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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

Sentiment recognition in egocentric photostreams

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arXiv 2017年

作者： Talavera, Estefania Strisciuglio, Nicola Petkov, Nicolai Radeva, Petia Intelligent Systems Group University of Groningen Netherlands Department of Mathematics and Computer Science University of Barcelona Spain Computer Vision Center Barcelona Spain

Lifelogging is a process of collecting rich source of information about daily life of people. In this paper, we introduce the problem of sentiment analysis in egocentric events focusing on the moments that compose the images recalling positive, neutral or negative feelings to the observer. We propose a method for the classification of the sentiments in egocentric pictures based on global and semantic image features extracted by Convolutional Neural Networks. We carried out experiments on an egocentric dataset, which we organized in 3 classes on the basis of the sentiment that is recalled to the user (positive, negative or neutral). Copyright © 2017, The Authors. All rights reserved.

关键词： Sentiment analysis

Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the heichole benchmark

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arXiv 2021年

作者： Wagner, Martin Müller-Stich, Beat-Peter Kisilenko, Anna Tran, Duc Heger, Patrick Mündermann, Lars Lubotsky, David M. Müller, Benjamin Davitashvili, Tornike Capek, Manuela Reinke, Annika Yu, Tong Vardazaryan, Armine Innocent Nwoye, Chinedu Padoy, Nicolas Liu, Xinyang Lee, Eung-Joo Disch, Constantin Meine, Hans Xia, Tong Jia, Fucang Kondo, Satoshi Reiter, Wolfgang Jin, Yueming Long, Yonghao Jiang, Meirui Dou, Qi Heng, Pheng Ann Twick, Isabell Kirtac, Kadir Hosgor, Enes Bolmgren, Jon Lindström Stenzel, Michael von Siemens, Björn Kenngott, Hannes G. Nickel, Felix von Frankenberg, Moritz Mathis-Ullrich, Franziska Maier-Hein, Lena Speidel, Stefanie Bodenstedt, Sebastian Department for General Visceral and Transplantation Surgery Heidelberg University Hospital Im Neuenheimer Feld 420 Heidelberg69120 Germany Heidelberg Im Neuenheimer Feld 460 Heidelberg69120 Germany Data Assisted Solutions Corporate Research & Technology KARL STORZ SE & Co. KG Dr. Karl-Storz-Str. 34 Tuttlingen78332 Germany Im Neuenheimer Feld 223 Heidelberg69120 Germany Im Neuenheimer Feld 223 Heidelberg69120 Germany Faculty of Mathematics and Computer Science Heidelberg University Im Neuenheimer Feld 205 Heidelberg69120 Germany ICube University of Strasbourg CNRS France 300 bd Sébastien Brant - CS 10413 Illkirch CedexF-67412 France IHU Strasbourg France 1 Place de l'hôpital Strasbourg67000 France Sheikh Zayed Institute for Pediatric Surgical Innovation Children's National Hospital 111 Michigan Ave NW WashingtonDC20010 United States University of Maryland College Park 2405 A V Williams Building College ParkMD20742 United States Fraunhofer Institute for Digital Medicine MEVIS Max-von-Laue-Str. 2 Bremen28359 Germany University of Bremen FB3 Medical Image Computing Group ℅ Fraunhofer MEVIS Am Fallturm 1 Bremen28359 Germany Lab for Medical Imaging and Digital Surgery Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Konika Minolta Inc. JP TOWER 2-7-2 Marunouchi Chiyoda-ku Tokyo100-7015 Japan Wintegral GmbH Ehrenbreitsteiner Str. 36 München80993 Germany Department of Computer Science and Engineering Ho Sin-Hang Engineering Building The Chinese University of Hong Kong Sha Tin NT Hong Kong Caresyntax GmbH Komturstr. 18A Berlin12099 Germany Department of Surgery Salem Hospital of the Evangelische Stadtmission Heidelberg Zeppelinstrasse 11-33 Heidelberg69121 Germany Health Robotics and Automation Laboratory Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Geb. 40.28 KIT Campus Süd Engler-Bunte-Ring 8 Karlsruhe76131 Germany Medical Faculty Heidelberg Uni

PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multi-center setting including more difficult recognition tasks such as surgical action and surgical skill. METHODS: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 hours was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment. RESULTS: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n=9 teams), for instrument presence detection between 38.5% and 63.8% (n=8 teams), but for action recognition only between 21.8% and 23.3% (n=5 teams). The average absolute error for skill assessment was 0.78 (n=1 team). CONCLUSION: Surgical workflow and skill analysis are promising technologies to support the surgical team, but are not solved yet, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost impo

关键词： Learning algorithms

Underwater multi-robot convoying using visual tracking by detection

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arXiv 2017年

作者： Shkurti, Florian Chang, Wei-Di Henderson, Peter Islam, Md Jahidul Higuera, Juan Camilo Gamboa Li, Jimmy Manderson, Travis Xu, Anqi Dudek, Gregory Sattar, Junaed Centre for Intelligent Machines School of Computer Science McGill University Interactive Robotics and Vision Laboratory Department of Computer Science and Engineering University of Minnesota- Twin Cities Element AI

We present a robust multi-robot convoying approach that relies on visual detection of the leading agent, thus enabling target following in unstructured 3-D environments. Our method is based on the idea of tracking-by-detection, which interleaves efficient model-based object detection with temporal filtering of image-based bounding box estimation. This approach has the important advantage of mitigating tracking drift (i.e. drifting away from the target object), which is a common symptom of model-free trackers and is detrimental to sustained convoying in practice. To illustrate our solution, we collected extensive footage of an underwater robot in ocean settings, and hand-annotated its location in each frame. Based on this dataset, we present an empirical comparison of multiple tracker variants, including the use of several convolutional neural networks, both with and without recurrent connections, as well as frequency-based model-free trackers. We also demonstrate the practicality of this tracking-by-detection strategy in real-world scenarios by successfully controlling a legged underwater robot in five degrees of freedom to follow another robot’s independent motion. Copyright © 2017, The Authors. All rights reserved.

关键词： Multipurpose robots

Reports on the 2017 AAAI spring symposium series

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Reports on the 2017 AAAI spring symposium series

作者： Bohg, Jeannette Boix, Xavier Chang, Nancy Chu, Vivian Churchill, Elizabeth F. Fang, Fei Feldman, Jerome González, Avelino J. Kido, Takashi Lawless, William F. Montaña, José L. Ontañón, Santiago Sinapov, Jivko Sofge, Don Steels, Luc Steenson, Molly Wright Takadama, Keiki Yadav, Amulya Max Planck Institute for Intelligent Systems United States Massachusetts Institute of Technology United States Institute of Robotics and Intelligent Machines Georgia Institute of Technology United States Google United States Harvard University United States University of California Berkeley United States Department of Computer Science University of Central Florida United States Preferred Networks in Japan Japan Paine College in Augusta GA United States Department of Mathematics Statistics and Computer Science University of Cantabria Spain Department of Computer Science Drexel University United States Department of Computer Science University of Texas Austin United States Naval Research Laboratory United States Barcelona Spain Carnegie Mellon University School of Design United States University of Electro-Communications Japan Computer Science Department University of Southern California United States

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's department of computer science, presented the 2017 Spring Symposium Series, held Monday through Wednesday, March 27-29, 2017, on the campus of Stanford University. The eight symposia held were Artificial Intelligence for the Social Good (SS-17-01);Computational Construction Grammar and Natural Language Understanding (SS-17-02);Computational Context: Why It's Important, What It Means, and Can It Be Computed? (SS-17-03);Designing the User Experience of Ma chine-Learning Systems (SS-17-04);Interactive Multisensory Object Perception for Embodied Agents (SS-17-05);Learning from Ob servation of Humans (SS-17-06);science of Intelligence: Com putational Principles of Natural and Artificial Intelligence (SS-17-07);and Well-Being AI: From Machine Learning to Subjectivity-Oriented Computing (SS-17-08). This report, compiled from organizers of the symposia, summarizes the research that took place. © Copyright 2017, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Learning systems

Heidelberg colorectal data set for surgical data science in the sensor operating room

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arXiv 2020年

作者： Maier-Hein, Lena Wagner, Martin Ross, Tobias Reinke, Annika Bodenstedt, Sebastian Full, Peter M. Hempe, Hellena Mindroc-Filimon, Diana Scholz, Patrick Tran, Thuy Nuong Bruno, Pierangela Kisilenko, Anna Müller, Benjamin Davitashvili, Tornike Capek, Manuela Tizabi, Minu Eisenmann, Matthias Adler, Tim J. Gröhl, Janek Schellenberg, Melanie Seidlitz, Silvia Lai, T.Y. Emmy Pekdemir, Bünyamin Roethlingshoefer, Veith Both, Fabian Bittel, Sebastian Mengler, Marc Mündermann, Lars Apitz, Martin Kopp-Schneider, Annette Speidel, Stefanie Kenngott, Hannes G. Müller-Stich, Beat P. Im Neuenheimer Feld 223 Heidelberg69120 Germany Department for General Visceral and Transplantation Surgery Heidelberg University Hospital Im Neuenheimer Feld 110 Heidelberg69120 Germany University of Heidelberg Seminarstraße 2 Heidelberg69117 Germany Division of Translational Surgical Oncology National Center for Tumor Diseases Partner Site Dresden Fetscherstraße 74 Dresden01307 Germany Im Neuenheimer Feld 223 Heidelberg69120 Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Im Neuenheimer Feld 223 Heidelberg69120 Germany Department of Mathematics and Computer Science University of Calabria Via Pietro Bucci Arcavacata Rende CS87036 Italy understandAI GmbH An der RaumFabrik 34 Karlsruhe76227 Germany International Max Planck Research School for Intelligent Systems Tübingen University of Tübingen Geschwister-Scholl-Platz Tübingen72074 Germany BMW Group Heidemannstraße 164 Munich80939 Germany Corporate Research and Technology Data-Assisted Solutions KARL STORZ SE & Co. KG Dr.-Karl-Storz-Strasse 34 Tuttlingen78532 Germany Im Neuenheimer Feld 581 Heidelberg69120 Germany TU Dresden Dresden01307 Germany

Image-based tracking of medical instruments is an integral part of surgical data science applications. Previous research has addressed the tasks of detecting, segmenting and tracking medical instruments based on laparoscopic video data. However, the proposed methods still tend to fail when applied to challenging images and do not generalize well to data they have not been trained on. This paper introduces the Heidelberg Colorectal (HeiCo) data set - the first publicly available data set enabling comprehensive benchmarking of medical instrument detection and segmentation algorithms with a specific emphasis on method robustness and generalization capabilities. Our data set comprises 30 laparoscopic videos and corresponding sensor data from medical devices in the operating room for three different types of laparoscopic surgery. Annotations include surgical phase labels for all video frames as well as information on instrument presence and corresponding instance-wise segmentation masks for surgical instruments (if any) in more than 10,000 individual frames. The data has successfully been used to organize international competitions within the Endoscopic vision Challenges 2017 and 2019. Copyright © 2020, The Authors. All rights reserved.

关键词： Laparoscopy

iCLAP: Shape recognition by combining proprioception and touch sensing

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arXiv 2018年

作者： Luo, Shan Mou, Wenxuan Althoefer, Kaspar Liu, Hongbin Center for Robotics Research Department of Informatics King's College London LondonWC2R 2LS United Kingdom Department of Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom Multimedia and Vision Research Group School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom School of Engineering and Materials Science Queen Mary University of London LondonE1 4NS United Kingdom

For humans, both the proprioception and touch sensing are highly utilized when performing haptic perception. However, most approaches in robotics use only either proprioceptive data or touch data in haptic object recognition. In this paper, we present a novel method named Iterative Closest Labeled Point (iCLAP) to link the kinesthetic cues and tactile patterns fundamentally and also introduce its extensions to recognize object shapes. In the training phase, the iCLAP first clusters the features of tactile readings into a codebook and assigns these features with distinct label numbers. A 4D point cloud of the object is then formed by taking the label numbers of the tactile features as an additional dimension to the 3D sensor positions;hence, the two sensing modalities are merged to achieve a synthesized perception of the touched object. Furthermore, we developed and validated hybrid fusion strategies, product based and weighted sum based, to combine decisions obtained from iCLAP and single sensing modalities. Extensive experimentation demonstrates a dramatic improvement of object recognition using the proposed methods and it shows great potential to enhance robot perception ability. Copyright © 2018, The Authors. All rights reserved.

关键词： Object recognition

The Liver Tumor Segmentation Benchmark (LiTS)

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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

Scalable co-optimization of morphology and control in embodied machines

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arXiv 2017年

作者： Cheney, Nick Bongard, Josh SunSpiral, Vytas Lipson, Hod Department of Computational Biology and Biological Statistics Cornell University IthacaNY United States Department of Computer Science University of Wyoming LaramieWY United States Department of Computer Science University of Vermont BurlingtonVT United States Intelligent Robotics Group Intelligent Systems Division NASA Ames/SGT Inc. Mountain ViewCA Department of Mechanical Engineering Columbia University New YorkNY United States

Evolution sculpts both the body plans and nervous systems of agents together over time. In contrast, in AI and robotics, a robot's body plan is usually designed by hand, and control policies are then optimized for that fixed design. The task of simultaneously co-optimizing the morphology and controller of an embodied robot has remained a challenge. In psychology, the theory of embodied cognition posits that behavior arises from a close coupling between body plan and sensorimotor control, which suggests why co-optimizing these two subsystems is so difficult: Most evolutionary changes to morphology tend to adversely impact sensorimotor control, leading to an overall decrease in behavioral performance. Here, we further examine this hypothesis and demonstrate a technique for "morphological innovation protection", which temporarily reduces selection pressure on recently morphologically-changed individuals, thus enabling evolution some time to "readapt" to the new morphology with subsequent control policy mutations. We show the potential for this method to avoid local optima and converge to similar highly fit morphologies across widely varying initial conditions, while sustaining fitness improvements further into optimization. While this technique is admittedly only the first of many steps that must be taken to achieve scalable optimization of embodied machines, we hope that theoretical insight into the cause of evolutionary stagnation in current methods will help to enable the automation of robot design and behavioral training - while simultaneously providing a testbed to investigate the theory of embodied cognition. Copyright © 2017, The Authors. All rights reserved.

关键词： Morphology