1,401 research outputs found
Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks
Magnetic resonance image (MRI) in high spatial resolution provides detailed
anatomical information and is often necessary for accurate quantitative
analysis. However, high spatial resolution typically comes at the expense of
longer scan time, less spatial coverage, and lower signal to noise ratio (SNR).
Single Image Super-Resolution (SISR), a technique aimed to restore
high-resolution (HR) details from one single low-resolution (LR) input image,
has been improved dramatically by recent breakthroughs in deep learning. In
this paper, we introduce a new neural network architecture, 3D Densely
Connected Super-Resolution Networks (DCSRN) to restore HR features of
structural brain MR images. Through experiments on a dataset with 1,113
subjects, we demonstrate that our network outperforms bicubic interpolation as
well as other deep learning methods in restoring 4x resolution-reduced images.Comment: Accepted by ISBI'1
The Scalable Brain Atlas: instant web-based access to public brain atlases and related content
The Scalable Brain Atlas (SBA) is a collection of web services that provide
unified access to a large collection of brain atlas templates for different
species. Its main component is an atlas viewer that displays brain atlas data
as a stack of slices in which stereotaxic coordinates and brain regions can be
selected. These are subsequently used to launch web queries to resources that
require coordinates or region names as input. It supports plugins which run
inside the viewer and respond when a new slice, coordinate or region is
selected. It contains 20 atlas templates in six species, and plugins to compute
coordinate transformations, display anatomical connectivity and fiducial
points, and retrieve properties, descriptions, definitions and 3d
reconstructions of brain regions. The ambition of SBA is to provide a unified
representation of all publicly available brain atlases directly in the web
browser, while remaining a responsive and light weight resource that
specializes in atlas comparisons, searches, coordinate transformations and
interactive displays.Comment: Rolf K\"otter sadly passed away on June 9th, 2010. He co-initiated
this project and played a crucial role in the design and quality assurance of
the Scalable Brain Atla
An Anatomy-aware Framework for Automatic Segmentation of Parotid Tumor from Multimodal MRI
Magnetic Resonance Imaging (MRI) plays an important role in diagnosing the
parotid tumor, where accurate segmentation of tumors is highly desired for
determining appropriate treatment plans and avoiding unnecessary surgery.
However, the task remains nontrivial and challenging due to ambiguous
boundaries and various sizes of the tumor, as well as the presence of a large
number of anatomical structures around the parotid gland that are similar to
the tumor. To overcome these problems, we propose a novel anatomy-aware
framework for automatic segmentation of parotid tumors from multimodal MRI.
First, a Transformer-based multimodal fusion network PT-Net is proposed in this
paper. The encoder of PT-Net extracts and fuses contextual information from
three modalities of MRI from coarse to fine, to obtain cross-modality and
multi-scale tumor information. The decoder stacks the feature maps of different
modalities and calibrates the multimodal information using the channel
attention mechanism. Second, considering that the segmentation model is prone
to be disturbed by similar anatomical structures and make wrong predictions, we
design anatomy-aware loss. By calculating the distance between the activation
regions of the prediction segmentation and the ground truth, our loss function
forces the model to distinguish similar anatomical structures with the tumor
and make correct predictions. Extensive experiments with MRI scans of the
parotid tumor showed that our PT-Net achieved higher segmentation accuracy than
existing networks. The anatomy-aware loss outperformed state-of-the-art loss
functions for parotid tumor segmentation. Our framework can potentially improve
the quality of preoperative diagnosis and surgery planning of parotid tumors.Comment: under revie
Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRI
Congenital Heart Disease (CHD) is a group of cardiac malformations present
already during fetal life, representing the prevailing category of birth
defects globally. Our aim in this study is to aid 3D fetal vessel topology
visualisation in aortic arch anomalies, a group which encompasses a range of
conditions with significant anatomical heterogeneity. We present a multi-task
framework for automated multi-class fetal vessel segmentation from 3D black
blood T2w MRI and anomaly classification. Our training data consists of binary
manual segmentation masks of the cardiac vessels' region in individual subjects
and fully-labelled anomaly-specific population atlases. Our framework combines
deep learning label propagation using VoxelMorph with 3D Attention U-Net
segmentation and DenseNet121 anomaly classification. We target 11 cardiac
vessels and three distinct aortic arch anomalies, including double aortic arch,
right aortic arch, and suspected coarctation of the aorta. We incorporate an
anomaly classifier into our segmentation pipeline, delivering a multi-task
framework with the primary motivation of correcting topological inaccuracies of
the segmentation. The hypothesis is that the multi-task approach will encourage
the segmenter network to learn anomaly-specific features. As a secondary
motivation, an automated diagnosis tool may have the potential to enhance
diagnostic confidence in a decision support setting. Our results showcase that
our proposed training strategy significantly outperforms label propagation and
a network trained exclusively on propagated labels. Our classifier outperforms
a classifier trained exclusively on T2w volume images, with an average balanced
accuracy of 0.99 (0.01) after joint training. Adding a classifier improves the
anatomical and topological accuracy of all correctly classified double aortic
arch subjects.Comment: Accepted for publication at the Journal of Machine Learning for
Biomedical Imaging (MELBA) https://melba-journal.org/2023:01
Deep active learning for suggestive segmentation of biomedical image stacks via optimisation of Dice scores and traced boundary length
Manual segmentation of stacks of 2D biomedical images (e.g., histology) is a time-consuming task which can be sped up with semi-automated techniques. In this article, we present a suggestive deep active learning framework that seeks to minimise the annotation effort required to achieve a certain level of accuracy when labelling such a stack. The framework suggests, at every iteration, a specific region of interest (ROI) in one of the images for manual delineation. Using a deep segmentation neural network and a mixed cross-entropy loss function, we propose a principled strategy to estimate class probabilities for the whole stack, conditioned on heterogeneous partial segmentations of the 2D images, as well as on weak supervision in the form of image indices that bound each ROI. Using the estimated probabilities, we propose a novel active learning criterion based on predictions for the estimated segmentation performance and delineation effort, measured with average Dice scores and total delineated boundary length, respectively, rather than common surrogates such as entropy. The query strategy suggests the ROI that is expected to maximise the ratio between performance and effort, while considering the adjacency of structures that may have already been labelled – which decrease the length of the boundary to trace. We provide quantitative results on synthetically deformed MRI scans and real histological data, showing that our framework can reduce labelling effort by up to 60–70% without compromising accuracy
Alzheimer Disease Detection using AI with Deep Learning based Features with Development and Validation based on Data Science
Alzheimer's disease (AD), a neurological condition that worsens over time, affects millions of individuals worldwide. Because of this, effective intervention and therapy depend on early and precise detection. In recent years, encouraging findings have been obtained using data science and artificial intelligence (AI) techniques in the field of medical diagnostics, particularly AD diagnosis. This work seeks to develop an accurate algorithm for diagnosing AD by identifying AI-based traits from neuroimaging and clinical data.The three key steps of the proposed methodology are data preprocessing, feature extraction, and model development and validation. To offer neuroimaging data, such as MRI and PET scans, as well as essential clinical information, a cohort of persons made up of AD patients and healthy controls is obtained. Throughout the preparation stage, the data are normalised, standardised, and quality-checked to ensure accuracy and consistency.The critical role of feature extraction in locating critical patterns and features potentially indicative of AD is critical. Advanced AI techniques like Convolutional Neural Networks and Recurrent Neural Networks are utilised to extract discriminative features from neuroimaging data after subjecting it to feature engineering methods.The retrieved features are then utilised to build a prediction model using state-of-the-art machine learning techniques such as Support Vector Machines (SVM), Random Forests, or Deep Learning architectures. Strict validation methods, such cross-validation and test datasets, are used to evaluate the model's performance in order to ensure generalizability and minimise overfitting.The project's objective is to identify AD with high specificity, sensitivity, and accuracy to support early diagnosis and tailored treatment planning. The results of this research contribute to the body of knowledge on AI-based diagnostics for neurodegenerative diseases and have the potential to significantly impact clinical practises by facilitating early interventions and improving patient outcomes. It is important to take into account the size and heterogeneity of the dataset as well as any prospective improvements and future expansions to the usage of AI in AD detection
- …