Journal ArticleDOI
Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge
Ching-Wei Wang,Cheng-Ta Huang,Meng-Che Hsieh,Chung-Hsing Li,Sheng-Wei Chang,Wei-Cheng Li,Rémy Vandaele,Raphaël Marée,Sébastien Jodogne,Pierre Geurts,Cheng Chen,Guoyan Zheng,Chengwen Chu,Hengameh Mirzaalian,Ghassan Hamarneh,Tomaz Vrtovec,Bulat Ibragimov +16 more
Reads0
Chats0
TLDR
Evaluation of the methods submitted to the Automatic Cephalometric X-Ray Landmark Detection Challenge provides insights into the performance of different landmark detection approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.Abstract:
Cephalometric analysis is an essential clinical and research tool in orthodontics for the orthodontic analysis and treatment planning. This paper presents the evaluation of the methods submitted to the Automatic Cephalometric X-Ray Landmark Detection Challenge, held at the IEEE International Symposium on Biomedical Imaging 2014 with an on-site competition. The challenge was set to explore and compare automatic landmark detection methods in application to cephalometric X-ray images. Methods were evaluated on a common database including cephalograms of 300 patients aged six to 60 years, collected from the Dental Department, Tri-Service General Hospital, Taiwan, and manually marked anatomical landmarks as the ground truth data, generated by two experienced medical doctors. Quantitative evaluation was performed to compare the results of a representative selection of current methods submitted to the challenge. Experimental results show that three methods are able to achieve detection rates greater than 80% using the 4 mm precision range, but only one method achieves a detection rate greater than 70% using the 2 mm precision range, which is the acceptable precision range in clinical practice. The study provides insights into the performance of different landmark detection approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.read more
Citations
More filters
Journal ArticleDOI
Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks
Bulat Ibragimov,Lei Xing +1 more
TL;DR: This work proposed the first deep learning‐based algorithm, for segmentation of OARs in HaN CT images, and compared its performance against state‐of‐the‐art automated segmentation algorithms, commercial software, and interobserver variability.
Journal ArticleDOI
A benchmark for comparison of dental radiography analysis algorithms
Ching-Wei Wang,Cheng-Ta Huang,Jia-Hong Lee,Chung-Hsing Li,Sheng-Wei Chang,Ming-Jhih Siao,Tat-Ming Lai,Bulat Ibragimov,Tomaz Vrtovec,Olaf Ronneberger,Philipp Fischer,Timothy F. Cootes,Claudia Lindner +12 more
TL;DR: Based on the quantitative evaluation results, it is believed automatic dental radiography analysis is still a challenging and unsolved problem and the datasets and the evaluation software are made available to the research community, further encouraging future developments in this field.
Journal ArticleDOI
Why rankings of biomedical image analysis competitions should be interpreted with care
Lena Maier-Hein,Matthias Eisenmann,Annika Reinke,Sinan Onogur,Marko Stankovic,Patrick Scholz,Tal Arbel,Hrvoje Bogunovic,Andrew P. Bradley,Aaron Carass,Carolin Feldmann,Alejandro F. Frangi,Peter M. Full,Bram van Ginneken,Allan Hanbury,Katrin Honauer,Michal Kozubek,Bennett A. Landman,Keno März,Oskar Maier,Klaus H. Maier-Hein,Bjoern H. Menze,Henning Müller,Peter Neher,Wiro J. Niessen,Nasir M. Rajpoot,Gregory C. Sharp,Korsuk Sirinukunwattana,Stefanie Speidel,Christian Stock,Danail Stoyanov,Abdel Aziz Taha,Fons van der Sommen,Ching-Wei Wang,Marc-André Weber,Guoyan Zheng,Pierre Jannin,Annette Kopp-Schneider +37 more
TL;DR: In this paper, the authors present a comprehensive analysis of biomedical image analysis challenges conducted up to now and demonstrate the importance of challenges and show that the lack of quality control has critical consequences.
Journal ArticleDOI
Fully automated quantitative cephalometry using convolutional neural networks.
TL;DR: It is demonstrated that CNNs, which merely input raw image patches, are promising for accurate quantitative cephalometry, and high anatomical type classification accuracy for test set is demonstrated.
Journal ArticleDOI
Collaborative analysis of multi-gigapixel imaging data using Cytomine
Raphaël Marée,Loïc Rollus,Benjamin Stevens,Renaud Hoyoux,Gilles Louppe,Rémy Vandaele,Jean-Michel Begon,Philipp Kainz,Pierre Geurts,Louis Wehenkel +9 more
TL;DR: Cytomine is developed to foster active and distributed collaboration of multidisciplinary teams for large-scale image-based studies and uses web development methodologies and machine learning in order to readily organize, explore, share and analyze multi-gigapixel imaging data over the internet.
References
More filters
Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Posted Content
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Journal ArticleDOI
Extremely randomized trees
TL;DR: A new tree-based ensemble method for supervised classification and regression problems that consists of randomizing strongly both attribute and cut-point choice while splitting a tree node and builds totally randomized trees whose structures are independent of the output values of the learning sample.
Book ChapterDOI
Muliscale Vessel Enhancement Filtering
TL;DR: The multiscale second order local structure of an image (Hessian) is examined with the purpose of developing a vessel enhancement filter and a vesselness measure is obtained.