Zhou Zhao

Multi-purpose tactile perception based on deep learning in a new tendon-driven optical tactile sensor

2022-10-01

In 2022 IEEE/RSJ international conference on intelligent robots and systems

Abstract In this paper, we create a new tendon-connected multi-functional optical tactile sensor, MechTac, for object perception in field of view (TacTip) and location of touching points in the blind area of vision (TacSide). In a multi-point touch task, the information of the TacSide and the TacTip are overlapped to commonly affect the distribution of papillae pins on the TacTip. Since the effects of TacSide are much less obvious to those affected on the TacTip, a perceiving out-of-view neural network (O$^2$VNet) is created to separate the mixed information with unequal affection.

Stacked and parallel U-nets with multi-output for myocardial pathology segmentation

By Zhou Zhao, Nicolas Boutry, Élodie Puybareau

2020-12-01

In Myocardial pathology segmentation combining multi-sequence CMR challenge

Abstract In the field of medical imaging, many different image modalities contain different information, helping practitionners to make diagnostic, follow-up, etc. To better analyze images, mixing multi-modalities information has become a trend. This paper provides one cascaded UNet framework and uses three different modalities (the late gadolinium enhancement (LGE) CMR sequence, the balanced- Steady State Free Precession (bSSFP) cine sequence and the T2-weighted CMR) to complete the segmentation of the myocardium, scar and edema in the context of the MICCAI 2020 myocardial pathology segmentation combining multi-sequence CMR Challenge dataset (MyoPS 2020).

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.

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.

Left atrial segmentation in a few seconds using fully convolutional network and transfer learning

By Élodie Puybareau, Zhou Zhao, Younes Khoudli, Edwin Carlinet, Yongchao Xu, Jérôme Lacotte, Thierry Géraud

2018-10-25

In Proceedings of the workshop on statistical atlases and computational modelling of the heart (STACOM 2018), in conjunction with MICCAI

Abstract In this paper, we propose a fast automatic method that segments left atrial cavity from 3D GE-MRIs without any manual assistance, using a fully convolutional network (FCN) and transfer learning. This FCN is the base network of VGG-16, pre-trained on ImageNet for natural image classification, and fine tuned with the training dataset of the MICCAI 2018 Atrial Segmentation Challenge. It relies on the “pseudo-3D” method published at ICIP 2017, which allows for segmenting objects from 2D color images which contain 3D information of MRI volumes.

Multi-purpose tactile perception based on deep learning in a new tendon-driven optical tactile sensor

Stacked and parallel U-nets with multi-output for myocardial pathology segmentation

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

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

Left atrial segmentation in a few seconds using fully convolutional network and transfer learning

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