Guillaume Tochon

Estimation of the noise level function for color images using mathematical morphology and non-parametric statistics

By Baptiste Esteban, Guillaume Tochon, Edwin Carlinet, Didier Verna

2022-04-08

In Proceedings of the 26th international conference on pattern recognition

Abstract Noise level information is crucial for many image processing tasks, such as image denoising. To estimate it, it is necessary to find homegeneous areas within the image which contain only noise. Rank-based methods have proven to be efficient to achieve such a task. In the past, we proposed a method to estimate the noise level function (NLF) of grayscale images using the tree of shapes (ToS). This method, relying on the connected components extracted from the ToS computed on the noisy image, had the advantage of being adapted to the image content, which is not the case when using square blocks, but is still restricted to grayscale images.

Learning grayscale mathematical morphology with smooth morphological layers

By Romain Hermary, Guillaume Tochon, Élodie Puybareau, Alexandre Kirszenberg, Jesús Angulo

2022-04-04

In Journal of Mathematical Imaging and Vision

Abstract The integration of mathematical morphology operations within convolutional neural network architectures has received an increasing attention lately. However, replacing standard convolution layers by morphological layers performing erosions or dilations is particularly challenging because the min and max operations are not differentiable. P-convolution layers were proposed as a possible solution to this issue since they can act as smooth differentiable approximation of min and max operations, yielding pseudo-dilation or pseudo-erosion layers.

QU-BraTS: MICCAI BraTS 2020 challenge on quantifying uncertainty in brain tumor segmentation — Analysis of ranking scores and benchmarking results

By Raghav Mehta, Angelos Filos, Ujjwal Baid, Chiharu Sako, Richard McKinley, Michael Rebsamen, Katrin Dätwyler, Raphael Meier, Piotr Radojewski, Gowtham Krishnan Murugesan, Sahil Nalawade, Chandan Ganesh, Ben Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian, Laura Daza, Catalina Gómez, Pablo Arbeláez, Chengliang Dai, Shuo Wang, Hadrien Reynaud, Yuanhan Mo, Elsa Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Linmin Pei, Murat AK, Sarahi Rosas-González, Ilyess Zemmoura, Clovis Tauber, Minh Hoang Vu, Tufve Nyholm, Tommy Löfstedt, Laura Mora Ballestar, Veronica Vilaplana, Hugh McHugh, Gonzalo Maso Talou, Alan Wang, Jay Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer, Nicolas Boutry, Alexis Huard, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin, Joseph Chazalon, Élodie Puybareau, Guillaume Tochon, Jun Ma, Mariano Cabezas, Xavier Llado, Arnau Oliver, Liliana Valencia, Sergi Valverde, Mehdi Amian, Mohammadreza Soltaninejad, Andriy Myronenko, Ali Hatamizadeh, Xue Feng, Quan Dou, Nicholas Tustison, Craig Meyer, Nisarg A. Shah, Sanjay Talbar, Marc-André Weber, Abhishek Mahajan, Andras Jakab, Roland Wiest, Hassan M. Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko, Daniel Marcus, Aikaterini Kotrotsou, Rivka Colen, John Freymann, Justin Kirby, Christos Davatzikos, Bjoern Menze, Spyridon Bakas, Yarin Gal, Tal Arbel

2022-01-09

In Journal of Machine Learning for Biomedical Imaging (MELBA)

Abstract Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation.

Learning Sentinel-2 spectral dynamics for long-run predictions using residual neural networks

By Joaquim Estopinan, Guillaume Tochon, Lucas Drumetz

2021-05-04

In Proceedings of the 29th european signal processing conference (EUSIPCO)

Abstract Making the most of multispectral image time-series is a promising but still relatively under-explored research direction because of the complexity of jointly analyzing spatial, spectral and temporal information. Capturing and characterizing temporal dynamics is one of the important and challenging issues. Our new method paves the way to capture real data dynamics and should eventually benefit applications like unmixing or classification. Dealing with time-series dynamics classically requires the knowledge of a dynamical model and an observation model.

Stability of the tree of shapes to additive noise

By Nicolas Boutry, Guillaume Tochon

2021-03-02

In Proceedings of the IAPR international conference on discrete geometry and mathematical morphology (DGMM)

Abstract The tree of shapes (ToS) is a famous self-dual hierarchical structure in mathematical morphology, which represents the inclusion relationship of the shapes (i.e. the interior of the level lines with holes filled) in a grayscale image. The ToS has already found numerous applications in image processing tasks, such as grain filtering, contour extraction, image simplification, and so on. Its structure consistency is bound to the cleanliness of the level lines, which are themselves deeply affected by the presence of noise within the image.

Going beyond p-convolutions to learn grayscale morphological operators

By Alexandre Kirszenberg, Guillaume Tochon, Élodie Puybareau, Jesus Angulo

2021-02-16

In Proceedings of the IAPR international conference on discrete geometry and mathematical morphology (DGMM)

Abstract Integrating mathematical morphology operations within deep neural networks has been subject to increasing attention lately. However, replacing standard convolution layers with erosions or dilations is particularly challenging because the min and max operations are not differentiable. Relying on the asymptotic behavior of the counter-harmonic mean, p-convolutional layers were proposed as a possible workaround to this issue since they can perform pseudo-dilation or pseudo-erosion operations (depending on the value of their inner parameter p), and very promising results were reported.

On some associations between mathematical morphology and artificial intelligence

By Isabelle Bloch, Samy Blusseau, Ramón Pino Pérez, Élodie Puybareau, Guillaume Tochon

2021-02-16

In Proceedings of the IAPR international conference on discrete geometry and mathematical morphology (DGMM)

Abstract This paper aims at providing an overview of the use of mathematical morphology, in its algebraic setting, in several fields of artificial intelligence (AI). Three domains of AI will be covered. In the first domain, mathematical morphology operators will be expressed in some logics (propositional, modal, description logics) to answer typical questions in knowledge representation and reasoning, such as revision, fusion, explanatory relations, satisfying usual postulates. In the second domain, spatial reasoning will benefit from spatial relations modeled using fuzzy sets and morphological operators, with applications in model-based image understanding.

Two stages CNN-based segmentation of gliomas, uncertainty quantification and prediction of overall patient survival

By Thibault Buatois, Élodie Puybareau, Guillaume Tochon, Joseph Chazalon

2020-09-03

In International MICCAI brainlesion workshop

Abstract

Using separated inputs for multimodal brain tumor segmentation with 3D U-Net-like architectures

By Nicolas Boutry, Joseph Chazalon, Élodie Puybareau, Guillaume Tochon, Hugues Talbot, Thierry Géraud

2020-06-01

In Proceedings of the 4th international workshop, BrainLes 2019, held in conjunction with MICCAI 2019

Abstract The work presented in this paper addresses the MICCAI BraTS 2019 challenge devoted to brain tumor segmentation using mag- netic resonance images. For each task of the challenge, we proposed and submitted for evaluation an original method. For the tumor segmentation task (Task 1), our convolutional neural network is based on a variant of the U-Net architecture of Ronneberger et al. with two modifications: first, we separate the four convolution parts to decorrelate the weights corresponding to each modality, and second, we provide volumes of size 240 * 240 * 3 as inputs in these convolution parts.

Experimental digital gabor hologram rendering of C. Elegans worms by a model-trained convolutional neural network (conference presentation)

By Michael Atlan, Julie Rivet, Antoine Taliercio, Nicolas Boutry, Guillaume Tochon, Jean-Pierre Huignard

2020-04-01

In Label-free biomedical imaging and sensing (LBIS) 2020

Abstract

Estimation of the noise level function for color images using mathematical morphology and non-parametric statistics

Learning grayscale mathematical morphology with smooth morphological layers

QU-BraTS: MICCAI BraTS 2020 challenge on quantifying uncertainty in brain tumor segmentation — Analysis of ranking scores and benchmarking results

Learning Sentinel-2 spectral dynamics for long-run predictions using residual neural networks

Stability of the tree of shapes to additive noise

Going beyond p-convolutions to learn grayscale morphological operators

On some associations between mathematical morphology and artificial intelligence

Two stages CNN-based segmentation of gliomas, uncertainty quantification and prediction of overall patient survival

Using separated inputs for multimodal brain tumor segmentation with 3D U-Net-like architectures

Experimental digital gabor hologram rendering of C. Elegans worms by a model-trained convolutional neural network (conference presentation)

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