Élodie Puybareau

A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging

By Zhaohan Xiong, Qing Xia, Zhiqiang Hu, Ning Huang, Cheng Bian, Yefeng Zheng, Sulaiman Vesal, Nishant Ravikumar, Andreas Maier, Xin Yang, Pheng-Ann Heng, Dong Ni, Caizi Li, Qianqian Tong, Weixin Si, Élodie Puybareau, Younes Khoudli, Thierry Géraud, Chen Chen, Wenjia Bai, Daniel Rueckert, Lingchao Xu, Xiahai Zhuang, Xinzhe Luo, Shuman Jia, Maxime Sermesant, Yashu Liu, Kuanquan Wang, Davide Borra, Alessandro Masci, Cristiana Corsi, Coen Vente, Mitko Veta, Rashed Karim, Chandrakanth Jayachandran Preetha, Sandy Engelhardt, Menyun Qiao, Yuanyuan Wang, Qian Tao, Marta Nunez-Garcia, Oscar Camara, Nicolo Savioli, Pablo Lamata, Jichao Zhao

2020-11-10

In Medical Image Analysis

Abstract Segmentation of medical images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) used for visualizing diseased atrial structures, is a crucial first step for ablation treatment of atrial fibrillation. However, direct segmentation of LGE-MRIs is challenging due to the varying intensities caused by contrast agents. Since most clinical studies have relied on manual, labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning approaches. To address this, we organized the 2018 Left Atrium Segmentation Challenge using 154 3D LGE-MRIs, currently the world’s largest atrial LGE-MRI dataset, and associated labels of the left atrium segmented by three medical experts, ultimately attracting the participation of 27 international teams.

PAIP 2019: Liver cancer segmentation challenge

By Yoo Jung Kim, Hyungjoon Jang, Kyoungbun Lee, Seongkeun Park, Sung-Gyu Min, Choyeon Hong, Jeong Hwan Park, Kanggeun Lee, Jisoo Kim, Wonjae Hong, Hyun Jung, Yanling Liu, Haran Rajkumar, Mahendra Khened, Ganapathy Krishnamurthi, Sen Yang, Xiyue Wang, Chang Hee Han, Jin Tae Kwak, Jianqiang Ma, Zhe Tang, Bahram Marami, Jack Zeineh, Zixu Zhao, Pheng-Ann Heng, Rudiger Schmitz, Frederic Madesta, Thomas Rosch, Rene Werner, Jie Tian, Élodie Puybareau, Matteo Bovio, Xiufeng Zhang, Yifeng Zhu, Se Young Chun, Won-Ki Jeong, Peom Park, Jinwook Choi

2020-11-10

In Medical Image Analysis

Abstract Pathology Artificial Intelligence Platform (PAIP) is a free research platform in support of pathological artificial intelligence (AI). The main goal of the platform is to construct a high-quality pathology learning data set that will allow greater accessibility. The PAIP Liver Cancer Segmentation Challenge, organized in conjunction with the Medical Image Computing and Computer Assisted Intervention Society (MICCAI 2019), is the first image analysis challenge to apply PAIP datasets. The goal of the challenge was to evaluate new and existing algorithms for automated detection of liver cancer in whole-slide images (WSIs).

Do not treat boundaries and regions differently: An example on heart left atrial segmentation

By Zhou Zhao, Nicolas Boutry, Élodie Puybareau, Thierry Géraud

2020-11-02

In Proceedings of the 25th international conference on pattern recognition (ICPR)

Abstract Atrial fibrillation is the most common heart rhythm disease. Due to a lack of understanding in matter of underlying atrial structures, current treatments are still not satisfying. Recently, with the popularity of deep learning, many segmentation methods based on fully convolutional networks have been proposed to analyze atrial structures, especially from late gadolinium-enhanced magnetic resonance imaging. However, two problems still occur: 1) segmentation results include the atrial- like background; 2) boundaries are very hard to segment.

FOANet: A focus of attention network with application to myocardium segmentation

By Zhou Zhao, Nicolas Boutry, Élodie Puybareau, Thierry Géraud

2020-11-02

In Proceedings of the 25th international conference on pattern recognition (ICPR)

Abstract In myocardium segmentation of cardiac magnetic resonance images, ambiguities often appear near the boundaries of the target domains due to tissue similarities. To address this issue, we propose a new architecture, called FOANet, which can be decomposed in three main steps: a localization step, a Gaussian-based contrast enhancement step, and a segmentation step. This architecture is supplied with a hybrid loss function that guides the FOANet to study the transformation relationship between the input image and the corresponding label in a three-level hierarchy (pixel-, patch- and map-level), which is helpful to improve segmentation and recovery of the boundaries.

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.

A two-stage temporal-like fully convolutional network framework for left ventricle segmentation and quantification on MR images

By Zhou Zhao, Nicolas Boutry, Élodie Puybareau, Thierry Géraud

2020-02-07

In Statistical atlases and computational models of the heart. Multi-sequence CMR segmentation, CRT-EPiggy and LV full quantification challenges—10th international workshop, STACOM 2019, held in conjunction with MICCAI 2019, shenzhen, china, october 13, 2019, revised selected papers

Abstract Automatic segmentation of the left ventricle (LV) of a living human heart in a magnetic resonance (MR) image (2D+t) allows to measure some clinical significant indices like the regional wall thicknesses (RWT), cavity dimensions, cavity and myocardium areas, and cardiac phase. Here, we propose a novel framework made of a sequence of two fully convolutional networks (FCN). The first is a modified temporal-like VGG16 (the “localization network”) and is used to localize roughly the LV (filled-in) epicardium position in each MR volume.

Benchmark on automatic 6-month-old infant brain segmentation algorithms: The iSeg-2017 challenge

By Li Wang, Dong Nie, Guannan Li, Élodie Puybareau, Jose Dolz, Qian Zhang, Fan Wang, Jing Xia, Zhengwang Wu, Jiawei Chen, Kim-Han Thung, Toan Duc Bui, Jitae Shin, Guodong Zeng, Guoyan Zheng, Vladimir S. Fonov, Andrew Doyle, Yongchao Xu, Pim Moeskops, Josien P. W. Pluim, Christian Desrosiers, Ismail Ben Ayed, Gerard Sanroma, Oualid M. Benkarim, Adrià Casamitjana, Verónica Vilaplana, Weili Lin, Gang Li, Dinggang Shen

2019-04-11

In IEEE Transactions on Medical Imaging

Abstract Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6-9 months of age), due to inherent myelination and maturation process, WM and GM exhibit similar levels of intensity in both T1-weighted (T1w) and T2-weighted (T2w) MR images, making tissue segmentation very challenging.

Standardized assessment of automatic segmentation of white matter hyperintensities: Results of the WMH segmentation challenge

By H. J. Kuijf, J. M. Biesbroek, J. de Bresser, R. Heinen, S. Andermatt, M. Bento, M. Berseth, M. Belyaev, M. J. Cardoso, A. Casamitjana, D. L. Collins, M. Dadar, A. Georgiou, M. Ghafoorian, D. Jin, A. Khademi, J. Knight, H. Li, X. Lladó, M. Luna, Q. Mahmood, R. McKinley, A. Mehrtash, S. Ourselin, B. Park, H. Park, S. H. Park, S. Pezold, Élodie Puybareau, L. Rittner, C. H. Sudre, S. Valverde, V. Vilaplana, R. Wiest, Yongchao Xu, Z. Xu, G. Zeng, J. Zhang, G. Zheng, C. Chen, W. van der Flier, F. Barkhof, M. A. Viergever, G. J. Biessels

2019-04-10

In IEEE Transactions on Medical Imaging

Abstract Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. Automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their method on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge (https://wmh.

Spherical fluorescent particle segmentation and tracking in 3D confocal microscopy

By Élodie Puybareau, Edwin Carlinet, Alessandro Benfenati, Hugues Talbot

2019-03-13

In Mathematical morphology and its application to signal and image processing – proceedings of the 14th international symposium on mathematical morphology (ISMM)

Abstract Spherical fluorescent particle are micrometer-scale spherical beads used in various areas of physics, chemistry or biology as markers associated with local physical media. They are useful for example in fluid dynamics to characterize flows, diffusion coefficients, viscosity or temperature; they are used in cells dynamics to estimate mechanical strain and stress at the micrometer scale. In order to estimate these physical measurements, tracking these particles is necessary. Numerous approaches and existing packages, both open-source and proprietary are available to achieve tracking with a high degree of precision in 2D.

A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging

PAIP 2019: Liver cancer segmentation challenge

Do not treat boundaries and regions differently: An example on heart left atrial segmentation

FOANet: A focus of attention network with application to myocardium segmentation

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

A two-stage temporal-like fully convolutional network framework for left ventricle segmentation and quantification on MR images

Benchmark on automatic 6-month-old infant brain segmentation algorithms: The iSeg-2017 challenge

Standardized assessment of automatic segmentation of white matter hyperintensities: Results of the WMH segmentation challenge

Spherical fluorescent particle segmentation and tracking in 3D confocal microscopy

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