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2. Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma

Author

Julien Calderaro, Narmin Ghaffari Laleh, Qinghe Zeng, Pascale Maille, Loetitia Favre, Anaïs Pujals, Christophe Klein, Céline Bazille, Lara R. Heij, Arnaud Uguen, Tom Luedde, Luca Di Tommaso, Aurélie Beaufrère, Augustin Chatain, Delphine Gastineau, Cong Trung Nguyen, Hiep Nguyen-Canh, Khuyen Nguyen Thi, Viviane Gnemmi, Rondell P. Graham, Frédéric Charlotte, Dominique Wendum, Mukul Vij, Daniela S. Allende, Federico Aucejo, Alba Diaz, Benjamin Rivière, Astrid Herrero, Katja Evert, Diego Francesco Calvisi, Jérémy Augustin, Wei Qiang Leow, Howard Ho Wai Leung, Emmanuel Boleslawski, Mohamed Rela, Arnaud François, Anthony Wing-Hung Cha, Alejandro Forner, Maria Reig, Manon Allaire, Olivier Scatton, Denis Chatelain, Camille Boulagnon-Rombi, Nathalie Sturm, Benjamin Menahem, Eric Frouin, David Tougeron, Christophe Tournigand, Emmanuelle Kempf, Haeryoung Kim, Massih Ningarhari, Sophie Michalak-Provost, Purva Gopal, Raffaele Brustia, Eric Vibert, Kornelius Schulze, Darius F. Rüther, Sören A. Weidemann, Rami Rhaiem, Jean-Michel Pawlotsky, Xuchen Zhang, Alain Luciani, Sébastien Mulé, Alexis Laurent, Giuliana Amaddeo, Hélène Regnault, Eleonora De Martin, Christine Sempoux, Pooja Navale, Maria Westerhoff, Regina Cheuk-Lam Lo, Jan Bednarsch, Annette Gouw, Catherine Guettier, Marie Lequoy, Kenichi Harada, Pimsiri Sripongpun, Poowadon Wetwittayaklang, Nicolas Loménie, Jarukit Tantipisit, Apichat Kaewdech, Jeanne Shen, Valérie Paradis, Stefano Caruso, and Jakob Nikolas Kather

Subjects
Science
Abstract

Abstract Primary liver cancer arises either from hepatocytic or biliary lineage cells, giving rise to hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA). Combined hepatocellular- cholangiocarcinomas (cHCC-CCA) exhibit equivocal or mixed features of both, causing diagnostic uncertainty and difficulty in determining proper management. Here, we perform a comprehensive deep learning-based phenotyping of multiple cohorts of patients. We show that deep learning can reproduce the diagnosis of HCC vs. CCA with a high performance. We analyze a series of 405 cHCC-CCA patients and demonstrate that the model can reclassify the tumors as HCC or ICCA, and that the predictions are consistent with clinical outcomes, genetic alterations and in situ spatial gene expression profiling. This type of approach could improve treatment decisions and ultimately clinical outcome for patients with rare and biphenotypic cancers such as cHCC-CCA.

Published
2023
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11. Can AI predict epithelial lesion categories via automated analysis of cervical biopsies: The TissueNet challenge?

Author

Nicolas Loménie, Capucine Bertrand, Rutger H.J. Fick, Saima Ben Hadj, Brice Tayart, Cyprien Tilmant, Isabelle Farré, Soufiane Z. Azdad, Samy Dahmani, Gilles Dequen, Ming Feng, Kele Xu, Zimu Li, Sophie Prevot, Christine Bergeron, Guillaume Bataillon, Mojgan Devouassoux-Shisheboran, Claire Glaser, Agathe Delaune, Séverine Valmary-Degano, and Philippe Bertheau

Subjects
Artificial intelligence, Data challenge, Whole slide images, Uterine cervix, Computer applications to medicine. Medical informatics, R858-859.7, Pathology, RB1-214
Abstract

The French Society of Pathology (SFP) organized its first data challenge in 2020 with the help of the Health Data Hub (HDH). The organization of this event first consisted of recruiting nearly 5000 cervical biopsy slides obtained from 20 pathology centers. After ensuring that patients did not refuse to include their slides in the project, the slides were anonymized, digitized, and annotated by expert pathologists, and finally uploaded to a data challenge platform for competitors from around the world. Competing teams had to develop algorithms that could distinguish 4 diagnostic classes in cervical epithelial lesions. Among the many submissions from competitors, the best algorithms achieved an overall score close to 95%. The final part of the competition lasted only 6 weeks, and the goal of SFP and HDH is now to allow for the collection to be published in open access for the scientific community. In this report, we have performed a “post-competition analysis” of the results. We first described the algorithmic pipelines of 3 top competitors. We then analyzed several difficult cases that even the top competitors could not predict correctly. A medical committee of several expert pathologists looked for possible explanations for these erroneous results by reviewing the images, and we present their findings here targeted for a large audience of pathologists and data scientists in the field of digital pathology.

Published
2022
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13. Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology

Author

Qinghe Zeng, Christophe Klein, Stefano Caruso, Pascale Maille, Narmin Ghaffari Laleh, Daniele Sommacale, Alexis Laurent, Giuliana Amaddeo, David Gentien, Audrey Rapinat, Hélène Regnault, Cécile Charpy, Cong Trung Nguyen, Christophe Tournigand, Raffaele Brustia, Jean Michel Pawlotsky, Jakob Nikolas Kather, Maria Chiara Maiuri, Nicolas Loménie, and Julien Calderaro

Subjects
Carcinoma, Hepatocellular, ROC Curve, Hepatology, Artificial Intelligence, Liver Neoplasms, Humans
Abstract

Patients with hepatocellular carcinoma (HCC) displaying overexpression of immune gene signatures are likely to be more sensitive to immunotherapy, however, the use of such signatures in clinical settings remains challenging. We thus aimed, using artificial intelligence (AI) on whole-slide digital histological images, to develop models able to predict the activation of 6 immune gene signatures.AI models were trained and validated in 2 different series of patients with HCC treated by surgical resection. Gene expression was investigated using RNA sequencing or NanoString technology. Three deep learning approaches were investigated: patch-based, classic MIL and CLAM. Pathological reviewing of the most predictive tissue areas was performed for all gene signatures.The CLAM model showed the best overall performance in the discovery series. Its best-fold areas under the receiver operating characteristic curves (AUCs) for the prediction of tumors with upregulation of the immune gene signatures ranged from 0.78 to 0.91. The different models generalized well in the validation dataset with AUCs ranging from 0.81 to 0.92. Pathological analysis of highly predictive tissue areas showed enrichment in lymphocytes, plasma cells, and neutrophils.We have developed and validated AI-based pathology models able to predict the activation of several immune and inflammatory gene signatures. Our approach also provides insights into the morphological features that impact the model predictions. This proof-of-concept study shows that AI-based pathology could represent a novel type of biomarker that will ease the translation of our biological knowledge of HCC into clinical practice.Immune and inflammatory gene signatures may be associated with increased sensitivity to immunotherapy in patients with advanced hepatocellular carcinoma. In the present study, the use of artificial intelligence-based pathology enabled us to predict the activation of these signatures directly from histology.

Published
2022
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16. Le premier data challenge organisé par la Société Française de Pathologie : une compétition internationale en 2020, un outil de recherche en intelligence artificielle pour l’avenir ?

Author

Agathe Delaune, Séverine Valmary-Degano, Nicolas Loménie, Karim Zryouil, Nesrine Benyahia, Olivier Trassard, Virginie Eraville, Christine Bergeron, Mojgan Devouassoux-Shisheboran, Claire Glaser, Guillaume Bataillon, Emmanuel Bacry, Stéphanie Combes, Sophie Prevot, and Philippe Bertheau

Subjects
Pathology and Forensic Medicine
Published
2022
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38. Évaluation et adaptation de réseaux VGG16 pour la détection de carcinome sur des lames d'histologie: de la résection à la biopsie

Author

Qinghe ZENG, Nicolas Loménie, Christophe Klein, Julien Calderaro, Laboratoire d'Informatique Paris Descartes (LIPADE (URP_2517)), Université de Paris (UP), Centre d'Histologie, d'Imagerie et de Cytométrie (CHIC), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Hôpital Henri Mondor, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Henri Mondor-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Centre National de la Recherche Scientifique [CNRS], Université Paris Cité (UPCité), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), and Sciencesconf.org, CCSD

Subjects
[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Apprentissage Profond, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO]Computer Science [cs], [INFO] Computer Science [cs], Généralisation de Domaine, Pathologie Computationnelle
Abstract

International audience; La classification assistée par ordinateur des images histologiques est un sujet de plus en plus populaire pour les applications d’apprentissage profond. Les images histologiques sont multi-gigapixels, hiérarchiques et de dimensions non uniformes, alors que les images de biopsie sont de même nature mais contiennent une quantité très limitée de tissus, ce qui pose de nouveaux défis à la pathologie computationnelle. L’analyse de la biopsie est une tâche clinique difficile, mais elle joue parfois un rôle décisif dans le diagnostic et la sélection de la gestion du traitement. Dans cette étude, nous avons effectué une optimisation de l’architecture et des paramètres pour VGG16, et avons montréqu’ils avaient la capacité d’identifier les tumeurs sur les images histologiques de résection. Nous avons également démontré leur généralisabilité aux images histologiquesdes biopsies de carcinome hépatocelluclaire et proposé en outre une stratégie de chevauchement pour améliorer les résultats.

Published
2021

39. Pembrolizumab with Capox Bevacizumab in patients with microsatellite stable metastatic colorectal cancer and a high immune infiltrate: The FFCD 1703-POCHI trial

Author

Romain Coriat, Nicolas Loménie, Côme Lepage, Thierry Lecomte, Violaine Randrian, Camille Kurtz, Jean-Louis Legoux, Jean-Marc Phelip, Astrid Lièvre, Pierre Laurent-Puig, Julien Taieb, Jean-François Emile, Carole Monterymard, David Tougeron, Harry Sokol, Jérémie Bez, Laurent Mineur, Claire Gallois, Laetitia Dahan, Stefano Kim, Vincent Hautefeuille, Claude Capron, Marine Gilabert, Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Hôpital Ambroise Paré [AP-HP], Hôpital JeanMinjoz, Lipides - Nutrition - Cancer [Dijon - U1231] (LNC), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC), Chemistry, Oncogenesis, Stress and Signaling (COSS), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-CRLCC Eugène Marquis (CRLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Pontchaillou [Rennes], Hôpital de la Timone [CHU - APHM] (TIMONE), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Institut Sainte Catherine [Avignon], AP-HP - Hôpital Cochin Broca Hôtel Dieu [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre Hospitalier Régional d'Orléans (CHRO), CHU Amiens-Picardie, Centre Hospitalier Universitaire de Saint-Etienne (CHU de Saint-Etienne), CHU Trousseau [Tours], Centre Hospitalier Régional Universitaire de Tours (CHRU Tours), CHU Saint-Antoine [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Centre hospitalier universitaire de Poitiers (CHU Poitiers), Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon), Laboratoire d'Informatique Paris Descartes (LIPADE (URP_2517)), Université de Paris (UP), HalioDX and the 'Ligue nationale contre le cancer', Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Université de Rennes (UR)-CRLCC Eugène Marquis (CRLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Université Paris Cité (UPCité), Jonchère, Laurent, École Pratique des Hautes Études (EPHE), and Centre Hospitalier Universitaire de Saint-Etienne [CHU Saint-Etienne] (CHU ST-E)

Subjects
Oncology, medicine.medical_specialty, Bevacizumab, Colorectal cancer, medicine.medical_treatment, [SDV.CAN]Life Sciences [q-bio]/Cancer, Pembrolizumab, Antibodies, Monoclonal, Humanized, DNA Mismatch Repair, 03 medical and health sciences, Antineoplastic Agents, Immunological, Clinical Trials, Phase II as Topic, 0302 clinical medicine, Immune system, [SDV.CAN] Life Sciences [q-bio]/Cancer, Checkpoint blockade, Internal medicine, Antineoplastic Combined Chemotherapy Protocols, Clinical endpoint, Humans, Medicine, Immune Checkpoint Inhibitors, Chemotherapy, Hepatology, business.industry, Tumour-infiltrating lymphocytes, Gastroenterology, [SDV.MHEP.HEG]Life Sciences [q-bio]/Human health and pathology/Hépatology and Gastroenterology, medicine.disease, [SDV.MHEP.HEG] Life Sciences [q-bio]/Human health and pathology/Hépatology and Gastroenterology, digestive system diseases, 3. Good health, Oxaliplatin, Clinical trial, 030220 oncology & carcinogenesis, Microsatellite Instability, 030211 gastroenterology & hepatology, Colorectal Neoplasms, business, medicine.drug
Abstract

International audience; Pembrolizumab, a PD1 immune checkpoint inhibitor (ICI), was recently reported to be very effective in patients with microsatellite instable/deficient mismatch repair metastatic colorectal cancer (MSI/dMMR mCRC), unlike patients with microsatellite stable/proficient MMR (MSS/pMMR) mCRC, in whom ICIs are generally ineffective. However, about 15% of MSS/pMMR CRCs are highly infiltrated by tumour infiltrating lymphocytes. In addition, both oxaliplatin and bevacizumab have been shown to have immunomodulatory properties that may increase the efficacy of an ICI. We formulated the hypothesis that patients with MSS/pMMR mCRC with a high immune infiltrate can be sensitive to ICI plus oxalipatin and bevacizumab-based chemotherapy. POCHI is a multicenter, open-label, single-arm phase II trial to evaluate efficacy of Pembrolizumab with Capox Bevacizumab as first-line treatment of MSS/pMMR mCRC with a high immune infiltrate for which we plan to enrol 55 patients. Primary endpoint is progression-free survival (PFS) at 10 months, which is expected greater than 50%, but a 70% rate is hoped for. Main secondary objectives are overall survival, secondary resection rate and depth of response. Patients must have been resected of their primary tumour so as to evaluate two different immune scores (Immunoscore® and TuLIS) and are eligible if one score is "high";. The first patient was included on April 20, 2021.

Published
2021
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40. [The first data challenge of the french society of pathology: An international competition in 2020, a research tool in A.I. for the future?]

Author

Agathe, Delaune, Séverine, Valmary-Degano, Nicolas, Loménie, Karim, Zryouil, Nesrine, Benyahia, Olivier, Trassard, Virginie, Eraville, Christine, Bergeron, Mojgan, Devouassoux-Shisheboran, Claire, Glaser, Guillaume, Bataillon, Emmanuel, Bacry, Stéphanie, Combes, Sophie, Prevot, and Philippe, Bertheau

Subjects
Pathologists, Artificial Intelligence, Biopsy, Humans, Female, Cervix Uteri, Algorithms
Abstract

The french society of pathology (SFP) organized in 2020 its first data challenge with the help of Health Data Hub (HDH). The organisation of this event first consisted in recruiting almost 5000 slides of uterus cervical biopsies obtained in 20 pathology centers. After having made sure that patients did not refuse to include their slides in the project, the slides were anonymised, digitized and annotated by expert pathologists, and were finally uploaded on a data challenge platform for competitors all around the world. Competitors teams had to develop algorithms that could distinguish among four diagnostic classes in epithelial lesions of uterine cervix. Among many submissions by competitors, the best algorithms obtained an overall score close to 95%. The best 3 teams shared 25k€ prizes during a special session organised during the national congress of the SFP. The final part of the competition lasted only 6 weeks and the goal of SFP and HDH is now to allow for the collection to be published in open access. This final step will allow data scientists and pathologists to further develop artificial intelligence algorithms in this medical area.

Published
2021

41. Artificial intelligence for solid tumour diagnosis in digital pathology

Author

Maria Chiara Maiuri, Julien Calderaro, Nicolas Loménie, Estelle Devevre, Christophe Klein, Floriane Arbaretaz, and Qinghe Zeng

Subjects
Pharmacology, Solid tumour, High magnification, Computer science, business.industry, Visual examination, Digital pathology, Artificial Intelligence, Neoplasms, Medical imaging, Image Processing, Computer-Assisted, Humans, Artificial intelligence, business, Algorithms
Abstract

Tumour diagnosis relies on the visual examination of histological slides by pathologists through a microscope eyepiece. Digital pathology, the digitalization of histological slides at high magnification with slides scanners, has raised the opportunity to extract quantitative information due to image analysis. In the last decade, medical image analysis has made exceptional progress due to the development of artificial intelligence (AI) algorithms. AI has been successfully used in the field of medical imaging and more recently in digital pathology. The feasibility and usefulness of AI assisted pathology tasks have been demonstrated in the very last years and we can expect those developments to be applied to routine histopathology in the future. In this review, we will describe and illustrate this technique and present the most recent applications in the field of tumour histopathology. LINKED ARTICLES: This article is part of a themed issue on Molecular imaging - visual themed issue. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v178.21/issuetoc.

Published
2021

44. Mitosis detection in breast cancer histological images An ICPR 2012 contest

Author

Ludovic Roux, Daniel Racoceanu, Nicolas Loménie, Maria Kulikova, Humayun Irshad, Jacques Klossa, Frédérique Capron, Catherine Genestie, Gilles Le Naour, and Metin N Gurcan

Subjects
Automated mitotic cell detection, breast cancer, H and E stained histological slides, Computer applications to medicine. Medical informatics, R858-859.7, Pathology, RB1-214
Abstract

Introduction: In the framework of the Cognitive Microscope (MICO) project, we have set up a contest about mitosis detection in images of H and E stained slides of breast cancer for the conference ICPR 2012. Mitotic count is an important parameter for the prognosis of breast cancer. However, mitosis detection in digital histopathology is a challenging problem that needs a deeper study. Indeed, mitosis detection is difficult because mitosis are small objects with a large variety of shapes, and they can thus be easily confused with some other objects or artefacts present in the image. We added a further dimension to the contest by using two different slide scanners having different resolutions and producing red-green-blue (RGB) images, and a multi-spectral microscope producing images in 10 different spectral bands and 17 layers Z-stack. 17 teams participated in the study and the best team achieved a recall rate of 0.7 and precision of 0.89. Context: Several studies on automatic tools to process digitized slides have been reported focusing mainly on nuclei or tubule detection. Mitosis detection is a challenging problem that has not yet been addressed well in the literature. Aims: Mitotic count is an important parameter in breast cancer grading as it gives an evaluation of the aggressiveness of the tumor. However, consistency, reproducibility and agreement on mitotic count for the same slide can vary largely among pathologists. An automatic tool for this task may help for reaching a better consistency, and at the same time reducing the burden of this demanding task for the pathologists. Subjects and Methods: Professor Frιdιrique Capron team of the pathology department at Pitiι-Salpκtriθre Hospital in Paris, France, has selected a set of five slides of breast cancer. The slides are stained with H and E. They have been scanned by three different equipments: Aperio ScanScope XT slide scanner, Hamamatsu NanoZoomer 2.0-HT slide scanner and 10 bands multispectral microscope. The data set is made up of 50 high power fields (HPF) coming from 5 different slides scanned at ×40 magnification. There are 10 HPFs/slide. The pathologist has annotated all the mitotic cells manually. A HPF has a size of 512 μm × 512 μm (that is an area of 0.262 mm 2 , which is a surface equivalent to that of a microscope field diameter of 0.58 mm. These 50 HPFs contain a total of 326 mitotic cells on images of both scanners, and 322 mitotic cells on the multispectral microscope. Results : Up to 129 teams have registered to the contest. However, only 17 teams submitted their detection of mitotic cells. The performance of the best team is very promising, with F-measure as high as 0.78. However, the database we provided is by far too small for a good assessment of reliability and robustness of the proposed algorithms. Conclusions : Mitotic count is an important criterion in the grading of many types of cancers, however, very little research has been made on automatic mitotic cell detection, mainly because of a lack of available data. A main objective of this contest was to propose a database of mitotic cells on digitized breast cancer histopathology slides to initiate works on automated mitotic cell detection. In the future, we would like to extend this database to have much more images from different patients and also for different types of cancers. In addition, mitotic cells should be annotated by several pathologists to reflect the partial agreement among them.

Published
2013
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45. A novel Computer-Aided Tree Species Identification method based on Burst Wind Segmentation of 3D bark textures

Author

Jean-Marie Favreau, Alexandre Piboule, Alice Othmani, Cansen Jiang, Lew F.C. Lew Yan Voon, Nicolas Loménie, Laboratoire Electronique, Informatique et Image ( Le2i ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), Image Science for Interventional Techniques ( ISIT ), Clermont Université-Centre National de la Recherche Scientifique ( CNRS ) -Université d'Auvergne - Clermont-Ferrand I ( UdA ), Office National des Forêts - ONF (FRANCE), Laboratoire d'Informatique Paris Descartes ( LIPADE - EA 2517 ), Université Paris Descartes - Paris 5 ( UPD5 ), Conseil Regional de Bourgogne 2010 9201AAO048S06469, 2010 9201CPERO007S06470, Office National des Forets, Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Image Science for Interventional Techniques (ISIT), Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Centre National de la Recherche Scientifique (CNRS), Office National des Forêts (ONF), Laboratoire d'Informatique Paris Descartes (LIPADE - EA 2517), Université Paris Descartes - Paris 5 (UPD5), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Office national des forêts (ONF), Laboratoire Electronique, Informatique et Image [UMR6303] ( Le2i ), Université de Bourgogne ( UB ) -École Nationale Supérieure d'Arts et Métiers ( ENSAM ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), Université d'Auvergne - Clermont-Ferrand I ( UdA ) -Clermont Université-Centre National de la Recherche Scientifique ( CNRS ), and Université d'Auvergne - Clermont-Ferrand I (UdA)-Centre National de la Recherche Scientifique (CNRS)-Clermont Université

Subjects
Terrestrial laser scanning, Single tree species recognition, [ INFO ] Computer Science [cs], 010504 meteorology & atmospheric sciences, Computer science, Point cloud, Context (language use), 02 engineering and technology, 01 natural sciences, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 0202 electrical engineering, electronic engineering, information engineering, Segmentation, Computer vision, [INFO]Computer Science [cs], [ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI], 0105 earth and related environmental sciences, Forest inventory, business.industry, 3D geometric textures classification, Point Clouds, Diameter at breast height, 15. Life on land, Classification, Computer Science Applications, Identification (information), Hardware and Architecture, Salient, Pattern recognition (psychology), 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, business, Software
Abstract

International audience; Terrestrial Laser Scanning (TLS) systems have gained increasing popularity in the forestry domain and are today widely used for the automatic measurement of forest inventory attributes. Nevertheless, to the best of our knowledge the problem of tree species recognition from TLS data has received very little attention from the scientific community. It is in this context that we present a novel Computer-Aided Tree Species Identification method based on 3D bark texture analysis. The novelty of our approach resides in the following three key points: (1) 3D salient regions extraction using a new morphological segmentation method that we have called Burst Wind Segmentation, (2) the extraction and pre-annotation of a collection of typical 3D bark patterns, known as scars, from each of the tree species. The pre-annotated scars are stored in a dictionary that we have called ScarBook and they are used as a reference for the comparison of the 3D salient segmented regions, (3) a wide variety of advanced shape, saliency, curvature and roughness features are extracted from the 3D salient segmented regions. To study the performance of our method, an experiment has been carried out on a dataset composed of 969 patches which correspond to 30 cm long segments of the trunk at breast height. Six species among the most dominant species in European forests have been tested with patches of different diameter at breast height values so as to study the identification accuracy with respect to age. The results obtained are very encouraging and promising and they confirm the possibility of identifying tree species using TLS data.

Published
2016
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46. Point set morphological filtering and semantic spatial configuration modeling: Application to microscopic image and bio-structure analysis

Author

Daniel Racoceanu and Nicolas Loménie

Subjects
0303 health sciences, Semantic query, Theoretical computer science, business.industry, Delaunay triangulation, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, 02 engineering and technology, Visual reasoning, Sparse approximation, 03 medical and health sciences, Spatial relation, Artificial Intelligence, Mesh analysis, Lattice (order), Signal Processing, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, business, Software, 030304 developmental biology, Shape analysis (digital geometry), Mathematics
Abstract

High-level spatial relation and configuration modeling issues are gaining momentum in the image analysis and pattern recognition fields. In particular, it is deemed important whenever one needs to mine high-content images or large scale image databases in a more expressive way than a purely statistically one. Continuing previous efforts to incorporate structural analysis by developing specific efficient morphological tools performing on mesh representations like Delaunay triangulations, we propose to formalize spatial relation modeling techniques dedicated to unorganized point sets. We provide an original mesh lattice framework which is more convenient for structural representations of large image data by means of interest point sets and their morphological analysis. The set of designed numerical operators is based on a specific dilation operator that makes it possible to handle concepts like ''between'' or ''left of'' over sparse representations of image data such as graphs. Based on this new theoretical framework for reasoning about images, we are able to process high-level queries over large histopathological images, knowing that digitized histopathology is a new challenge in the field of bio-imaging due to the high-content nature and large size of these images.

Published
2012
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47. Search Guided Saliency

Author

Jianfei Cai, Byung-Uck Kim, Nicolas Loménie, Shijian Lu, and Joo-Hwee Lim

Subjects
Visual search, Pixel, Computer science, business.industry, Histogram, Homogeneity (statistics), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, Artificial intelligence, business
Abstract

We propose a new type of saliency as inspired by findings from visual search studies - the searching difficulty is correlated with the target-distractor contrast, the distractor homogeneity, as well as the target uniqueness. By putting an image pixel as the target and the surrounding pixels as distractors, a search guided saliency model is designed in accordance with these findings. In particular, three saliency measures in correspondence to the three searching factors are simultaneously computed and integrated by using a series of contextual histograms. The proposed model has been evaluated over three public datasets and experiments show superior prediction of the human fixations when compared to the state-of-the-art models.

Published
2015
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48. Advances in Bio-Imaging: From Physics to Signal Understanding Issues : State-of-the-Art and Challenges

Author

Nicolas Loménie, Daniel Racoceanu, Alexandre Gouaillard, Nicolas Loménie, Daniel Racoceanu, and Alexandre Gouaillard

Subjects
Diagnostic imaging, Bioinformatics
Abstract

Advances in Imaging Devices and Image processing stem from cross-fertilization between many fields of research such as Chemistry, Physics, Mathematics and Computer Sciences.This BioImaging Community feel the urge to integrate more intensively its various results, discoveries and innovation into ready to use tools that can address all the new exciting challenges that Life Scientists (Biologists, Medical doctors,...) keep providing, almost on a daily basis.Devising innovative chemical probes, for example, is an archetypal goal in which image quality improvement must be driven by the physics of acquisition, the image processing and analysis algorithms and the chemical skills in order to design an optimal bioprobe. This book offers an overview of the current advances in many research fields related to bioimaging and highlights the current limitations that would need to be addressed in the next decade to design fully integrated BioImaging Device.

Published
2012

49. Tree Species Classification Based on 3D Bark Texture Analysis

Author

Lew F.C. Lew Yan Voon, Alexandre Piboule, Nicolas Loménie, Oscar Dalmau, Said Mokrani, Ahlem Othmani, Laboratoire Electronique, Informatique et Image [UMR6303] ( Le2i ), Centre National de la Recherche Scientifique ( CNRS ) -Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-École Nationale Supérieure d'Arts et Métiers ( ENSAM ), Image Science for Interventional Techniques ( ISIT ), Université d'Auvergne - Clermont-Ferrand I ( UdA ) -Clermont Université-Centre National de la Recherche Scientifique ( CNRS ), Image & Pervasive Access Lab ( IPAL ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -National University of Singapore ( NUS ) -MATHEMATIQUES, SCIENCES ET TECHNOLOGIES DE L'INFORMATION ET DE LA COMMUNICATION (UJF)-Agency for science, technology and research [Singapore] ( A*STAR ) -Centre National de la Recherche Scientifique ( CNRS ) -Institute for Infocomm Research - I²R [Singapore], Office National des Forêts - ONF (FRANCE), Centro de Investigación en Matemáticas ( CIMAT ), Consejo Nacional de Ciencia y Tecnología [Mexico] ( CONACYT ), Centre de Recherche en Informatique de Paris 5 ( CRIP5 - EA 2517 ), Université Paris Descartes - Paris 5 ( UPD5 ), Laboratoire Electronique, Informatique et Image ( Le2i ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Image Science for Interventional Techniques (ISIT), Université d'Auvergne - Clermont-Ferrand I (UdA)-Centre National de la Recherche Scientifique (CNRS)-Clermont Université, Image & Pervasive Access Lab (IPAL), Institute for Infocomm Research - I²R [Singapore]-Centre National de la Recherche Scientifique (CNRS)-Agency for science, technology and research [Singapore] (A*STAR)-MATHEMATIQUES, SCIENCES ET TECHNOLOGIES DE L'INFORMATION ET DE LA COMMUNICATION (UJF)-National University of Singapore (NUS)-Université Pierre et Marie Curie - Paris 6 (UPMC), Office National des Forêts (ONF), Centro de Investigación en Matemáticas (CIMAT), Consejo Nacional de Ciencia y Tecnología [Mexico] (CONACYT), Centre de Recherche en Informatique de Paris 5 (CRIP5 - EA 2517), Université Paris Descartes - Paris 5 (UPD5), Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Centre National de la Recherche Scientifique (CNRS), Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre et Marie Curie - Paris 6 (UPMC)-National University of Singapore (NUS)-Agency for science, technology and research [Singapore] (A*STAR)-Centre National de la Recherche Scientifique (CNRS)-Institute for Infocomm Research - I²R [Singapore], Office national des forêts (ONF), and Clermont Université-Centre National de la Recherche Scientifique (CNRS)-Université d'Auvergne - Clermont-Ferrand I (UdA)

Subjects
[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing, Computer science, Point cloud, 02 engineering and technology, computer.software_genre, [ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Texture (geology), 3D pattern recognition, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [ INFO.INFO-TI ] Computer Science [cs]/Image Processing, 0202 electrical engineering, electronic engineering, information engineering, forest inventory, [STAT.AP]Statistics [stat]/Applications [stat.AP], Forest inventory, Trees pecies classification, [ STAT.AP ] Statistics [stat]/Applications [stat.AP], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 3D bark texture analysis, 020206 networking & telecommunications, Terrestrial laser scanning, 15. Life on land, Random forest, visual_art, Test set, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], visual_art.visual_art_medium, 020201 artificial intelligence & image processing, Bark, Data mining, computer, Tree species
Abstract

Terrestrial Laser Scanning (TLS) technique is today widely used in ground plots to acquire 3D point clouds from which forest inventory attributes are calculated. In the case of mixed plantings where the 3D point clouds contain data from several different tree species, it is important to be able to automatically recognize the tree species in order to analyze the data of each of the species separately. Although automatic tree species recognition from TLS data is an important problem, it has received very little attention from the scientific community. In this paper we propose a method for classifying five different tree species using TLS data. Our method is based on the analysis of the 3D geometric texture of the bark in order to compute roughness measures and shape characteristics that are fed as input to a Random Forest classifier to classify the tree species. The method has been evaluated on a test set composed of 265 samples (53 samples of each of the 5 species) and the results obtained are very encouraging.

Published
2013
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50. Adaptive picture-in-picture technology based on visual saliency

Author

Nicolas Loménie, Joo-Hwee Lim, Byung-Uck Kim, and Shijian Lu

Subjects
Computer science, business.industry, Picture-in-picture, Frame (networking), Process (computing), Display device, Feature (computer vision), Computer graphics (images), Key (cryptography), Computer vision, Artificial intelligence, business, Visual saliency, Block (data storage)
Abstract

Picture-in-picture (PiP) is a feature of some television receivers and video devices, which allows one main program to be displayed on the full screen while one or more subprogram displayed in inset windows. Currently most TV/video devices require users to specify where and how large to place the sub-program over the main program display. This process is however not user-friendly as it involves a manual process and once specified, the size and the location of the sub-program will be fixed even when they block some key visual information from the main program. We propose an automatic and adaptive PiP technology that makes use of computational modeling of visual saliency. For each frame of the main program, a saliency map is computed efficiently which quantifies how probable a display region of the main program contains useful information and will attract humans' attention/eyes. The sub-program can thus be adaptively resized and placed to the display region that contains the least useful information. Preliminary experiments show the effectiveness of the proposed technology.

Published
2013
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