942 research outputs found
Learning Rigid Image Registration - Utilizing Convolutional Neural Networks for Medical Image Registration
Many traditional computer vision tasks, such as segmentation, have seen large step-changes in accuracy and/or speed with the application of Convolutional Neural Networks (CNNs). Image registration, the alignment of two or more images to a common space, is a fundamental step in many medical imaging workflows. In this paper we investigate whether these techniques can also bring tangible benefits to the registration task. We describe and evaluate the use of convolutional neural networks (CNNs) for both mono- and multi- modality registration and compare their performance to more traditional schemes, namely multi-scale, iterative registration. This paper also investigates incorporating inverse consistency of the learned spatial transformations to impose additional constraints on the network during training and investigate any benefit in accuracy during detection. The approaches are validated with a series of artificial mono-modal registration tasks utilizing T1-weighted MR brain i mages from the Open Access Series of Imaging Studies (OASIS) study and IXI brain development dataset and a series of real multi-modality registration tasks using T1-weighted and T2-weighted MR brain images from the 2015 Ischemia Stroke Lesion segmentation (ISLES) challenge. The results demonstrate that CNNs give excellent performance for both mono- and multi- modality head and neck registration compared to the baseline method with significantly fewer outliers and lower mean errors
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRI
Multi-slice magnetic resonance images of the fetal brain are usually
contaminated by severe and arbitrary fetal and maternal motion. Hence, stable
and robust motion correction is necessary to reconstruct high-resolution 3D
fetal brain volume for clinical diagnosis and quantitative analysis. However,
the conventional registration-based correction has a limited capture range and
is insufficient for detecting relatively large motions. Here, we present a
novel Affinity Fusion-based Framework for Iteratively Random Motion (AFFIRM)
correction of the multi-slice fetal brain MRI. It learns the sequential motion
from multiple stacks of slices and integrates the features between 2D slices
and reconstructed 3D volume using affinity fusion, which resembles the
iterations between slice-to-volume registration and volumetric reconstruction
in the regular pipeline. The method accurately estimates the motion regardless
of brain orientations and outperforms other state-of-the-art learning-based
methods on the simulated motion-corrupted data, with a 48.4% reduction of mean
absolute error for rotation and 61.3% for displacement. We then incorporated
AFFIRM into the multi-resolution slice-to-volume registration and tested it on
the real-world fetal MRI scans at different gestation stages. The results
indicated that adding AFFIRM to the conventional pipeline improved the success
rate of fetal brain super-resolution reconstruction from 77.2% to 91.9%
Towards segmentation and spatial alignment of the human embryonic brain using deep learning for atlas-based registration
We propose an unsupervised deep learning method for atlas based registration
to achieve segmentation and spatial alignment of the embryonic brain in a
single framework. Our approach consists of two sequential networks with a
specifically designed loss function to address the challenges in 3D first
trimester ultrasound. The first part learns the affine transformation and the
second part learns the voxelwise nonrigid deformation between the target image
and the atlas. We trained this network end-to-end and validated it against a
ground truth on synthetic datasets designed to resemble the challenges present
in 3D first trimester ultrasound. The method was tested on a dataset of human
embryonic ultrasound volumes acquired at 9 weeks gestational age, which showed
alignment of the brain in some cases and gave insight in open challenges for
the proposed method. We conclude that our method is a promising approach
towards fully automated spatial alignment and segmentation of embryonic brains
in 3D ultrasound
Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson's disease and schizophrenia
Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided
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