16 research outputs found
Hippocampal representations for deep learning on Alzheimer’s disease
Deep learning offers a powerful approach for analyzing hippocampal changes in Alzheimer’s disease (AD) without relying on handcrafted features. Nevertheless, an input format needs to be selected to pass the image information to the neural network, which has wide ramifications for the analysis, but has not been evaluated yet. We compare five hippocampal representations (and their respective tailored network architectures) that span from raw images to geometric representations like meshes and point clouds. We performed a thorough evaluation for the prediction of AD diagnosis and time-to-dementia prediction with experiments on an independent test dataset. In addition, we evaluated the ease of interpretability for each representation–network pair. Our results show that choosing an appropriate representation of the hippocampus for predicting Alzheimer’s disease with deep learning is crucial, since it impacts performance and ease of interpretation
Joint Reconstruction and Parcellation of Cortical Surfaces
The reconstruction of cerebral cortex surfaces from brain MRI scans is
instrumental for the analysis of brain morphology and the detection of cortical
thinning in neurodegenerative diseases like Alzheimer's disease (AD). Moreover,
for a fine-grained analysis of atrophy patterns, the parcellation of the
cortical surfaces into individual brain regions is required. For the former
task, powerful deep learning approaches, which provide highly accurate brain
surfaces of tissue boundaries from input MRI scans in seconds, have recently
been proposed. However, these methods do not come with the ability to provide a
parcellation of the reconstructed surfaces. Instead, separate
brain-parcellation methods have been developed, which typically consider the
cortical surfaces as given, often computed beforehand with FreeSurfer. In this
work, we propose two options, one based on a graph classification branch and
another based on a novel generic 3D reconstruction loss, to augment
template-deformation algorithms such that the surface meshes directly come with
an atlas-based brain parcellation. By combining both options with two of the
latest cortical surface reconstruction algorithms, we attain highly accurate
parcellations with a Dice score of 90.2 (graph classification branch) and 90.4
(novel reconstruction loss) together with state-of-the-art surfaces.Comment: accepted at MLCN workshop 202
Influence of the Basal Metabolic Profile on the Evolution of the Pediatric Patient with Obesity
Childhood obesity is a problem of growing importance globally. It is associated with significant health problems. Knowing how to treat it effectively would improve the quality of life of these children. The aim of this chapter is to study how basal metabolism influences the somatometric evolution of the child and adolescent population with obesity in a pediatric endocrinology clinic. Study childhood obesity in a tertiary hospital by means of a multichannel impedanceometry study. All the patients had a basal metabolism lower than the calculated theoretical ideal. In overall terms, weight reduction is not achieved in this pediatric population. However, it is observed a decrease in fat content in the medium term (1-3Â years). Bioelectrical impedanceometry measurement is a simple method in clinical practice to evaluate the energy consumption and the body composition. Knowing the body composition of these children would help to intervene more effectively to help control obesity and its health consequences
The Medical Segmentation Decathlon
International challenges have become the de facto standard for comparative
assessment of image analysis algorithms given a specific task. Segmentation is
so far the most widely investigated medical image processing task, but the
various segmentation challenges have typically been organized in isolation,
such that algorithm development was driven by the need to tackle a single
specific clinical problem. We hypothesized that a method capable of performing
well on multiple tasks will generalize well to a previously unseen task and
potentially outperform a custom-designed solution. To investigate the
hypothesis, we organized the Medical Segmentation Decathlon (MSD) - a
biomedical image analysis challenge, in which algorithms compete in a multitude
of both tasks and modalities. The underlying data set was designed to explore
the axis of difficulties typically encountered when dealing with medical
images, such as small data sets, unbalanced labels, multi-site data and small
objects. The MSD challenge confirmed that algorithms with a consistent good
performance on a set of tasks preserved their good average performance on a
different set of previously unseen tasks. Moreover, by monitoring the MSD
winner for two years, we found that this algorithm continued generalizing well
to a wide range of other clinical problems, further confirming our hypothesis.
Three main conclusions can be drawn from this study: (1) state-of-the-art image
segmentation algorithms are mature, accurate, and generalize well when
retrained on unseen tasks; (2) consistent algorithmic performance across
multiple tasks is a strong surrogate of algorithmic generalizability; (3) the
training of accurate AI segmentation models is now commoditized to non AI
experts
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor
Segmentation Benchmark (LITS) organized in conjunction with the IEEE
International Symposium on Biomedical Imaging (ISBI) 2016 and International
Conference On Medical Image Computing Computer Assisted Intervention (MICCAI)
2017. Twenty four valid state-of-the-art liver and liver tumor segmentation
algorithms were applied to a set of 131 computed tomography (CT) volumes with
different types of tumor contrast levels (hyper-/hypo-intense), abnormalities
in tissues (metastasectomie) size and varying amount of lesions. The submitted
algorithms have been tested on 70 undisclosed volumes. The dataset is created
in collaboration with seven hospitals and research institutions and manually
reviewed by independent three radiologists. We found that not a single
algorithm performed best for liver and tumors. The best liver segmentation
algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation
the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image
data and manual annotations continue to be publicly available through an online
evaluation system as an ongoing benchmarking resource.Comment: conferenc
CASHformer: Cognition Aware SHape Transformer for Longitudinal Analysis
Modeling temporal changes in subcortical structures is crucial for a better
understanding of the progression of Alzheimer's disease (AD). Given their
flexibility to adapt to heterogeneous sequence lengths, mesh-based transformer
architectures have been proposed in the past for predicting hippocampus
deformations across time. However, one of the main limitations of transformers
is the large amount of trainable parameters, which makes the application on
small datasets very challenging. In addition, current methods do not include
relevant non-image information that can help to identify AD-related patterns in
the progression. To this end, we introduce CASHformer, a transformer-based
framework to model longitudinal shape trajectories in AD. CASHformer
incorporates the idea of pre-trained transformers as universal compute engines
that generalize across a wide range of tasks by freezing most layers during
fine-tuning. This reduces the number of parameters by over 90% with respect to
the original model and therefore enables the application of large models on
small datasets without overfitting. In addition, CASHformer models cognitive
decline to reveal AD atrophy patterns in the temporal sequence. Our results
show that CASHformer reduces the reconstruction error by 73% compared to
previously proposed methods. Moreover, the accuracy of detecting patients
progressing to AD increases by 3% with imputing missing longitudinal shape
data