Fully Automated Brain Extraction and Orientation in Raw Fetal MRI
نویسندگان
چکیده
Abstract. Fetal magnetic resonance imaging (MRI) is a rapidly growing field of research for its potential to study brain development in utero. However, in contrast to adult studies automatic brain extraction and orientation is not yet solved, but remains challenging in wide field of view raw fetal MRI volumes. This has limited research to small scale studies. This paper presents an automatic fetal brain extraction and orientation framework to remove this limitation. The method consists of a two-phase random forest classifier, and an approximate high-order Markov random field solution, that results in a brain mask for an MRI stack. The resulting extraction achieves 98% detection rate with 88% mean sensitivity when validated on a set of cases aged between 18-30.2 gestational weeks (GW), supporting a robust pipeline to automated fetal MRI processing techniques.
منابع مشابه
Automatic Segmentation of Fetal Brain from MRI of Human
Fetal MRI is an essential tool for analyzing morphological changes of fetal brain structure. The automated methods developed for adult brain extraction are unsuitable for fetal brain extraction because of the differences in tissue types and tissue properties between adult and fetal brain. However, only few automated fetal brain segmentation methods are available. In this paper we propose a full...
متن کاملReal-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning
Brain segmentation is a fundamental first step in neuroimage analysis. In the case of fetal MRI, it is particularly challenging and important due to the arbitrary orientation of the fetus, organs that surround the fetal head, and intermittent fetal motion. Several promising methods have been proposed but are limited in their performance in challenging cases and in realtime segmentation. We aime...
متن کاملComparison the Accuracy of Fetal Brain Extraction from T2-Half-Fourier Acquisition Single-Shot Turbo Spin-Echo (HASTE) MR Image with T2-True Fast Imaging with Steady State Free Precession (TRUFI) MR Image by Level Set Algorithm
Background Access to appropriate images of fetal brain can greatly assist to diagnose of probable abnormalities. The aim of this study was to compare the suitability of T2-True Fast Imaging with Steady State Free Precession (T2-TRUFI), and T2-Half-Fourier Acquisition Single-Shot Turbo Spin-Echo (T2- HASTE( magnetic resonance imaging (MRI) to extract the fetal brain using the level set algorithm...
متن کاملA Two-Dimensional Convolutional Neural Network for Brain Tumor Detection From MRI
Aims: Cancerous brain tumors are among the most dangerous diseases that lower the quality of life of people for many years. Their detection in the early stages paves the way for the proper treatment. The present study aimed to present a two-dimensional Convolutional Neural Network (CNN) for detecting brain tumors under Magnetic Resonance Imaging (MRI) using the deep learning method. Methods & ...
متن کاملAutomated Brain Extraction in Fetal Mri by Multi-atlas Fusion Strategy: Study on Healthy and Pathological Subjects
AUTOMATED BRAIN EXTRACTION IN FETAL MRI BY MULTI-ATLAS FUSION STRATEGY: STUDY ON HEALTHY AND PATHOLOGICAL SUBJECTS. Sébastien Tourbier, Xavier Bresson, Patric Hagmann, Maud Cagneaux, Marie Schaer, Laurent Guibaud, Jean-Philippe Thiran, Reto Meuli, and Meritxell Bach Cuadra Centre d'Imagerie BioMédicale (CIBM), Lausanne, Vaud, Switzerland, Department of Radiology, University Hospital Center (CHU...
متن کامل