Dr. John S. H. Baxter Profile

Dr. John S. H. Baxter

Chargé de Recherche (Assistant Research Professor) at Univ de Rennes 1- INSERM

SPIE Involvement:

Conference Program Committee | Author

Publications (20)

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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SPIE Journal Paper | 28 February 2017

Effects of line fiducial parameters and beamforming on ultrasound calibration

Golafsoun Ameri, John S. Baxter, A. Jonathan McLeod, Terry Peters, Elvis C. Chen

JMI, Vol. 4, Issue 01, 015002, (February 2017) https://doi.org/10.1117/12.10.1117/1.JMI.4.1.015002

KEYWORDS: Calibration, Image segmentation, Ultrasonography, Image resolution, Magnetic tracking, Image registration, Image processing, Error analysis, Image quality, Scanners

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SPIE Journal Paper | 20 December 2016

Shape complexes: the intersection of label orderings and star convexity constraints in continuous max-flow medical image segmentation

John S. Baxter, Jiro Inoue, Maria Drangova, Terry Peters

JMI, Vol. 3, Issue 04, 044005, (December 2016) https://doi.org/10.1117/12.10.1117/1.JMI.3.4.044005

KEYWORDS: Image segmentation, Stars, Blood, Ultrasonography, Medical imaging, Computed tomography, Computing systems, Tissues, Statistical modeling, Optimization (mathematics)

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SPIE Journal Paper | 14 June 2016

Optimization-based interactive segmentation interface for multiregion problems

John S. Baxter, Martin Rajchl, Terry Peters, Elvis C. Chen

JMI, Vol. 3, Issue 02, 024003, (June 2016) https://doi.org/10.1117/12.10.1117/1.JMI.3.2.024003

KEYWORDS: Image segmentation, Human-machine interfaces, Interfaces, Brain, Data modeling, Blood, Medical imaging, Computed tomography, Magnetic resonance imaging, Image processing algorithms and systems

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Proceedings Article | 21 March 2016 Paper

Shape complexes in continuous max-flow segmentation

John S. Baxter, Jing Yuan, Maria Drangova, Terry Peters, Jiro Inoue

Proceedings Volume 9784, 978434 (2016) https://doi.org/10.1117/12.2216258

KEYWORDS: Image segmentation, Computing systems, Computed tomography, Medical imaging, Machine vision, Computer vision technology, Shape analysis, Blood, Optimization (mathematics), MATLAB, Tissues, Analog electronics

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Proceedings Article | 18 March 2016 Paper

Single slice US-MRI registration for neurosurgical MRI-guided US

Utsav Pardasani, John S. Baxter, Terry Peters, Ali Khan

Proceedings Volume 9786, 97862D (2016) https://doi.org/10.1117/12.2214265

KEYWORDS: Image registration, Image fusion, Ultrasonography, Calibration, Magnetic resonance imaging, Magnetism, Optical tracking, Neuroimaging, Particles, Image filtering

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Proceedings Article | 21 April 2015 Paper

Optimization-based interactive segmentation interface for multi-region problems

John S. H. Baxter, Martin Rajchl, Terry Peters, Elvis C. S. Chen

Proceedings Volume 9413, 94133T (2015) https://doi.org/10.1117/12.2081388

KEYWORDS: Image segmentation, Human-machine interfaces, Medical imaging, Blood, Brain, Image processing algorithms and systems, Data modeling, Visualization, Algorithm development, Magnetic resonance imaging

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Proceedings Article | 18 March 2015 Paper

Line fiducial material and thickness considerations for ultrasound calibration

Golafsoun Ameri, A. McLeod, John S. Baxter, Elvis C. Chen, Terry Peters

Proceedings Volume 9415, 941529 (2015) https://doi.org/10.1117/12.2081294

KEYWORDS: Calibration, Image segmentation, Ultrasonography, Error analysis, Image resolution, Image-guided intervention, Synthetic aperture imaging, Image registration, Detection and tracking algorithms, Magnetic tracking

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Ultrasound calibration is a necessary procedure in many image-guided interventions, relating the position of tools and anatomical structures in the ultrasound image to a common coordinate system. This is a necessary component of augmented reality environments in image-guided interventions as it allows for a 3D visualization where other surgical tools outside the imaging plane can be found. Accuracy of ultrasound calibration fundamentally affects the total accuracy of this interventional guidance system. Many ultrasound calibration procedures have been proposed based on a variety of phantom materials and geometries. These differences lead to differences in representation of the phantom on the ultrasound image which subsequently affect the ability to accurately and automatically segment the phantom. For example, taut wires are commonly used as line fiducials in ultrasound calibration. However, at large depths or oblique angles, the fiducials appear blurred and smeared in ultrasound images making it hard to localize their cross-section with the ultrasound image plane. Intuitively, larger diameter phantoms with lower echogenicity are more accurately segmented in ultrasound images in comparison to highly reflective thin phantoms. In this work, an evaluation of a variety of calibration phantoms with different geometrical and material properties for the phantomless calibration procedure was performed. The phantoms used in this study include braided wire, plastic straws, and polyvinyl alcohol cryogel tubes with different diameters. Conventional B-mode and synthetic aperture images of the phantoms at different positions were obtained. The phantoms were automatically segmented from the ultrasound images using an ellipse fitting algorithm, the centroid of which is subsequently used as a fiducial for calibration. Calibration accuracy was evaluated for these procedures based on the leave-one-out target registration error. It was shown that larger diameter phantoms with lower echogenicity are more accurately segmented in comparison to highly reflective thin phantoms. This improvement in segmentation accuracy leads to a lower fiducial localization error, which ultimately results in low target registration error. This would have a profound effect on calibration procedures and the feasibility of different calibration procedures in the context of image-guided procedures.

Proceedings Article | 21 March 2014 Paper

Smoothness parameter tuning for generalized hierarchical continuous max-flow segmentation

John S. Baxter, Martin Rajchl, A. Jonathan McLeod, Ali Khan, Jing Yuan, Terry Peters

Proceedings Volume 9034, 903410 (2014) https://doi.org/10.1117/12.2043192

KEYWORDS: Image segmentation, Gold, Error analysis, Medical imaging, Brain, Visualization, Iterative methods, Databases, Image processing algorithms and systems, Optimization (mathematics)

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Proceedings Article | 12 March 2014 Paper

Motion magnification for endoscopic surgery

A. Jonathan McLeod, John S. Baxter, Sandrine de Ribaupierre, Terry Peters

Proceedings Volume 9036, 90360C (2014) https://doi.org/10.1117/12.2043997

KEYWORDS: Video, Endoscopy, Surgery, Arteries, Robotics, Linear filtering, Visualization, Electronic filtering, Imaging systems, Tissues

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Proceedings Article | 12 March 2014 Paper

Synthetic aperture imaging in ultrasound calibration

Golafsoun Ameri, John S. Baxter, A. Jonathan McLeod, Uditha Jayaranthe, Elvis C. Chen, Terry Peters

Proceedings Volume 9036, 90361I (2014) https://doi.org/10.1117/12.2043899

KEYWORDS: Calibration, Ultrasonography, Image segmentation, Image resolution, Synthetic aperture imaging, Magnetic tracking, Image registration, Electroluminescent displays, Calcium, Image processing

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Proceedings Article | 22 February 2012 Paper

Perceptual enhancement of arteriovenous malformation in MRI angiography displays

Kamyar Abhari, John S. Baxter, Roy Eagleson, Terry Peters, Sandrine de Ribaupierre

Proceedings Volume 8318, 831809 (2012) https://doi.org/10.1117/12.911687

KEYWORDS: Visualization, Angiography, Blood vessels, Opacity, Volume rendering, Magnetic resonance imaging, Veins, Tissues, Surgery, Brain

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Proceedings Article | 17 February 2012 Paper

Characterization of tissue-simulating phantom materials for ultrasound-guided needle procedures

Susan Buchanan, John Moore, Deanna Lammers, John Baxter, Terry Peters

Proceedings Volume 8316, 83162B (2012) https://doi.org/10.1117/12.911432

KEYWORDS: Tissues, Ultrasonography, Therapeutics, Liquids, Skin, Distance measurement, Biopsy, Computed tomography, Medical imaging, Image segmentation

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Proceedings Article | 17 February 2012 Paper

An augmented reality platform for planning of minimally invasive cardiac surgeries

Elvis C. Chen, Kripasindhu Sarkar, John Baxter, John Moore, Chris Wedlake, Terry Peters

Proceedings Volume 8316, 831617 (2012) https://doi.org/10.1117/12.911998

KEYWORDS: Cameras, Autoregressive models, Surgery, Glasses, Calibration, Optical tracking, Imaging systems, Distortion, Video, Optical calibration

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Proceedings Article | 2 March 2011 Paper

A unified framework for voxel classification and triangulation

John S. Baxter, Terry Peters, Elvis C. Chen

Proceedings Volume 7964, 796436 (2011) https://doi.org/10.1117/12.877715

KEYWORDS: Visualization, Data modeling, Binary data, Optical spheres, Volume rendering, Brain, Magnetic resonance imaging, Medical imaging, Visual process modeling, Image classification

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Showing 5 of 20 publications

Conference Committee Involvement (2)

Image-Guided Procedures, Robotic Interventions, and Modeling

17 February 2025 | San Diego, California, United States

Image-Guided Procedures, Robotic Interventions, and Modeling

19 February 2024 | San Diego, California, United States

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