Nathan Painchaud

Nathan Painchaud

Postdoctoral researcher

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Pages

Posts

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portfolio

publications

Cardiac MRI Segmentation with Strong Anatomical Guarantees

Published in Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019

In this paper, we propose a cardiac MRI segmentation method which always produces anatomically plausible results.

Recommended citation: N. Painchaud, Y. Skandarani, T. Judge, O. Bernard, A. Lalande, and P.-M. Jodoin, "Cardiac MRI Segmentation with Strong Anatomical Guarantees," in proc. Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019, pp. 632–640.
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On the effectiveness of GAN generated cardiac MRIs for segmentation

Published in Medical Imaging with Deep Learning (MIDL), 2020

In this work, we propose a Variational Autoencoder (VAE) - Generative Adversarial Networks (GAN) model that can produce highly realistic MRI together with its pixel accurate groundtruth for the application of cine-MR image cardiac segmentation.

Recommended citation: Y. Skandarani, N. Painchaud, P.-M. Jodoin, and A. Lalande, "On the effectiveness of GAN generated cardiac MRIs for segmentation," presented at Medical Imaging with Deep Learning (MIDL), 2020.
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Cardiac Segmentation with Strong Anatomical Guarantees

Published in IEEE Transactions on Medical Imaging (IEEE TMI), 2020

In this paper, we present a framework for producing cardiac image segmentation maps that are guaranteed to respect pre-defined anatomical criteria, while remaining within the inter-expert variability.

Recommended citation: N. Painchaud, Y. Skandarani, T. Judge, O. Bernard, A. Lalande, and P.-M. Jodoin, "Cardiac Segmentation with Strong Anatomical Guarantees," IEEE Transactions on Medical Imaging, vol. 39, no. 11, pp. 3703–3713, Nov. 2020.
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Echocardiography Segmentation With Enforced Temporal Consistency

Published in IEEE Transactions on Medical Imaging (IEEE TMI), 2022

In this paper, we propose a framework to learn the 2D+time apical long-axis cardiac shape such that the segmented sequences can benefit from temporal and anatomical consistency constraints.

Recommended citation: N. Painchaud, N. Duchateau, O. Bernard, and P.-M. Jodoin, "Echocardiography Segmentation With Enforced Temporal Consistency," IEEE Transactions on Medical Imaging, vol. 41, no. 10, pp. 2867–2878, Oct. 2022.
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Neural Teleportation

Published in Mathematics, 2023

We explore a process called neural teleportation, a mathematical consequence of applying quiver representation theory to neural networks.

Recommended citation: M. Armenta, T. Judge, N. Painchaud, Y. Skandarani, C. Lemaire, G. G. Sanchez, P. Spino, and P.-M. Jodoin, "Neural Teleportation," Mathematics, vol. 11, no. 2, pp. 480, Jan. 2023.
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Extraction of Volumetric Indices from Echocardiography: Which Deep Learning Solution for Clinical Use?

Published in Functional Imaging and Modeling of the Heart (FIMH), 2023

We propose a comprehensive comparison between the current best performing methods in medical/echocardiographic image segmentation, with a particular focus on temporal consistency and cross-dataset aspects.

Recommended citation: H. J. Ling, N. Painchaud, P.-Y. Courand, P.-M. Jodoin, D. Garcia, and O. Bernard, "Extraction of Volumetric Indices from Echocardiography: Which Deep Learning Solution for Clinical Use?," in proc. Functional Imaging and Modeling of the Heart (FIMH), 2023, pp. 245-254.
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talks

teaching

Functional programming

Undergraduate course, Université de Sherbrooke, Aug 2017 - Dec 2017

Formalize the notions of procedural abstraction and data abstraction in the context of functional programming.

Neural networks

Graduate course, Université de Sherbrooke, Jan 2020 - Apr 2020

Be familiar with and understand several types of neural networks. Know how to implement them, train them and analyze their performance. Know how to read, understand, synthesize and present scientific papers on neural networks. Be able to reproduce the results of a scientific article or design a new neural network and evaluate its performance.

Concurrent processes and parallelism

Undergraduate course, Université de Sherbrooke, Jan 2021 - Apr 2021

Become familiar with the concepts of concurrent programming. Learn to solve problems using concurrent programming.

Analysis and programming

Undergraduate course, Université de Sherbrooke, Aug 2018 - Dec 2021

Knowing how to analyze a problem, having high standards for the quality of programs, being able to systematically develop good quality programs in the framework of sequential procedural programming.

Compilers and interpreters

Undergraduate course, Université Laval, Aug 2021 - Dec 2021

General compilation process. Lexical analysis and finite automata. Predictive top-down parsing and context-free grammars. Intermediate representation. Attribute grammars. Typing. Runtime infrastructure. Memory allocation. Code generation. Optimization.

Data structures

Undergraduate course, Université de Sherbrooke, Jan 2018 - Dec 2021

Formalize data structures (stacks, lists, trees, etc.); compare and choose the most suitable implementations of the structures according to the problem at hand; put into practice the notions of module and abstract type.

Deep Learning for Medical Imaging (DLMI)

Spring/summer school, INSA Lyon / École de technologie supérieure (ÉTS Montréal), Jan 2021 - May 2025

  • Since 2025, as Scientific Committee Member: Deciding the program and organizing the school, preparing and delivering 1-2 hours lectures.
  • Since 2021, as Hands-on Manager: Preparing administrative and scientific content (video tutorials, interactive exercises, etc.), organizing and moderating 4-hours hands-on sessions.