Dr. Pierre Jannin Profile

Dr. Pierre Jannin

INSERM Research Director at Univ de Rennes

SPIE Involvement:

Conference Program Committee | Author | Instructor

Publications (19)

Proceedings Article | 30 March 2024 Presentation

Effect of the prior distribution on a Bayesian model or errors of type for transcranial magnetic stimulation

John S. Baxter, Pierre Jannin

Proceedings Volume 12928, 129280P (2024) https://doi.org/10.1117/12.3008324

KEYWORDS: Magnetism, Data modeling, Machine learning, Mathematical modeling, Error analysis, Detection and tracking algorithms, Computer simulations, Computation time, Analytics

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SPIE Journal Paper | 11 July 2022

Combining simple interactivity and machine learning: a separable deep learning approach to subthalamic nucleus localization and segmentation in MRI for deep brain stimulation surgical planning

John S. Baxter, Pierre Jannin

JMI, Vol. 9, Issue 04, 045001, (July 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.4.045001

KEYWORDS: Image segmentation, Machine learning, Convolution, Image registration, Magnetic resonance imaging, Electrodes, Human-computer interaction, Brain stimulation, Neurons, Network architectures

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Purpose: Deep brain stimulation (DBS) is an interventional treatment for some neurological and neurodegenerative diseases. For example, in Parkinson’s disease, DBS electrodes are positioned at particular locations within the basal ganglia to alleviate the patient’s motor symptoms. These interventions depend greatly on a preoperative planning stage in which potential targets and electrode trajectories are identified in a preoperative MRI. Due to the small size and low contrast of targets such as the subthalamic nucleus (STN), their segmentation is a difficult task. Machine learning provides a potential avenue for development, but it has difficulty in segmenting such small structures in volumetric images due to additional problems such as segmentation class imbalance. Approach: We present a two-stage separable learning workflow for STN segmentation consisting of a localization step that detects the STN and crops the image to a small region and a segmentation step that delineates the structure within that region. The goal of this decoupling is to improve accuracy and efficiency and to provide an intermediate representation that can be easily corrected by a clinical user. This correction capability was then studied through a human–computer interaction experiment with seven novice participants and one expert neurosurgeon. Results: Our two-step segmentation significantly outperforms the comparative registration-based method currently used in clinic and approaches the fundamental limit on variability due to the image resolution. In addition, the human–computer interaction experiment shows that the additional interaction mechanism allowed by separating STN segmentation into two steps significantly improves the users’ ability to correct errors and further improves performance. Conclusions: Our method shows that separable learning not only is feasible for fully automatic STN segmentation but also leads to improved interactivity that can ease its translation into clinical use.

Proceedings Article | 4 April 2022 Presentation + Paper

Errors of type or errors of degree? Cortical point targeting in transcranial magnetic stimulation

John S. Baxter, Stéphane Croci, Antoine Delmas, Luc Bredoux, Jean-Pascal Lefaucheur, Pierre Jannin

Proceedings Volume 12034, 1203403 (2022) https://doi.org/10.1117/12.2605797

KEYWORDS: Computer simulations, Magnetic resonance imaging, Data modeling, Probability theory, Machine learning, 3D image processing, Neuroimaging, Human-computer interaction

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Segmentation of the subthalamic nucleus in MRI via Convolutional Neural Networks for deep brain stimulation planning

John S. Baxter, Ehouarn Maguet, Pierre Jannin

Proceedings Volume 11598, 115981K (2021) https://doi.org/10.1117/12.2579565

KEYWORDS: Magnetic resonance imaging, Brain stimulation, Convolutional neural networks, Image segmentation, Parkinson's disease, Image registration, Image processing, Electrodes, Basal ganglia

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Proceedings Article | 16 March 2020 Presentation + Paper

Localisation of the subthalamic nucleus in MRI via convolutional neural networks for deep brain stimulation planning

John S. Baxter, Ehouarn Maguet, Pierre Jannin

Proceedings Volume 11315, 113150M (2020) https://doi.org/10.1117/12.2548420

KEYWORDS: Convolutional neural networks, Magnetic resonance imaging, Image registration, Brain stimulation, Image resolution, Target detection

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Proceedings Article | 10 March 2020 Presentation + Paper

Topology-aware activation layer for neural network image segmentation

John S. Baxter, Pierre Jannin

Proceedings Volume 11313, 113131A (2020) https://doi.org/10.1117/12.2548303

KEYWORDS: Image segmentation, Neural networks, Medical imaging, Algorithm development, Network architectures, Convolution, Image processing, Image processing algorithms and systems

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Proceedings Article | 3 March 2017 Paper

Real-time phase recognition in novel needle-based intervention: a multi-operator feasibility study

Sébastien Muller, Fabien Despinoy, Daniel Bratbak, Erling Tronvik, Pierre Jannin

Proceedings Volume 10135, 101350E (2017) https://doi.org/10.1117/12.2253619

KEYWORDS: Navigation systems, Surgery, Data modeling, Machine learning, Classification systems, Image classification, Image-guided intervention, Visualization, Magnetic resonance imaging, Computed tomography, Skin, Spiral phase plates, Optical tracking, Medical imaging

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SPIE Journal Paper | 30 June 2016

Image-guided preoperative prediction of pyramidal tract side effect in deep brain stimulation: proof of concept and application to the pyramidal tract side effect induced by pallidal stimulation

Clement Baumgarten, Yulong Zhao, Paul Sauleau, Cecile Malrain, Pierre Jannin, Claire Haegelen

JMI, Vol. 3, Issue 02, 025001, (June 2016) https://doi.org/10.1117/12.10.1117/1.JMI.3.2.025001

KEYWORDS: Neodymium, Electrodes, 3D modeling, Surgery, Brain stimulation, Magnetic resonance imaging, Tissues, Artificial neural networks, Image segmentation, Data modeling

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Proceedings Article | 18 March 2016 Paper

Partition-based acquisition model for speed up navigated beta-probe surface imaging

Frédéric Monge, Dzhoshkun Shakir, Nassir Navab, Pierre Jannin

Proceedings Volume 9786, 97862P (2016) https://doi.org/10.1117/12.2216231

KEYWORDS: Phase modulation, Tumors, Navigation systems, Signal attenuation, Data modeling, Performance modeling, Tissues, Imaging systems, Bioluminescence, Spatial resolution

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Proceedings Article | 18 March 2016 Paper

Image-guided preoperative prediction of pyramidal tract side effect in deep brain stimulation

C. Baumgarten, Y. Zhao, P. Sauleau, C. Malrain, P. Jannin, C. Haegelen

Proceedings Volume 9786, 97860U (2016) https://doi.org/10.1117/12.2216314

KEYWORDS: Brain stimulation, 3D modeling, Artificial neural networks, Surgery, Electrodes, Computational modeling, Computer simulations, Image-guided intervention, Machine learning, Neodymium, Neural networks, Magnetic resonance imaging, Parkinson's disease, Tissues, Image registration

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Proceedings Article | 17 February 2012 Paper

Analysis of electrodes' placement and deformation in deep brain stimulation from medical images

Maroua Mehri, Florent Lalys, Camille Maumet, Claire Haegelen, Pierre Jannin

Proceedings Volume 8316, 831632 (2012) https://doi.org/10.1117/12.912225

KEYWORDS: Electrodes, Brain, Image segmentation, Algorithm development, Computed tomography, Brain stimulation, Medical imaging, Parkinson's disease, Magnetic resonance imaging, Evolutionary algorithms

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Proceedings Article | 13 March 2009 Paper

Post-operative assessment in Deep Brain Stimulation based on multimodal images: registration workflow and validation

Florent Lalys, Claire Haegelen, Alexandre Abadie, Pierre Jannin

Proceedings Volume 7261, 72612M (2009) https://doi.org/10.1117/12.810475

KEYWORDS: Image registration, Magnetic resonance imaging, Electrodes, Brain, Visualization, Parkinson's disease, Brain stimulation, Computed tomography, Super resolution, Surgery

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Proceedings Article | 31 March 2008 Paper

Preoperative brain shift: study of three surgical cases

O. El Ganaoui, X. Morandi, S. Duchesne, P. Jannin

Proceedings Volume 6918, 691818 (2008) https://doi.org/10.1117/12.772634

KEYWORDS: Tumors, Brain, Image segmentation, Surface plasmons, Image registration, Neuroimaging, Brain mapping, Visualization, Imaging systems, Tissues

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In successful brain tumor surgery, the neurosurgeon's objectives are threefold: (1) reach the target, (2) remove it and (3) preserve eloquent tissue surrounding it. Surgical Planning (SP) consists in identifying optimal access route(s) to the target based on anatomical references and constrained by functional areas. Preoperative images are essential input in Multi-modal Image Guided NeuroSurgery systems (MIGNS) and update of these images, with precision and accuracy, is crucial to approach the anatomical reality in the Operating Room (OR). Intraoperative brain deformation has been previously identified by many research groups and related update of preoperative images has also been studied. We present a study of three surgical cases with tumors accompanied with edema and where corticosteroids were administered and monitored during a preoperative stage [t₀, t₁ = t₀ + 10 days]. In each case we observed a significant change in the Region Of Interest (ROI) and in anatomical references around it. This preoperative brain shift could induce error for localization during intervention (time t_S) if the SP is based on the t₀ preoperative images. We computed volume variation, distance maps based on closest point (CP) for different components of the ROI, and displacement of center of mass (CM) of the ROI. The matching between sets of homologous landmarks from t₀ to t₁ was performed by an expert. The estimation of the landmarks displacement showed significant deformations around the ROI (landmarks shifted with mean of 3.90 ± 0.92 mm and maximum of 5.45 mm for one case resection). The CM of the ROI moved about 6.92 mm for one biopsy. Accordingly, there was a sizable difference between SP based at t₀ vs SP based at t₁, up to 7.95 mm for localization of reference access in one resection case. When compared to the typical MIGNS system accuracy (2 mm), it is recommended that preoperative images be updated within the interval time [t₁,t_S] in order to minimize the error correspondence between the anatomical reality and the preoperative data. This should help maximize the accuracy of registration between the preoperative images and the patient in the OR.

Proceedings Article | 16 March 2006 Paper

Structured recording of intraoperative surgical workflows

T. Neumuth, N. Durstewitz, M. Fischer, G. Strauss, A. Dietz, J. Meixensberger, P. Jannin, K. Cleary, H. Lemke, O. Burgert

Proceedings Volume 6145, 61450A (2006) https://doi.org/10.1117/12.653462

KEYWORDS: Surgery, Process modeling, Computer architecture, Data acquisition, Visualization, Image-guided intervention, Databases, Imaging systems, Control systems, Endoscopes

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Proceedings Article | 13 March 2006 Paper

Knowledge modeling in image-guided neurosurgery: application in understanding intraoperative brain shift

Julien Cohen-Adad, Perrine Paul, Xavier Morandi, Pierre Jannin

Proceedings Volume 6141, 61412E (2006) https://doi.org/10.1117/12.655752

KEYWORDS: Brain, Databases, Neuroimaging, Image analysis, Blood, Data modeling, Data mining, Surgery, Protactinium, Systems modeling

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Proceedings Article | 15 April 2005 Paper

Toward models of surgical procedures: analyzing a database of neurosurgical cases

Melanie Raimbault, Xavier Morandi, Pierre Jannin

Proceedings Volume 5748, (2005) https://doi.org/10.1117/12.594053

KEYWORDS: Tumors, Surgery, Databases, Data modeling, Image-guided intervention, Systems modeling, Performance modeling, Skin, Medicine, Brain

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Proceedings Article | 7 May 1997 Paper

Visual matching between real and virtual images in image-guided neurosurgery

Pierre Jannin, Alain Bouliou, Jean-Marie Scarabin, Christian Barillot, J. Luber

Proceedings Volume 3031, (1997) https://doi.org/10.1117/12.273931

KEYWORDS: Visualization, Image fusion, 3D image processing, Data fusion, Surgery, Microscopes, 3D displays, Virtual reality, Navigation systems, Image segmentation

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Proceedings Article | 1 May 1994 Paper

Three-dimensional approach for the simulation of neurosurgical stereotactic act

Pierre Jannin, Jean-Marie Scarabin, D. Schwartz

Proceedings Volume 2164, (1994) https://doi.org/10.1117/12.173997

KEYWORDS: 3D displays, 3D image processing, Computer simulations, Computed tomography, 3D acquisition, Image segmentation, Imaging systems, Human-machine interfaces, Medical imaging, Brain

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Proceedings Article | 1 May 1992 Paper

Design of user interface in medical imaging: lessons of 3-D application definition

Pierre Jannin, G. Mevel, Yves Gandon, Emmanuel Cordonnier

Proceedings Volume 1653, (1992) https://doi.org/10.1117/12.59519

KEYWORDS: Human-machine interfaces, Medical imaging, 3D displays, Visualization, 3D image processing, Computing systems, Image visualization, Pollution control, Image processing, Product engineering

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Showing 5 of 19 publications

Conference Committee Involvement (21)