Magnetic resonance image-based brain age as a discriminator of dementia conversion in patients with amyloid-negative amnestic mild cognitive impairment.

Patients with amyloid-negative amnestic mild cognitive impairment (MCI) have a conversion rate of approximately 10% to dementia within 2 years. We aimed to investigate whether brain age is an important factor in predicting conversion to dementia in patients with amyloid-negative amnestic MCI. We conducted a retrospective cohort study of patients with amyloid-negative amnestic MCI. All participants underwent detailed neuropsychological evaluation, brain magnetic resonance imaging (MRI), and [18F]-florbetaben positron emission tomography. Brain age was determined by the volumetric assessment of 12 distinct brain regions using an automatic segmentation software. During the follow-up period, 38% of the patients converted from amnestic MCI to dementia. Further, 73% of patients had a brain age greater than their actual chronological age. When defining 'survival' as the non-conversion of MCI to dementia, these groups differed significantly in survival probability (p = 0.036). The low-educated female group with a brain age greater than their actual age had the lowest survival rate among all groups. Our findings suggest that the MRI-based brain age used in this study can contribute to predicting conversion to dementia in patients with amyloid-negative amnestic MCI.

Automatic segmentation of white matter hyperintensities in T2-FLAIR with AQUA: A comparative validation study against conventional methods.

White matter hyperintensities (WMHs) are lesions in the white matter of the brain that are associated with cognitive decline and an increased risk of dementia. The manual segmentation of WMHs is highly time-consuming and prone to intra- and inter-variability. Therefore, automatic segmentation approaches are gaining attention as a more efficient and objective means to detect and monitor WMHs. In this study, we propose AQUA, a deep learning model designed for fully automatic segmentation of WMHs from T2-FLAIR scans, which improves upon our previous study for small lesion detection and incorporating a multicenter approach. AQUA implements a two-dimensional U-Net architecture and uses patch-based training. Additionally, the network was modified to include Bottleneck Attention Module on each convolutional block of both the encoder and decoder to enhance performance for small-sized WMH. We evaluated the performance and robustness of AQUA by comparing it with five well-known supervised and unsupervised methods for automatic segmentation of WMHs (LGA, LPA, SLS, UBO, and BIANCA). To accomplish this, we tested these six methods on the MICCAI 2017 WMH Segmentation Challenge dataset, which contains MRI images from 170 elderly participants with WMHs of presumed vascular origin, and assessed their robustness across multiple sites and scanner types. The results showed that AQUA achieved superior performance in terms of spatial (Dice = 0.72) and volumetric (logAVD = 0.10) agreement with the manual segmentation compared to the other methods. While the recall and F1-score were moderate at 0.49 and 0.59, respectively, they improved to 0.75 and 0.82 when excluding small lesions (≤ 6 voxels). Remarkably, despite being trained on a different dataset with different ethnic backgrounds, lesion loads, and scanners, AQUA's results were comparable to the top 10 ranked methods of the MICCAI challenge. The findings suggest that AQUA is effective and practical for automatic segmentation of WMHs from T2-FLAIR scans, which could help identify individuals at risk of cognitive decline and dementia and allow for early intervention and management.

Fetal brain tissue annotation and segmentation challenge results.

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.

A Fully Automated Visual Grading System for White Matter Hyperintensities of T2-Fluid Attenuated Inversion Recovery Magnetic Resonance Imaging.

BACKGROUND: The Fazekas scale is one of the most commonly used visual grading systems for white matter hyperintensity (WMH) for brain disorders like dementia from T2-fluid attenuated inversion recovery magnetic resonance (MR) images (T2-FLAIRs). However, the visual grading of the Fazekas scale suffers from low-intra and inter-rater reliability and high labor-intensive work. Therefore, we developed a fully automated visual grading system using quantifiable measurements. METHODS: Our approach involves four stages: (1) the deep learning-based segmentation of ventricles and WMH lesions, (2) the categorization into periventricular white matter hyperintensity (PWMH) and deep white matter hyperintensity (DWMH), (3) the WMH diameter measurement, and (4) automated scoring, following the quantifiable method modified for Fazekas grading. We compared the performances of our method and that of the modified Fazekas scale graded by three neuroradiologists for 404 subjects with T2-FLAIR utilized from a clinical site in Korea. RESULTS: The Krippendorff's alpha across our method and raters (A) versus those only between the radiologists (R) were comparable, showing substantial (0.694 vs. 0.732; 0.658 vs. 0.671) and moderate (0.579 vs. 0.586) level of agreements for the modified Fazekas, the DWMH, and the PWMH scales, respectively. Also, the average of areas under the receiver operating characteristic curve between the radiologists (0.80 ± 0.09) and the radiologists against our approach (0.80 ± 0.03) was comparable. CONCLUSIONS: Our fully automated visual grading system for WMH demonstrated comparable performance to the radiologists, which we believe has the potential to assist the radiologist in clinical findings with unbiased and consistent scoring.

Semi-Supervised Learning in Medical MRI Segmentation: Brain Tissue with White Matter Hyperintensity Segmentation Using FLAIR MRI.

White-matter hyperintensity (WMH) is a primary biomarker for small-vessel cerebrovascular disease, Alzheimer's disease (AD), and others. The association of WMH with brain structural changes has also recently been reported. Although fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) provide valuable information about WMH, FLAIR does not provide other normal tissue information. The multi-modal analysis of FLAIR and T1-weighted (T1w) MRI is thus desirable for WMH-related brain aging studies. In clinical settings, however, FLAIR is often the only available modality. In this study, we thus propose a semi-supervised learning method for full brain segmentation using FLAIR. The results of our proposed method were compared with the reference labels, which were obtained by FreeSurfer segmentation on T1w MRI. The relative volume difference between the two sets of results shows that our proposed method has high reliability. We further evaluated our proposed WMH segmentation by comparing the Dice similarity coefficients of the reference and the results of our proposed method. We believe our semi-supervised learning method has a great potential for use for other MRI sequences and will encourage others to perform brain tissue segmentation using MRI modalities other than T1w.

Split-Attention U-Net: A Fully Convolutional Network for Robust Multi-Label Segmentation from Brain MRI.

Multi-label brain segmentation from brain magnetic resonance imaging (MRI) provides valuable structural information for most neurological analyses. Due to the complexity of the brain segmentation algorithm, it could delay the delivery of neuroimaging findings. Therefore, we introduce Split-Attention U-Net (SAU-Net), a convolutional neural network with skip pathways and a split-attention module that segments brain MRI scans. The proposed architecture employs split-attention blocks, skip pathways with pyramid levels, and evolving normalization layers. For efficient training, we performed pre-training and fine-tuning with the original and manually modified FreeSurfer labels, respectively. This learning strategy enables involvement of heterogeneous neuroimaging data in the training without the need for many manual annotations. Using nine evaluation datasets, we demonstrated that SAU-Net achieved better segmentation accuracy with better reliability that surpasses those of state-of-the-art methods. We believe that SAU-Net has excellent potential due to its robustness to neuroanatomical variability that would enable almost instantaneous access to accurate neuroimaging biomarkers and its swift processing runtime compared to other methods investigated.