

NASDUCK SERF: New constraints on axion-like dark matter from a SERF comagnetometer

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arXiv 2022年

作者： Bloch, Itay M. Shaham, Roy Hochberg, Yonit Kuflik, Eric Volansky, Tomer Katz, Or Berkeley Center for Theoretical Physics University of California BerkeleyCA94720 United States Theory Group Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Rafael Ltd. Haifa31021 Israel Department of Physics of Complex Systems Weizmann Institute of Science Rehovot76100 Israel Racah Institute of Physics Hebrew University of Jerusalem Jerusalem91904 Israel Department of Physics Tel Aviv University Tel Aviv Israel Duke Quantum Center Duke University DurhamNC27701 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States

Ultralight axion-like particles are well-motivated relics that might compose the cosmological dark matter and source anomalous time-dependent magnetic fields. We report on new terrestrial bounds on the coupling of axion-like particles to neutrons and protons. The detector uses nuclei of noble-gas and alkali-metal atoms and operates in the Spin-Exchange Relaxation-Free (SERF) regime, achieving high sensitivity to axion-like dark matter fields. Conducting a month-long search, we cover the mass range of 1.4 × 10−12 eV/c2 to 2 × 10−10 eV/c2 and provide world-leading limits which supersede robust astrophysical bounds, and improve upon previous terrestrial constraints by up to two orders of magnitudes for many masses within this range. These are the first reliable terrestrial bounds reported on the coupling of protons with axion-like dark matter, covering a new and unexplored terrain in its parameter space. © 2022, CC BY.

关键词： Dark Matter

Unconventional methods in voynich manuscript analysis 25

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Unconventional methods in voynich manuscript analysis

25th International Conference on Soft Computing, MENDEL 2019

作者： Zelinka, Ivan Zmeskal, Oldrich Windsor, Leah Cai, Zhiqiang Department of Computer Science Faculty of Electrical Engineering and Computer Science VŠB-TU Czech Republic Institute of Physical and Applied Chemistry Faculty of Chemistry Brno University of Technology Czech Republic Institute for Intelligent Systems University of Memphis United States

This paper discusses the possible use of unconventional algorithms on analysis and categorization of the unknown text, including documents written in unknown languages. Scholars have identified about ten famous manuscripts, mostly encrypted or written in the unknown language. The most famous is the Voynich manuscript, an illustrated codex hand-written in an unknown language or writing system. Using carbon-dating methods, the researchers determined its age as the early 15th century (between 1404-1438). Many professional and amateur cryptographers have studied the Voynich manuscript, and none has deciphered its meaning as yet, including American and British code-breakers and cryptologists. While there exist many hypotheses about the meaning and structure of the document, they have yet to be confirmed empirically. In this paper, we discuss two different kinds of unconventional approaches for how to handle manuscripts with unidentified writing systems and determine whether its properties are characterized by a natural language, or is only historical fake text. © 2019, Brno University of Technology. All rights reserved.

关键词： Complex networks

Accurate Virus Identification with Interpretable Raman Signatures by Machine Learning

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arXiv 2022年

作者： Ye, Jiarong Yeh, Yin-Ting Xue, Yuan Wang, Ziyang Zhang, Na Liu, He Zhang, Kunyan Ricker, Ryeanne Yu, Zhuohang Roder, Allison Lopez, Nestor Perea Organtini, Lindsey Greene, Wallace Hafenstein, Susan Lu, Huaguang Ghedin, Elodie Terrones, Mauricio Huang, Shengxi Huang, Sharon Xiaolei College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Department of Physics The Pennsylvania State University University ParkPA United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States Department of Electrical and Computer Engineering The Pennsylvania State University University ParkPA United States Department of Biomedical Engineering George Washington University WashingtonDC United States Systems Genomics Section Laboratory of Parasitic Diseases National Institute of Allergy and Infectious Diseases National Institutes of Health BethesdaMD United States Department of Biochemistry and Molecular Biology The Pennsylvania State University University ParkPA United States Department of Pathology and Laboratory Medicine Division of Clinical Pathology The Pennsylvania State University College of Medicine HersheyPA United States Department of Veterinary and Biomedical Sciences The Pennsylvania State University University ParkPA United States 413G Eric. J. Barron Innovation Hub 123 S. Burrowes Street State CollegePA16801 United States

Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device coupled with label-free Raman Spectroscopy holds the promise of fast detection by rapidly obtaining the Raman signature of a virus followed by a machine learning approach applied to recognize the virus based on its Raman spectrum, which is used as a fingerprint. We present such a machine learning approach for analyzing Raman spectra of human and avian viruses. A Convolutional Neural Network (CNN) classifier specifically designed for spectral data achieves very high accuracy for a variety of virus type or subtype identification tasks. In particular, it achieves 99% accuracy for classifying influenza virus type A vs. type B, 96% accuracy for classifying four subtypes of influenza A, 95% accuracy for differentiating enveloped and nonenveloped viruses, and 99% accuracy for differentiating avian coronavirus (infectious bronchitis virus, IBV) from other avian viruses. Furthermore, interpretation of neural net responses in the trained CNN model using a full-gradient algorithm highlights Raman spectral ranges that are most important to virus identification. By correlating ML-selected salient Raman ranges with the signature ranges of known biomolecules and chemical functional groups-for example, amide, amino acid, carboxylic acid-we verify that our ML model effectively recognizes the Raman signatures of proteins, lipids and other vital functional groups present in different viruses and uses a weighted combination of these signatures to identify viruses. © 2022, CC BY.

关键词： Raman spectroscopy

Bright-field holography:cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram

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Light(Science & Applications) 2019年第1期8卷 936-942页

作者： Yichen Wu Yilin Luo Gunvant Chaudhari Yair Rivenson Ayfer Calis Kevin de Haan Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California Nano Systems Institute(CNSI) University of CaliforniaLos AngelesCA 90095USA David Geffen School of Medicine University of CaliforniaLos AngelesCA 90095USA

Digital holographic microscopy enables the 3D reconstruction of volumetric samples from a single-snapshot ***,unlike a conventional bright-field microscopy image,the quality of holographic reconstructions is compromised by interference fringes as a result of twin images and out-of-plane ***,we demonstrate that cross-modality deep learning using a generative adversarial network(GAN)can endow holographic images of a sample volume with bright-field microscopy contrast,combining the volumetric imaging capability of holography with the speckle-and artifact-free image contrast of incoherent bright-field *** illustrate the performance of this“bright-field holography”method through the snapshot imaging of bioaerosols distributed in 3D,matching the artifact-free image contrast and axial sectioning performance of a high-NA bright-field *** data-driven deep-learning-based imaging method bridges the contrast gap between coherent and incoherent imaging,and enables the snapshot 3D imaging of objects with bright-field contrast from a single hologram,benefiting from the wave-propagation framework of holography.

关键词： enable holographic bridges

SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma

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arXiv 2023年

作者： Luo, Xiangde Fu, Jia Zhong, Yunxin Liu, Shuolin Han, Bing Astaraki, Mehdi Bendazzoli, Simone Toma-Dasu, Iuliana Ye, Yiwen Chen, Ziyang Xia, Yong Su, Yanzhou Ye, Jin He, Junjun Xing, Zhaohu Wang, Hongqiu Zhu, Lei Yang, Kaixiang Fang, Xin Wang, Zhiwei Lee, Chan Woong Park, Sang Joon Chun, Jaehee Ulrich, Constantin Maier-Hein, Klaus H. Ndipenoch, Nchongmaje Miron, Alina Li, Yongmin Zhang, Yimeng Chen, Yu Bai, Lu Huang, Jinlong An, Chengyang Wang, Lisheng Huang, Kaiwen Gu, Yunqi Zhou, Tao Zhou, Mu Zhang, Shichuan Liao, Wenjun Wang, Guotai Zhang, Shaoting School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Department of Radiation Oncology Sichuan Cancer Hospital & Institute Chengdu China Shanghai AI Lab Shanghai China Co. Ltd. Beijing China Department of Medical Radiation Physics Stockholm University Solna Sweden Department of Biomedical Engineering and Health Systems KTH Huddinge Sweden Northwestern Polytechnical University Xi’an China Guangzhou China Wuhan National Laboratory for Optoelectronics with MoE Key Laboratory for Biomedical Photonics China Yonsei University College of Medicine Seoul Korea Republic of Oncosoft Inc. Seoul Korea Republic of Heidelberg Germany Department of Computer Science Brunel University London Uxbridge United Kingdom MedMind Technology Co. Ltd. Beijing China Shanghai Jiao Tong University Shanghai China Nanjing University of Science and Technology Nanjing China

Radiation therapy is a primary and effective NasoPharyngeal Carcinoma (NPC) treatment strategy. The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis. Previously, the delineation of GTVs and OARs was performed by experienced radiation oncologists. Recently, deep learning has achieved promising results in many medical image segmentation tasks. However, for NPC OARs and GTVs segmentation, few public datasets are available for model development and evaluation. To alleviate this problem, the SegRap2023 challenge was organized in conjunction with MICCAI2023 and presented a large-scale benchmark for OAR and GTV segmentation with 400 Computed Tomography (CT) scans from 200 NPC patients, each with a pair of pre-aligned non-contrast and contrast-enhanced CT scans. The challenge’s goal was to segment 45 OARs and 2 GTVs from the paired CT scans. In this paper, we detail the challenge and analyze the solutions of all participants. The average Dice similarity coefficient scores for all submissions ranged from 76.68% to 86.70%, and 70.42% to 73.44% for OARs and GTVs, respectively. We conclude that the segmentation of large-size OARs is well-addressed, and more efforts are needed for GTVs and small-size or thin-structure OARs. The benchmark will remain publicly available here: https://***. © 2023, CC BY-NC-ND.

关键词： Radiotherapy