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201. Shaping for PET image analysis

Author

Grossiord, Éloïse, Passat, Nicolas, Talbot, Hugues, Naegel, Benoît, Kanoun, Salim, Tal, Ilan, Tervé, Pierre, Ken, Soléakhéna, Casasnovas, Olivier, Meignan, Michel, and Najman, Laurent

Published
2020
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202. P 45. Different K+-release in distal myopathy and motor neuropathy during non-ischemic exercise

Author

Stephan Zierz, A. Mensch, S.A. Göbel, Steffen Naegel, and P. Burow

Subjects
medicine.medical_specialty, business.industry, medicine.disease, Myotonic dystrophy, Sensory Systems, K release, medicine.anatomical_structure, Endocrinology, Neurology, Forearm, Physiology (medical), Internal medicine, Healthy volunteers, medicine, Neurology (clinical), Maximal contraction, Non ischemic, medicine.symptom, business, Myopathy, Motor neuropathy
Abstract

Background. In myotonic dystrophy, a significantly increased potassium release upon exercise was previously described. However, it remained unclear so far whether this is a specific feature of myotonic dystrophies or a consequence of distal muscular weakness in general. Methods. We performed non-ischemic forearm exercise test (NIFET) and measured venous K+ concentration at rest and at three different force levels (20–30%, 50–60%, 70–80%) related to maximal contraction force (MCF) in patients with different myopathies (n = 7), motor neuropathies (n = 8) with distal phenotype and healthy volunteers (n = 12). The specific K+ production was defined as venous K+ increase related to workload. Results. Maximal contraction force, workload and venous K+ increase were lower at all force levels in both myopathy and neuropathy groups compared to the control group. With increasing workload, the control group showed a quasi-linear increase in K+ release, resulting in a nearly constant specific K+ production within the three force levels (p = 0.752, Kruskal-Wallis-Test). In contrast, in myopathies a higher specific K+ production was measured at low force levels (20-30% MCF) in comparison to higher force levels (p = 0.015, Kruskal-Wallis test). In neuropathies, the lowest specific K+ production was obtained at 20–30% MCF. At this force level, the specific K+ production was significantly higher in myopathies compared to neuropathies (Fig. 1; p = 0.005, Mann-Whitney U Test). At 50–60% and 70–80% MCF, the specific K+ values increasingly converged and did not significantly differ between the three groups (Fig. 1; p = 0.09 and p = 0.37, Kruskal-Wallis test). Conclusion. In our study, increased K+ efflux related to workload occurred in patients with myogenic in comparison to neurogenic distal muscular weakness at low force levels. High specific K+ production does not seem to be an exclusive feature of myotonic dystrophies but of myogenic muscular weakness in general. Our results point to a different regulation of K+ balance in neurogenic and myogenic muscular weakness possibly due to a different recruitment behaviour of motor units and the action potential frequency of motor neurons in neurogenic and myogenic processes. Fig. 1.

Published
2021

203. Cerebrovascular Network Segmentation of MRA Images With Deep Learning

Author

Cyril Meyer, Pedro Sanchesa, Benoît Naegel, Vincent Vigon, Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche Mathématique Avancée (IRMA), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), and Naegel, Benoît

Subjects
FOS: Computer and information sciences, Network segmentation, Computer Science - Artificial Intelligence, Computer science, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Context (language use), Convolutional Neural Network, Convolutional neural network, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Deep Learning, Segmentation, Medical imaging, Structure (mathematical logic), business.industry, Deep learning, Pattern recognition, Artificial Intelligence (cs.AI), [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Artificial intelligence, State (computer science), business, 030217 neurology & neurosurgery, Magnetic Resonance Angiography
Abstract

Deep learning has been shown to produce state of the art results in many tasks in biomedical imaging, especially in segmentation. Moreover, segmentation of the cerebrovascular structure from magnetic resonance angiography is a challenging problem because its complex geometry and topology have a large inter-patient variability. Therefore, in this work, we present a convolutional neural network approach for this problem. Particularly, a new network topology inspired by the U-net 3D and by the Inception modules, entitled Uception. In addition, a discussion about the best objective function for sparse data also guided most choices during the project. State of the art models are also implemented for a comparison purpose and final results show that the proposed architecture has the best performance in this particular context.

Published
2019

209. Predicting the available space for liver transplantation in cirrhotic patients: a computed tomography-based volumetric study

Author

François Faitot, Caroline Schaaf, Pietro Addeo, Philippe Bachellier, Benoît Naegel, Alfonso Terrone, Lawrence Serfaty, Chloe Paul, Pierre de Mathelin, Vincent Noblet, Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace (FMNGE), and Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Liver Cirrhosis, medicine.medical_specialty, medicine.medical_treatment, Population, Liver transplantation, Severity of Illness Index, End Stage Liver Disease, 03 medical and health sciences, Liver disease, 0302 clinical medicine, Internal medicine, Ascites, medicine, Humans, education, 2. Zero hunger, education.field_of_study, Hepatology, business.industry, medicine.disease, Liver Transplantation, Liver, 030220 oncology & carcinogenesis, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Cohort, Portal hypertension, 030211 gastroenterology & hepatology, medicine.symptom, business, Nuclear medicine, Tomography, X-Ray Computed, Body mass index
Abstract

Anthropometric parameters (weight, height) are usually used for quick matching between two individuals (donor and recipient) in liver transplantation (LT). This study aimed to evaluate clinical factors influencing the overall available space for implanting a liver graft in cirrhotic patients. In a cohort of 275 cirrhotic patients undergoing LT, we calculated the liver volume (LV), cavity volume (CV), which is considered the additional space between the liver and the right hypocondrium, and the overall volume (OV = LV + CV) using a computed tomography (CT)-based volumetric system. We then chose the formula based on anthropometric parameters that showed the best predictive value for LV. This formula was used to predict the OV in the same population. Factors influencing OV variations were identified by multivariable logistic analysis. The Hashimoto formula (961.3 × BSA_D-404.8) yielded the lowest median absolute percentage error (21.7%) in predicting the LV. The median LV was 1531 ml. One-hundred eighty-five patients (67.2%) had a median CV of 1156 ml (range: 70–7006), and the median OV was 2240 ml (range: 592–8537). Forty-nine patients (17%) had an OV lower than that predicted by the Hashimoto formula. Independent factors influencing the OV included the number of portosystemic shunts, right anteroposterior abdominal diameter, model for end-stage liver disease (MELD) score > 25, high albumin value, and BMI > 30. Additional anthropometric characteristics (right anteroposterior diameter, body mass index) clinical (number of portosystemic shunts), and biological (MELD, albumin) factors might influence the overall volume available for liver graft implantation. Knowledge of these factors might be helpful during the donor–recipient matching.

Published
2020

210. Erenumab in Highly Therapy-refractory Migraine Patients: First Real-world Evidence

Author

Armin Scheffler, Olga Messel, Sebastian Wurthmann, Michael Nsaka, Christoph Kleinschnitz, Martin Glas, Steffen Naegel, and Dagny Holle-Lee

Abstract

Background: Calcitonin gene related peptide (CGRP) monoclonal antibodies (mAB) are the first specific migraine prophylactic medication. Erenumab is the only CGRP mAB targeting the CGRP receptor. Clinical data regarding efficacy and tolerability of erenumab in highly therapy-refractory patients are not available, yet, although many patients treated with CGRP mAB under real world conditions can be considered as highly therapy-refractory.Methods: Clinical routine data of highly therapy-refractory migraine patients treated with erenumab 70mg for three months between November 2018 and December 2019 in the West German Headache Center, University Hospital Essen, Germany, were analysed. Monthly migraine days (MMD), monthly headache days (MHD) and days of acute medication intake (AMD) were assessed. Statistical analysis was performed using the Wilcoxon test. Descriptive statistics were performed to evaluate changes of vegetative symptoms, acute medication response, side effects, as well as treatment satisfaction.Results: Complete clinical data were available for 26 episodic (EM) and 74 chronic (CM) migraineurs. Sixty-six % (n=49) of CM patients had an additional medication overuse headache (MOH). After three months 57.7 % of EM patients and 41.9 % of CM patients had a 50% or greater reduction of MMD. The mean number of MMD was reduced by 3.43 (SE 1.26) in EM, and by 4.72 (SE 0.87) in CM. Thirty-nine patients (52.7%) returned from chronic to episodic course of migraine. After three months, 23 patients (46.9%) were not suffering from a MOH anymore.Conclusions: Erenumab seems to be a promising therapeutic option in highly therapy-refractory migraine patients.

Published
2020

211. Large-for-Size Orthotopic Liver Transplantation: a Systematic Review of Definitions, Outcomes, and Solutions

Author

Benoît Naegel, Philippe Bachellier, Pietro Addeo, Vincent Noblet, Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, and Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Male, medicine.medical_specialty, Orthotopic liver transplantation, medicine.medical_treatment, Liver transplantation, Cochrane Library, 03 medical and health sciences, 0302 clinical medicine, Negative-pressure wound therapy, medicine, Hepatectomy, Humans, Clinical syndrome, Body surface area, business.industry, Graft Survival, Gastroenterology, Organ Size, Anthropometry, Liver Transplantation, 3. Good health, Surgery, Treatment Outcome, Systematic review, Liver, 030220 oncology & carcinogenesis, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Female, 030211 gastroenterology & hepatology, business
Abstract

We systematically reviewed the literature on definitions and outcomes of large-for-size (LFS) syndrome in orthotopic liver transplantation (LT). This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Cochrane Library, PubMed, and Embase were searched (January 1990–January 2019) for studies reporting LFS in LT. Primary outcomes were definitions and mortality of LFS LT. Eleven studies reporting patients with LFS LT were identified. Four different formulas (graft-to-recipient weight ratio (GRWR), body surface area index (BSAi), donor standardized total liver volume (sTLV)–to–recipient sTLV ratio, and graft weight/right anteroposterior distance (RAP) ratio) with their critical thresholds were found. There were 81 patients (54% women) with a median weight and height of 62.5 kg (range, 40–105 kg) and 165 cm (range, 145–180 cm). The median graft weight was 1772 g (range, 1290–2400 g), and the median GWRW was 2.77% (range, 2.1–4.00%). Graft venous outflow obstruction was described in seven patients (8.6%). At the time of LT, fascial closure was not achieved in 24 patients (29.6%) and the graft size was reduced by a liver resection in three patients (3.7%). Thirteen deaths (16%) were reported in the first 90 postoperative days with two patients undergoing re-transplant. LFS LT remains heterogeneously defined but characterized by high mortality rates despite the use of tailored surgical solutions (graft reduction and open abdomen). A composite definition is proposed in order to better describe LFS clinical syndrome.

Published
2020

212. Erenumab in highly therapy-refractory migraine patients : First German real-world evidence

Author

Dagny Holle, Martin Glas, Olga Messel, Michael Nsaka, Christoph Kleinschnitz, Sebastian Wurthmann, Armin Scheffler, and Steffen Naegel

Subjects
Adult, Male, medicine.medical_specialty, Neurology, Calcitonin Gene-Related Peptide, Migraine Disorders, Medizin, lcsh:Medicine, Calcitonin gene-related peptide, Antibodies, Monoclonal, Humanized, Real world evidence, 03 medical and health sciences, 0302 clinical medicine, Double-Blind Method, Calcitonin Gene-Related Peptide Receptor Antagonists, Germany, Internal medicine, 2. erenumab, Headache Disorders, Secondary, Humans, Medicine, 030212 general & internal medicine, CGRP receptor, Retrospective Studies, 1. migraine, business.industry, lcsh:R, Headache, General Medicine, Middle Aged, 5. CGRP antibody, University hospital, medicine.disease, 4. real-world, Anesthesiology and Pain Medicine, 3. therapy, Tolerability, Migraine, Refractory migraine, Female, Neurology (clinical), business, 030217 neurology & neurosurgery, Research Article, Receptors, Calcitonin Gene-Related Peptide
Abstract

Background: Calcitonin gene related peptide (CGRP) monoclonal antibodies (mAB) are the first specific migraine prophylactic medication. Erenumab is the only CGRP mAB targeting the CGRP receptor. Clinical data regarding efficacy and tolerability of erenumab in highly therapy-refractory patients are not available, yet, although many patients treated with CGRP mAB under real world conditions can be considered as highly therapy-refractory.Methods: Clinical routine data of highly therapy-refractory migraine patients treated with erenumab 70mg for three months between November 2018 and December 2019 in the West German Headache Center, University Hospital Essen, Germany, were analysed. Monthly migraine days (MMD), monthly headache days (MHD) and days of acute medication intake (AMD) were assessed. Statistical analysis was performed using the Wilcoxon test. Descriptive statistics were performed to evaluate changes of vegetative symptoms, acute medication response, side effects, as well as treatment satisfaction.Results: Complete clinical data were available for 26 episodic (EM) and 74 chronic (CM) migraineurs. Sixty-six % (n=49) of CM patients had an additional medication overuse headache (MOH). After three months 57.7 % of EM patients and 41.9 % of CM patients had a 50% or greater reduction of MMD. The mean number of MMD was reduced by 3.43 (SE 1.26) in EM, and by 4.72 (SE 0.87) in CM. Thirty-nine patients (52.7%) returned from chronic to episodic course of migraine. After three months, 23 patients (46.9%) were not suffering from a MOH anymore.Conclusions: Erenumab seems to be a promising therapeutic option in highly therapy-refractory migraine patients.Trial registration: Retrospective registered.

Published
2020

213. Shaping for PET image analysis

Author

Pierre Tervé, Olivier Casasnovas, Benoît Naegel, Ilan Tal, Salim Kanoun, Nicolas Passat, Eloïse Grossiord, Hugues Talbot, Laurent Najman, Michel Meignan, S. Ken, Institut de Mathématiques de Toulouse UMR5219 (IMT), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804 (CRESTIC), Université de Reims Champagne-Ardenne (URCA), OPtimisation Imagerie et Santé (OPIS), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de vision numérique (CVN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-CentraleSupélec-Université Paris-Saclay, Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre Régional de Lutte contre le cancer Georges-François Leclerc [Dijon] (UNICANCER/CRLCC-CGFL), UNICANCER, Institut Universitaire du Cancer de Toulouse - Oncopole (IUCT Oncopole - UMR 1037), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-CHU Toulouse [Toulouse]-Institut National de la Santé et de la Recherche Médicale (INSERM), Keosys, Service d'Hématologie Clinique (CHU de Dijon), Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon), The Lymphoma Academic Research Organisation [Lyon] (LYSARC), Laboratoire d'Informatique Gaspard-Monge (LIGM), École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel, Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Organ Modeling through Extraction, Representation and Understanding of Medical Image Content (GALEN-POST), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Régional de Lutte contre le cancer - Centre Georges-François Leclerc (CRLCC - CGFL), École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel (UNIV GUSTAVE EIFFEL), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace (FMNGE)

Subjects
Computer science, Physics::Medical Physics, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Context (language use), 02 engineering and technology, 01 natural sciences, Image (mathematics), Artificial Intelligence, Simple (abstract algebra), 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, medicine, Medical imaging, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, 010306 general physics, medicine.diagnostic_test, business.industry, Cancer, Pattern recognition, medicine.disease, Maxima and minima, Positron emission tomography, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Signal Processing, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, business, Software
Abstract

International audience; Component-trees constitute an efficient data structure for hierarchical image modeling.In particular they are relevant for processing and analyzing images where the structures of interest correspond either to local maxima or local minima of intensity.This is indeed the case of functional data in medical imaging.This motivates the use of component-tree-based approaches for analyzing Positron Emission Tomography (PET) images in the context of oncology.In this article, we present a simple, yet efficient, methodological framework for PET image analysis based on component-trees.More precisely, we show that the second-order paradigm of shaping, that broadly consists of computing the component-tree of a component-tree, provides a relevant way of generalizing the threshold-based strategies classically used by medical practitioners for handling PET images. In addition, it also allows to embed relevant priors regarding the sought cancer lesions.

Published
2020

214. Segmentation of axillary and supraclavicular tumoral lymph nodes in PET/CT: A hybrid CNN/component-tree approach

Author

Nicolas Gogin, D. Morland, D. L. Farfan Cabrera, Benoît Naegel, Nicolas Passat, Dimitri Papathanassiou, Passat, Nicolas, Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804 (CRESTIC), Université de Reims Champagne-Ardenne (URCA), General Electric Medical Systems [Buc] (GE Healthcare), General Electric Medical Systems, Institut Jean Godinot [Reims], UNICANCER, Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace (FMNGE), and Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Computer science, PET/CT, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, component-tree, [SDV.CAN]Life Sciences [q-bio]/Cancer, 02 engineering and technology, Convolutional neural network, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, 0302 clinical medicine, Breast cancer, [SDV.CAN] Life Sciences [q-bio]/Cancer, lymph nodes, 0202 electrical engineering, electronic engineering, information engineering, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Segmentation, PET-CT, business.industry, segmentation, Pattern recognition, medicine.disease, U-Net, 3. Good health, Supraclavicular lymph nodes, Tree (data structure), medicine.anatomical_structure, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, 020201 artificial intelligence & image processing, Artificial intelligence, Lymph, business, 030217 neurology & neurosurgery, CNN, region-based features
Abstract

International audience; The analysis of axillary and supraclavicular lymph nodes is a primary prognostic factor for the staging of breast cancer. However, due to the size of lymph nodes and the low resolution of PET data, their segmentation is challenging. We investigate the relevance of considering axillary and supraclavicular lymph node segmentation from PET/CT images by coupling Convolutional Neural Networks (CNNs) and Component-Trees (C-Trees). Building upon the U-Net architecture, we propose a framework that couples a multi-modal U-Net fed with PET and CT, coupled with a hierarchical model obtained from the PET that provides additional high-level region-based features as input channels. Our working hypotheses are twofold.First, we take advantage of both anatomical information from CT for detecting the nodes, and functional information from PET for detecting the pathological ones. Second, we consider region-based attributes extracted from C-Tree analysis of 3D PET/CT images to improve the CNN segmentation. We carried out experiments on a dataset of 240 pathological lymph nodes from 52 patients scans, and compared our outputs with human expert-defined ground-truth, leading to promising results.

Published
2020

215. sj-pdf-2-cep-10.1177_0333102420977183 - Supplemental material for Prevalence and risk factors of migraine and non-migraine headache in older people – results of the Heinz Nixdorf Recall study

Author

Schramm, Sara, Tenhagen, Isabell, Schmidt, Börge, Holle-Lee, Dagny, Naegel, Steffen, Katsarava, Zaza, Karl-Heinz Jöckel, and Moebus, Susanne

Subjects
FOS: Psychology, FOS: Clinical medicine, 170199 Psychology not elsewhere classified, 110319 Psychiatry (incl. Psychotherapy), 110306 Endocrinology, 111599 Pharmacology and Pharmaceutical Sciences not elsewhere classified, 110904 Neurology and Neuromuscular Diseases, Neuroscience
Abstract

Supplemental material, sj-pdf-2-cep-10.1177_0333102420977183 for Prevalence and risk factors of migraine and non-migraine headache in older people – results of the Heinz Nixdorf Recall study by Sara Schramm, Isabell Tenhagen, Börge Schmidt, Dagny Holle-Lee, Steffen Naegel, Zaza Katsarava, Karl-Heinz Jöckel and Susanne Moebus in Cephalalgia

Published
2020
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216. Editorial — Special Issue: ISMM 2019

Author

Benoît Naegel, Bernhard Burgeth, Benjamin Perret, and Andreas Kleefeld

Subjects
ismm, Computer science, 0202 electrical engineering, electronic engineering, information engineering, Calculus, QA1-939, 020201 artificial intelligence & image processing, mathematical morphology, 02 engineering and technology, Mathematical morphology, ddc:510, Geometry and topology, Mathematics
Abstract

This editorial presents the Special Issue dedicated to the conference ISMM 2019 and summarizes the articles published in this Special Issue.

Published
2020

217. sj-pdf-1-cep-10.1177_0333102420977183 - Supplemental material for Prevalence and risk factors of migraine and non-migraine headache in older people – results of the Heinz Nixdorf Recall study

Author

Schramm, Sara, Tenhagen, Isabell, Schmidt, Börge, Holle-Lee, Dagny, Naegel, Steffen, Katsarava, Zaza, Karl-Heinz Jöckel, and Moebus, Susanne

Subjects
FOS: Psychology, FOS: Clinical medicine, 170199 Psychology not elsewhere classified, 110319 Psychiatry (incl. Psychotherapy), 110306 Endocrinology, 111599 Pharmacology and Pharmaceutical Sciences not elsewhere classified, 110904 Neurology and Neuromuscular Diseases, Neuroscience
Abstract

Supplemental material, sj-pdf-1-cep-10.1177_0333102420977183 for Prevalence and risk factors of migraine and non-migraine headache in older people – results of the Heinz Nixdorf Recall study by Sara Schramm, Isabell Tenhagen, Börge Schmidt, Dagny Holle-Lee, Steffen Naegel, Zaza Katsarava, Karl-Heinz Jöckel and Susanne Moebus in Cephalalgia

Published
2020
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218. Mortality estimates of the intertidal purple snail Plicopurpura pansa in the Baja California Peninsula, Mexico

Author

Lopez-Rocha, Jorge A. and Naegel, Ludwig

Subjects
Baja California (Peninsula) -- Environmental aspects, Mortality -- Mexico, Fish populations -- Management -- Protection and preservation -- Environmental aspects, Snails -- Protection and preservation -- Environmental aspects, Biological sciences, Zoology and wildlife conservation, Company business management, Management, Protection and preservation, Environmental aspects
Abstract

ABSTRACT The intertidal purple snail Plicopurpura pansa was intensively exploited for 'Tyrian Purple,' leading to declining populations and prohibition by the Mexican government of commercial exploitation. A probable cause for [...]

Published
2007

221. Reproductive cycle of the purple snail Plicopurpura pansa (Gould 1853) from two locations at Baja California Sur, Mexico

Author

Naegel, Ludwig C.A. and Garcia-Dominguez, Federico A.

Subjects
Baja California Sur, Mexico -- Natural resources, Sexual cycle -- Research -- Physiological aspects, Snails -- Physiological aspects -- Natural history -- Research, Biological sciences, Zoology and wildlife conservation, Physiological aspects, Research, Natural resources, Natural history
Abstract

ABSTRACT The reproductive cycle of purple snail, Plicopurpura pansa (Gould 1853), sampled randomly monthly over a period of 20 mo at Playa Cerritos at the Pacific, and Punta Perico at [...]

Published
2006

222. Can the collection of 'Tyrian purple' from Plicopurpura pansa (Gould, 1853) (Prosobranchia, Muricidae) be blamed for its declining population?

Author

Naegel, Ludwig C.A. and Lopez-Rocha, Jorge A.

Subjects
Population biology -- Research -- Physiological aspects, Snails -- Physiological aspects -- Research, Biological sciences, Zoology and wildlife conservation, Physiological aspects, Research
Abstract

ABSTRACT Marked, sized and sex determined purple snails Plicopurpura pansa, (Gould, 1853) were distributed randomly among other snails in crevices of an intertidal rocky island splashed during high tides by [...]

Published
2006

223. The hypobranchial gland from the purple snail Plicopurpura pansa (Gould, 1853) (prosobranchia: Muricidae)

Author

Naegel, Ludwig C.A. and Aguilar-Cruz, Carlos Augusto

Subjects
Mucus -- Physiological aspects, Snails -- Physiological aspects, Biological sciences, Zoology and wildlife conservation, Physiological aspects
Abstract

ABSTRACT Results are presented on the histology of the hypobranchial gland of the marine muricid Plicopurpura pansa (Gould 1853). The general structure and secretory features were investigated using light microscopy [...]

Published
2006

224. Mathematical modelling of the viable epidermis: impact of cell shape and vertical arrangement

Author

Rebecca Wittum, Michael Heisig, Arne Naegel, and Gabriel Wittum

Subjects
genetic structures, Chemistry, General Mathematics, In silico, 02 engineering and technology, 01 natural sciences, 010101 applied mathematics, 020303 mechanical engineering & transports, Epidermis (zoology), 0203 mechanical engineering, Mechanics of Materials, Biophysics, General Materials Science, 0101 mathematics, Cell shape, Function (biology), Barrier function
Abstract

In-silico methods are valuable tools for understanding the barrier function of the skin. The key benefit is that mathematical modelling allows the interplay between cell shape and function to be elucidated. This study focuses on the viable (living) epidermis. For this region, previous works suggested a diffusion model and an approximation of the cells by hexagonal prisms. The work at hand extends this in three ways. First, the extracellular space is treated with full spatial resolution. This induces a decrease of permeability by about 10%. Second, cells of tetrakaidecahedral shape are considered, in addition to the original hexagonal prisms. For both cell types, the resulting membrane permeabilities are compared. Third, for the first time, the influence of cell stacking in the vertical direction is considered. This is particularly important for the stratum granulosum, where tight junctions are present.

Published
2017

228. Biological and chemical properties of the secretion from the hypobranchial gland of the purple snail Plicopurpura pansa (Gould, 1853)

Author

Naegel, Ludwig C.A. and Alvarez, Jesus I. Murillo

Subjects
Glands -- Research -- Study and teaching -- Behavior, Snails -- Study and teaching -- Behavior -- Research, Biological sciences, Zoology and wildlife conservation, Research, Behavior, Study and teaching
Abstract

ABSTRACT The hypobranchial gland of the muricid Plicopurpura pansa (Gould, 1853) is so active that the snails can be stimulated periodically without harming them to expulse the secretion. This property [...]

Published
2005

230. The effect of periodically 'milking' to obtain Tyrian Purple from Plicopurpura pansa (Gould, 1853) on the frequency of expulsion and mortality

Author

Naegel, Ludwig C.A.

Subjects
Marine fauna -- Behavior -- Study and teaching -- Protection and preservation, Snails -- Behavior -- Study and teaching -- Protection and preservation, Biological sciences, Zoology and wildlife conservation, Protection and preservation, Behavior, Study and teaching
Abstract

ABSTRACT Most marine snails of the families Muricidae and Thaididae produce in their hypobranchial gland (mucus gland) a colorless secretion containing minute amounts of chromogens, which develop under the influence [...]

Published
2005

239. No structural brain alterations in new daily persistent headache – a cross sectional VBM/SBM study.

Author

Naegel, Steffen, Zeller, Julia, Hougard, Anders, Weise, Christopher Michael, Zuelow, Stefan, Kleinschnitz, Christoph, Obermann, Mark, Solbach, Kasja, and Holle, Dagny

Subjects
*PRIMARY headache disorders, *HEADACHE, *VOXEL-based morphometry
Abstract

Objective: To identify grey matter alterations in patients suffering new daily persistent headache to enrich the pathophysiological concept of this rare headache disorder characterised by a distinct, clearly remembered onset and its instant chronification. Method: Magnetic resonance-based voxel-based and surface-based morphometry was used to investigate 23 patients suffering from new daily persistent headache and 23 age- and gender-matched healthy controls with 1.5 Tesla MRI. Independent statistical analysis was performed at three sites using statistical parametric mapping, as well as FSL(FMRIB Software Library)-based approaches. Results: No grey matter changes were detected using this sophisticated and cross-checked method. Conclusion: The absence of structural brain changes in patients with new daily persistent headache contribute to the recent discussion regarding structural alterations in primary headache disorders in general and does not provide evidence for grey matter changes being associated with the pathophysiology of new daily persistent headache. Future research will have to determine the underlying pathophysiological mechanisms of this disorder. [ABSTRACT FROM AUTHOR]

Published
2022
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245. Embryonic and intracapsular larval development of Plicopurpura pansa (Gould, 1853) (Prosobranchia, Muricidae) under laboratory conditions

Author

LC A-Naegel and MC Gómez-del-Prado-Rosas

Subjects
gastropods, muricids, Plicopurpura pansa, embryogenesis, intracapsular development, Biology (General), QH301-705.5
Abstract

The embryonic development of the marine muricid Plicopurpura pansa (Gould, 1853) until hatching is described. Each female spawned about 150 egg capsules in a 10-week period. The egg showed spiral segmentation, followed by gastrulation through epiboly, the formation of a non-typical trochophore and veliger larva. The embryos fed on the egg yolk. Nurse eggs were not observed. In 13% of the egg capsules, different embryonic or larval stages of development were found in the same capsule. At 21–23ºC the intracapsular development was completed with the hatching of planktotrophic veliger larvae after a period of 5 to 8 weeks.

Published
2004
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246. Growth of the purple snail Plicopurpura pansa in Baja California Sur, Mexico

Author

M Ramírez-Rodríguez and LCA Naegel

Subjects
purple snail, Plicopurpura pansa, growth, Mexico, Biology (General), QH301-705.5
Abstract

To study the growth of the purple snail Plicopurpura pansa at the northern limit of its distribution, from 1999 to 2001 monthly random samples were obtained from two beaches in the southern part of the Baja California Peninsula (Mexico): one on the Gulf of California and the other on the west coast of the peninsula. Mark and recapture experiments, as well as laboratory rearing were also done. The von Bertalanffy growth equation parameters derived from length frequency data were K = 0.17 and L∞ = 90 mm, and agree with those estimated from the mark and recapture data, but are lower that those reported in the literature for P. pansa from the central and southern Mexican Pacific. Snails maintained under laboratory conditions had low growth rates. These results could be used to implement models for the assessment of purple snail fisheries.

Published
2003
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247. Preventing Chaining in Alpha-Trees Using Gabor Filters

Author

Zhang, Xiaoxuan, Wilkinson, M.H.F., Burgeth, Bernhard, Kleefeld, Andreas, Naegel, Benoit, Passat, Nicolas, Perret, Benjamin, and Intelligent Systems

Subjects
Alpha-tree, Gabor filter, mathematical morphology, image segmentation
Abstract

Hierarchical segmentation using α-trees can suffer from unwanted leakage or chaining effects, which lower segmentation quality by reducing the depth of the hierarchy. In this paper we introduce a new way to prevent the chaining effect of α-trees. It relies on the odd 2-D Gabor filter. A series of clean, noisy, and blurred synthetic images was used to test the ability of improved α-trees to stop the chaining effect obtaining a 99.8% segmentation accuracy, and we compared it with the contrast-based α-tree proposed previously by Soille. Two remote sensing images were also used to test the performance of the methods on natural images. The results showed that both 4-CN and 8-CN odd Gabor filter based α-trees can prevent the chaining effect efficiently.

Published
2019

248. Ensemble Of Neural Networks For High Endothelial Venules Detection In Meca-79 Immunohistochemistry Images

Author

A. Abreu, C. Wemmert, P. Denefle, J.P. Girard P. Denefle, P. Brousset, Benoît Naegel, C. Franchet, and F-X. Frenois

Subjects
0301 basic medicine, Pathology, medicine.medical_specialty, Artificial neural network, High endothelial venules, biochemical phenomena, metabolism, and nutrition, Biology, Ensemble learning, Convolutional neural network, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, 030220 oncology & carcinogenesis, Whole slide image, medicine, Immunohistochemistry, Ensemble strategy, Immunostaining
Abstract

The presence of high endothelial venules (HEVs) in the tumor micro-environment is associated with favorable clinical out-come in breast cancer and other human solid tumors. HEVs can be highlighted in tissue samples by MECA-79 immunohistochemistry. HEVs are heterogeneously scattered in the tumor micro-environment, preferentially in areas infiltrated by lymphocytes, and their density must be assessed at high resolution on whole slide in the tumor area. We present a frame-work for HEV detection through classification of MECA-79 immunostaining in whole slide images. A classifier is provided, as well as a whole slide image metric for general performance evaluation of detectors. An original ensemble strategy for convolutional neural networks has been developed and its relevance in MECA-79 and general immunostaining classification tasks is discussed.

Published
2019

249. nD variational restoration of curvilinear structures with prior-based directional regularization

Author

Odyssée Merveille, Nicolas Passat, Hugues Talbot, Benoît Naegel, Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Organ Modeling through Extraction, Representation and Understanding of Medical Image Content (GALEN-POST), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804 (CRESTIC), Université de Reims Champagne-Ardenne (URCA), PNRIA, ANR-12-MONU-0010,VIVABRAIN,Simulation d'angiographies virtuelles à partir de modèles vasculaires 3D et 3D+t(2012), ANR-18-CE45-0018,R-VESSEL-X,Extraction et interprétation robustes des réseaux vasculaires dans les images biomédicales hépatiques(2018), ANR-18-CE45-0014,HANUMAN,Modèles numériques du système cranio-spinal pour l'homme et l'animal(2018), ANR-15-CE40-0006,CoMeDiC,Métriques convergentes pour le calcul digital(2015), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace (FMNGE), and Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Curvilinear coordinates, Computer science, directional gradient, Computation, Isotropy, segmentation, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Image processing, 02 engineering and technology, Image segmentation, filtering, Computer Graphics and Computer-Aided Design, Regularization (mathematics), Operator (computer programming), variational framework, nD images, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Prior probability, 0202 electrical engineering, electronic engineering, information engineering, proximity operator, 020201 artificial intelligence & image processing, Segmentation, Algorithm, Software, Image restoration
Abstract

International audience; Curvilinear structure restoration in image processing procedures is a difficult task, which can be compounded when these structures are thin, i.e. when their smallest dimension is close to the resolution of the sensor.Many recent restoration methods involve considering a local gradient-based regularization term as prior, assuming gradient sparsity. An isotropic gradient operator is typically not suitable for thin curvilinear structures, since gradients are not sparse for these. In this article, we propose a mixed gradient operator that combines a standard gradient in the isotropic image regions, and a directional gradient in the regions where specific orientations are likely. In particular, such information can be provided by curvilinear structure detectors (e.g. RORPO or Frangi filters). Our proposed mixed gradient operator, that can be viewed as a companion tool of such detectors, is proposed in a discrete framework and its formulation / computation holds in any dimension; in other words, it is valid in $\mathbb Z^n$, $n \geq 1$. We show how this mixed gradient can be used to construct image priors that take edge orientation as well as intensity into account, and then involved in various image processing tasks while preserving curvilinear structures. Experiments carried out on 2D, 3D, real and synthetic images illustrate the relevance of the proposed gradient and its use in variational frameworks for both denoising and segmentation tasks.

Published
2019

250. A Graph Formalism for Time and Memory Efficient Morphological Attribute-Space Connected Filters

Author

Babai, Mohammed, Chowdhury, Ananda S., Wilkinson, Michael H. F., Burget, Bernhard, Kleefeld, Andreas, Naegel, Benoît, Passat, Nicolas, Perret, Benjamin, and Intelligent Systems

Subjects
Clustering high-dimensional data, Spacetime, 010308 nuclear & particles physics, Computer science, Binary image, 02 engineering and technology, Image segmentation, Memory load, 01 natural sciences, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Graph (abstract data type), 020201 artificial intelligence & image processing, Overlapping structures, Algorithm, Curse of dimensionality
Abstract

Attribute-space connectivity has been put forward as a means of improving image segmentation in the case of overlapping structures. Its main drawback is the huge memory load incurred by mapping a N-dimensional image to an \((N + \dim (A))\)-dimensional volume, with \(\dim (A)\) the dimensionality of the attribute vectors used. In this theoretical paper we introduce a more space and time efficient scheme, by representing attribute spaces for analysis of binary images as a graph rather than a volume. Introducing a graph formalism for attribute-space connectivity opens up the possibility of using attribute-space connectivity on 3D volumes or using more than one attribute dimension, without incurring huge memory costs. Furthermore, the graph formalism does not require quantization of the attribute values, as is the case when representing attribute spaces in terms of \((N + \dim (A))\)-dimensional discrete volumes. Efficient processing of high dimensional data produced by multi-sensor detection systems is another advantage of application of our formalism.

Published
2019

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