Quentin Delannoy1, Chi-Hieu Pham2, Clément Cazorla1, Carlos Tor-Díez2, Guillaume Dollé3, Hélène Meunier4, Nathalie Bednarek5, Ronan Fablet6, Nicolas Passat7, François Rousseau2. 1. Université de Reims Champagne Ardenne, CReSTIC EA 3804, 51097 Reims, France. 2. IMT Atlantique, LaTIM U1101 INSERM, UBL, Brest, France. 3. Université de Reims Champagne Ardenne, CNRS, LMR UMR 9008, 51097 Reims, France. 4. Service de médecine néonatale et réanimation pédiatrique, CHU de Reims, France. 5. Université de Reims Champagne Ardenne, CReSTIC EA 3804, 51097 Reims, France; Service de médecine néonatale et réanimation pédiatrique, CHU de Reims, France. 6. IMT Atlantique, Lab-STICC UMR CNRS 6285, Brest, France. 7. Université de Reims Champagne Ardenne, CReSTIC EA 3804, 51097 Reims, France. Electronic address: nicolas.passat@univ-reims.fr.
Abstract
BACKGROUND AND OBJECTIVE: One of the main issues in the analysis of clinical neonatal brain MRI is the low anisotropic resolution of the data. In most MRI analysis pipelines, data are first re-sampled using interpolation or single image super-resolution techniques and then segmented using (semi-)automated approaches. In other words, image reconstruction and segmentation are then performed separately. In this article, we propose a methodology and a software solution for carrying out simultaneously high-resolution reconstruction and segmentation of brain MRI data. METHODS: Our strategy mainly relies on generative adversarial networks. The network architecture is described in detail. We provide information about its implementation, focusing on the most crucial technical points (whereas complementary details are given in a dedicated GitHub repository). We illustrate the behavior of the proposed method for cortex analysis from neonatal MR images. RESULTS: The results of the method, evaluated quantitatively (Dice, peak signal-to-noise ratio, structural similarity, number of connected components) and qualitatively on a research dataset (dHCP) and a clinical one (Epirmex), emphasize the relevance of the approach, and its ability to take advantage of data-augmentation strategies. CONCLUSIONS: Results emphasize the potential of our proposed method/software with respect to practical medical applications. The method is provided as a freely available software tool, which allows one to carry out his/her own experiments, and involve the method for the super-resolution reconstruction and segmentation of arbitrary cerebral structures from any MR image dataset.
BACKGROUND AND OBJECTIVE: One of the main issues in the analysis of clinical neonatal brain MRI is the low anisotropic resolution of the data. In most MRI analysis pipelines, data are first re-sampled using interpolation or single image super-resolution techniques and then segmented using (semi-)automated approaches. In other words, image reconstruction and segmentation are then performed separately. In this article, we propose a methodology and a software solution for carrying out simultaneously high-resolution reconstruction and segmentation of brain MRI data. METHODS: Our strategy mainly relies on generative adversarial networks. The network architecture is described in detail. We provide information about its implementation, focusing on the most crucial technical points (whereas complementary details are given in a dedicated GitHub repository). We illustrate the behavior of the proposed method for cortex analysis from neonatal MR images. RESULTS: The results of the method, evaluated quantitatively (Dice, peak signal-to-noise ratio, structural similarity, number of connected components) and qualitatively on a research dataset (dHCP) and a clinical one (Epirmex), emphasize the relevance of the approach, and its ability to take advantage of data-augmentation strategies. CONCLUSIONS: Results emphasize the potential of our proposed method/software with respect to practical medical applications. The method is provided as a freely available software tool, which allows one to carry out his/her own experiments, and involve the method for the super-resolution reconstruction and segmentation of arbitrary cerebral structures from any MR image dataset.
Authors: Xiao Zhou; Shangran Qiu; Prajakta S Joshi; Chonghua Xue; Ronald J Killiany; Asim Z Mian; Sang P Chin; Rhoda Au; Vijaya B Kolachalama Journal: Alzheimers Res Ther Date: 2021-03-14 Impact factor: 8.823
Authors: Maria Elena Laino; Pierandrea Cancian; Letterio Salvatore Politi; Matteo Giovanni Della Porta; Luca Saba; Victor Savevski Journal: J Imaging Date: 2022-03-23