776 research outputs found
Image-level harmonization of multi-site data using image-and-spatial transformer networks
We investigate the use of image-and-spatial transformer networks (ISTNs) to tackle domain shift in multi-site medical imaging data. Commonly, domain adaptation (DA) is performed with little regard for explainability of the inter-domain transformation and is often conducted at the feature-level in the latent space. We employ ISTNs for DA at the image-level which constrains transformations to explainable appearance and shape changes. As proof-of-concept we demonstrate that ISTNs can be trained adversarially on a classification problem with simulated 2D data. For real-data validation, we construct two 3D brain MRI datasets from the Cam-CAN and UK Biobank studies to investigate domain shift due to acquisition and population differences. We show that age regression and sex classification models trained on ISTN output improve generalization when training on data from one and testing on the other site
Efficacy of MRI data harmonization in the age of machine learning. A multicenter study across 36 datasets
Pooling publicly-available MRI data from multiple sites allows to assemble
extensive groups of subjects, increase statistical power, and promote data
reuse with machine learning techniques. The harmonization of multicenter data
is necessary to reduce the confounding effect associated with non-biological
sources of variability in the data. However, when applied to the entire dataset
before machine learning, the harmonization leads to data leakage, because
information outside the training set may affect model building, and potentially
falsely overestimate performance. We propose a 1) measurement of the efficacy
of data harmonization; 2) harmonizer transformer, i.e., an implementation of
the ComBat harmonization allowing its encapsulation among the preprocessing
steps of a machine learning pipeline, avoiding data leakage. We tested these
tools using brain T1-weighted MRI data from 1740 healthy subjects acquired at
36 sites. After harmonization, the site effect was removed or reduced, and we
measured the data leakage effect in predicting individual age from MRI data,
highlighting that introducing the harmonizer transformer into a machine
learning pipeline allows for avoiding data leakage
Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in
medical imaging. However, these approaches primarily focus on supervised
learning, assuming that the training and testing data are drawn from the same
distribution. Unfortunately, this assumption may not always hold true in
practice. To address these issues, unsupervised domain adaptation (UDA)
techniques have been developed to transfer knowledge from a labeled domain to a
related but unlabeled domain. In recent years, significant advancements have
been made in UDA, resulting in a wide range of methodologies, including feature
alignment, image translation, self-supervision, and disentangled representation
methods, among others. In this paper, we provide a comprehensive literature
review of recent deep UDA approaches in medical imaging from a technical
perspective. Specifically, we categorize current UDA research in medical
imaging into six groups and further divide them into finer subcategories based
on the different tasks they perform. We also discuss the respective datasets
used in the studies to assess the divergence between the different domains.
Finally, we discuss emerging areas and provide insights and discussions on
future research directions to conclude this survey.Comment: Under Revie
Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site data
Many clinical and research studies of the human brain require accurate structural MRI segmentation. While traditional atlas-based methods can be applied to volumes from any acquisition site, recent deep learning algorithms ensure high accuracy only when tested on data from the same sites exploited in training (i.e., internal data). Performance degradation experienced on external data (i.e., unseen volumes from unseen sites) is due to the inter-site variability in intensity distributions, and to unique artefacts caused by different MR scanner models and acquisition parameters. To mitigate this site-dependency, often referred to as the scanner effect, we propose LOD-Brain, a 3D convolutional neural network with progressive levels-of-detail (LOD), able to segment brain data from any site. Coarser network levels are responsible for learning a robust anatomical prior helpful in identifying brain structures and their locations, while finer levels refine the model to handle site-specific intensity distributions and anatomical variations. We ensure robustness across sites by training the model on an unprecedentedly rich dataset aggregating data from open repositories: almost 27,000 T1w volumes from around 160 acquisition sites, at 1.5 - 3T, from a population spanning from 8 to 90 years old. Extensive tests demonstrate that LOD-Brain produces state-of-the-art results, with no significant difference in performance between internal and external sites, and robust to challenging anatomical variations. Its portability paves the way for large-scale applications across different healthcare institutions, patient populations, and imaging technology manufacturers. Code, model, and demo are available on the project website
Domain Generalization for Medical Image Analysis: A Survey
Medical Image Analysis (MedIA) has become an essential tool in medicine and
healthcare, aiding in disease diagnosis, prognosis, and treatment planning, and
recent successes in deep learning (DL) have made significant contributions to
its advances. However, DL models for MedIA remain challenging to deploy in
real-world situations, failing for generalization under the distributional gap
between training and testing samples, known as a distribution shift problem.
Researchers have dedicated their efforts to developing various DL methods to
adapt and perform robustly on unknown and out-of-distribution data
distributions. This paper comprehensively reviews domain generalization studies
specifically tailored for MedIA. We provide a holistic view of how domain
generalization techniques interact within the broader MedIA system, going
beyond methodologies to consider the operational implications on the entire
MedIA workflow. Specifically, we categorize domain generalization methods into
data-level, feature-level, model-level, and analysis-level methods. We show how
those methods can be used in various stages of the MedIA workflow with DL
equipped from data acquisition to model prediction and analysis. Furthermore,
we include benchmark datasets and applications used to evaluate these
approaches and analyze the strengths and weaknesses of various methods,
unveiling future research opportunities
Scanner Invariant Multiple Sclerosis Lesion Segmentation from MRI
This paper presents a simple and effective generalization method for magnetic
resonance imaging (MRI) segmentation when data is collected from multiple MRI
scanning sites and as a consequence is affected by (site-)domain shifts. We
propose to integrate a traditional encoder-decoder network with a
regularization network. This added network includes an auxiliary loss term
which is responsible for the reduction of the domain shift problem and for the
resulting improved generalization. The proposed method was evaluated on
multiple sclerosis lesion segmentation from MRI data. We tested the proposed
model on an in-house clinical dataset including 117 patients from 56 different
scanning sites. In the experiments, our method showed better generalization
performance than other baseline networks
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