P
Pierre-Henri Conze
Researcher at French Institute of Health and Medical Research
Publications - 67
Citations - 1195
Pierre-Henri Conze is an academic researcher from French Institute of Health and Medical Research. The author has contributed to research in topics: Segmentation & Motion estimation. The author has an hindex of 13, co-authored 67 publications receiving 611 citations. Previous affiliations of Pierre-Henri Conze include Institut national des sciences appliquées de Rennes & Institut Mines-Télécom.
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Journal ArticleDOI
CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation.
A. Emre Kavur,N. Sinem Gezer,Mustafa Baris,Sinem Aslan,Pierre-Henri Conze,Vladimir Groza,Duc Duy Pham,Soumick Chatterjee,Philipp Ernst,Savas Ozkan,Bora Baydar,Dmitrii Lachinov,Shuo Han,Josef Pauli,Fabian Isensee,Matthias Perkonigg,Rachana Sathish,Ronnie Rajan,Debdoot Sheet,Gurbandurdy Dovletov,Oliver Speck,Andreas Nürnberger,Klaus H. Maier-Hein,Gozde Bozdagi Akar,Gozde Unal,Oğuz Dicle,M. Alper Selver +26 more
TL;DR: The analysis shows that the performance of DL models for single modality (CT / MR) can show reliable volumetric analysis performance, but the best MSSD performance remains limited, and multi-tasking DL models designed to segment all organs are observed to perform worse compared to organ-specific ones.
Journal ArticleDOI
CATARACTS: Challenge on automatic tool annotation for cataRACT surgery
Hassan Al Hajj,Mathieu Lamard,Pierre-Henri Conze,Soumali Roychowdhury,Xiaowei Hu,Gabija Maršalkaitė,Odysseas Zisimopoulos,Muneer Ahmad Dedmari,Fenqiang Zhao,Jonas Prellberg,Manish Sahu,Adrian Galdran,Teresa Araújo,Duc My Vo,Chandan Panda,Navdeep Dahiya,Satoshi Kondo,Zhengbing Bian,Arash Vahdat,Jonas Bialopetravičius,Evangello Flouty,Chenhui Qiu,Sabrina Dill,Anirban Mukhopadhyay,Pedro Alves Costa,Guilherme Aresta,Senthil Ramamurthy,Sang-Woong Lee,Aurélio Campilho,Stefan Zachow,Shunren Xia,Sailesh Conjeti,Danail Stoyanov,Jogundas Armaitis,Pheng-Ann Heng,William G. Macready,Béatrice Cochener,Gwenole Quellec +37 more
TL;DR: Evaluating tool annotation algorithms based on deep learning for cataract surgery finds that the quality of their annotations are compared to that of human interpretations, and it is expected that they will guide the design of efficient surgery monitoring tools in the near future.
Proceedings ArticleDOI
Objective view synthesis quality assessment
TL;DR: This method is dedicated to artifacts detection in synthesized view-points and aims to handle areas where disparity estimation may fail: thin objects, object borders, transparency, variations of illumination or color differences between left and right views, periodic objects...
Journal ArticleDOI
Monitoring tool usage in surgery videos using boosted convolutional and recurrent neural networks.
TL;DR: The automatic monitoring of tool usage during a surgery is investigated, with potential applications in report generation, surgical training and real‐time decision support, and a novel boosting strategy is proposed to achieve this goal.
Journal ArticleDOI
Scale-adaptive supervoxel-based random forests for liver tumor segmentation in dynamic contrast-enhanced CT scans
Pierre-Henri Conze,Vincent Noblet,François Rousseau,Fabrice Heitz,Vito De Blasi,Riccardo Memeo,Patrick Pessaux +6 more
TL;DR: Assessment on clinical data confirms the benefits of multi-phase information embedded in a multi-scale supervoxel representation for HCC tumor segmentation and both contributions reach further steps toward more accurate multi-label tissue classification.