Background The extent to which pathological processes in aging and Alzheimer’s disease (AD) relate to functional disruption of the medial temporal lobe (MTL)-dependent brain networks is poorly understood. To address ...
Background The extent to which pathological processes in aging and Alzheimer’s disease (AD) relate to functional disruption of the medial temporal lobe (MTL)-dependent brain networks is poorly understood. To address this knowledge gap, we examined functional connectivity (FC) alterations between anterior and posterior regions of the MTL and in MTL-associated functional communities – the Anterior-Temporal (AT) and Posterior-Medial (PM) networks – in normal agers, individuals with preclinical AD, and patients with Mild Cognitive Impairment or mild dementia due to AD. Method In this cross-sectional study, we analyzed data from 179 individuals from the Aging Brain Cohort study of the penn ADRC. Detailed information about participants is provided in Table 1. For intra-MTL FC comparisons, the MTL subregions were segmented using the automated segmentation of hippocampal subfields-T1 (ASHS-T1) pipeline (Fig. 1a). When modeling the MTL’s interactions with the rest of the cortex, we employed four MTL ROIs (left/right × anterior/posterior) derived from an ex vivo atlas of tau accumulation in the MTL. Our functional datasets were preprocessed using a customized fMRIprep pipeline. Sparse network estimations and modularity-based consensus clustering were used to reconstruct the AT and PM network systems (Fig. 1b). Age effect analyses and group comparisons along the AD continuum were performed using the General Linear Model within the network-based statistical framework. Result The preclinical stage of AD was characterized by increased FC between the perirhinal cortex and other regions of the MTL, as well as between the anterior MTL and its direct neighbors in the AT network (Fig. 1c-d). This effect was not present in symptomatic AD. Instead, symptomatic patients displayed reduced hippocampal and intra-PM connectivity. For normal aging, our results led to three main conclusions (for visuals, see Fig. 2). First, intra-network connectivity of both the AT and PM networks decreases wi
Single image super-resolution (SISR) reconstruction for magnetic resonance imaging (MRI) has generated significant interest because of its potential to not only speed up imaging but to improve quantitative processing ...
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ISBN:
(数字)9781728165530
ISBN:
(纸本)9781728165547
Single image super-resolution (SISR) reconstruction for magnetic resonance imaging (MRI) has generated significant interest because of its potential to not only speed up imaging but to improve quantitative processing and analysis of available image data. Generative Adversarial Networks (GAN) have proven to perform well in image recovery tasks. In this work, we followed the GAN framework and developed a generator coupled with discriminator to tackle the task of 3D SISR on T1 brain MRI images. We developed a novel 3D memory-efficient residual-dense block generator (MRDG) that achieves state-of-the-art performance in terms of SSIM (Structural Similarity), PSNR (Peak Signal to Noise Ratio) and NRMSE (Normalized Root Mean Squared Error) metrics. We also designed a pyramid pooling discriminator (PPD) to recover details on different size scales simultaneously. Finally, we introduced model blending, a simple and computational efficient method to balance between image and texture quality in the final output, to the task of SISR on 3D images.
Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to thei...
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Background and Objective: The segmentation of oropharyngeal epithelium tissue within Haematoxylin&Eosin(HE)-stained pathological images is of great clinical significance for the pathological analysis and diagnosis...
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Quantitative organ assessment is an essential step in automated abdominal disease diagnosis and treatment planning. Artificial intelligence (AI) has shown great potential to automatize this process. However, most exis...
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International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from t...
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.
Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that parti...
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Previous foundation models for fundus images were pre-trained with limited disease categories and knowledge base. Here we introduce a knowledge-rich vision-language model (RetiZero) that leverages knowledge from more ...
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Accurate segmentation and biometric analysis are essential for studying the developing fetal brain in utero. The Fetal Brain Tissue Annotation (FeTA) Challenge 2024 builds upon previous editions to further advance the...
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Accurate segmentation and biometric analysis are essential for studying the developing fetal brain in utero. The Fetal Brain Tissue Annotation (FeTA) Challenge 2024 builds upon previous editions to further advance the clinical relevance and robustness of automated fetal brain MRI analysis. This year’s challenge introduced biometry prediction as a new task complementing the usual segmentation task. The segmentation task also included a new low-field (0.55T) MRI testing set and used Euler characteristic difference (ED) as a topology-aware metric for ranking, extending the traditional overlap or distance-based measures. A total of 16 teams submitted segmentation methods for evaluation. Segmentation performance across top teams was highly consistent across both standard and low-field MRI data. Longitudinal analysis over past FeTA editions revealed minimal improvement in accuracy over time, suggesting a potential performance plateau, particularly as results now approach or surpass reported levels of inter-rater variability. However, the introduction of the ED metric revealed topological differences that were not captured by conventional metrics, underscoring its value in assessing segmentation quality. Notably, the curated low-field MRI dataset achieved the highest segmentation performance, illustrating the potential of affordable imaging systems when combined with high-quality preprocessing and reconstruction. A total of 7 teams submitted automated biometry methods for evaluation. While promising, this task exposed a critical limitation: most submitted methods failed to outperform a simple baseline that predicted measurements based solely on gestational age, without using image data. Performance varied widely across biometric measurements and between teams, indicating both current challenges and opportunities for improvement in this area. These findings highlight the need for better integration of volumetric context and stronger modeling strategies needed for the clinic
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not ref...
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