Human vision has different concentration on visual fields. Cortical magnification factor (CMF) is a popular measurement on visual acuity and cortex concentration. In order to achieve thorough measurement of CMF across the whole visual field, we propose a method to measure planar CMF upon retinotopic maps generated by pRF decoding, with help of our proposed methods: optimal transportation and topological smoothing. The optimal transportation re-calculates vertex location in retinotopic mapping, and topological smoothing guarantees topological conditions in retinotopic maps, which allow us to calculate planar CMF with the proposed 1-ring patch method. The pipeline was applied to the HCP 7T dataset, giving new planar results on CMF measurement across all 181 subjects, which illustrate novel concentration behavior on visual fields and their individual difference.
KEYWORDS: Image segmentation, Brain, Skull, Magnetic resonance imaging, 3D mask effects, Education and training, Image classification, Neuroimaging, Image processing, Bone
In early life, the neurocranium undergoes rapid changes to accommodate the expanding brain. Neurocranial maturation can be disrupted by developmental abnormalities and environmental factors such as sleep position. To establish a baseline for the early detection of anomalies, it is important to understand how this structure typically grows in healthy children. Here, we designed a deep neural network pipeline NEC-NET, including segmentation and classification, to analyze the normative development of the neurocranium in T1 MR images from healthy children aged 12 to 60 months old. The pipeline optimizes the segmentation of the neurocranium and shows the preliminary results of age-based regional differences among infants.
Lesion appearance is a crucial clue for medical providers to distinguish referable diabetic retinopathy (rDR) from non-referable DR. Most existing large-scale DR datasets contain only image-level labels rather than pixel-based annotations. This motivates us to develop algorithms to classify rDR and segment lesions via image-level labels. This paper leverages self-supervised equivariant learning and attention-based multi-instance learning (MIL) to tackle this problem. MIL is an effective strategy to differentiate positive and negative instances, helping us discard background regions (negative instances) while localizing lesion regions (positive ones). However, MIL only provides coarse lesion localization and cannot distinguish lesions located across adjacent patches. Conversely, a self-supervised equivariant attention mechanism (SEAM) generates a segmentation-level class activation map (CAM) that can guide patch extraction of lesions more accurately. Our work aims at integrating both methods to improve rDR classification accuracy. We conduct extensive validation experiments on the Eyepacs dataset, achieving an area under the receiver operating characteristic curve (AU ROC) of 0.958, outperforming current state-of-the-art algorithms.
KEYWORDS: Control systems, Magnetic resonance imaging, Image segmentation, Brain, Analytical research, Neuroimaging, Image registration, Shape analysis, In vivo imaging, Standards development
Preliminary evidence suggests individuals born very-premature have smaller hippocampi on MRI when compared to term-born controls. Moreover, these volumetric reductions have been associated with various cognitive deficits. The hippocampus undergoes an intense period of postnatal volumetric growth during the first year of life. However, this period of development has only been characterized in post-mortem studies. Although volume gain has been previously delineated, changes in hippocampal shape remain undescribed during this unique period. The objective of this study was to characterize and compare morphometric development between very-preterm born infant and healthy controls throughout the first year of life using multivariate tensor-based morphometry (mTBM). We segmented left and right hippocampi from 133 T1-weighted images acquired from 20 very-preterm infants and 67 term-born controls between atbirth or term-equivalent age and 12 months of age. MRI were performed on a 3 Tesla scanner at 3-month intervals (i.e., term-equivalence, 3, 6, 9, 12 months). We used mTBM to compare shape between groups at each time-point. We found that subregions of the hippocampus including the dentate gyrus, CA2, CA3 and subiculum were morphometrically different, especially at term-equivalence age. Morphometric differences were less prominent at 3 and 6 months but reappeared at 9 and 12 months, particularly in the left hippocampus. Although hippocampal shape differences between very-preterm and healthy term-born infants seem to decrease during the first 6 months of life, atypical shape development reappeared at 9-12 months which likely highlights altered periods of morphologic development. Future long-term studies will inform if these developmental differences continue to increase or disappear in subsequent years.
Biomarker-assisted diagnosis and intervention in Alzheimer’s disease (AD) may be the key to prevention breakthroughs. One of the hallmarks of AD is the accumulation of tau plaques in the human brain. However, current methods to detect tau pathology are either invasive (lumbar puncture) or quite costly and not widely available (Tau PET). In our previous work, structural MRI-based hippocampal multivariate morphometry statistics (MMS) showed superior performance as an effective neurodegenerative biomarker for preclinical AD and Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) has excellent ability to generate low-dimensional representations with strong statistical power for brain amyloid prediction. In this work, we apply this framework together with ridge regression models to predict Tau deposition in Braak12 and Braak34 brain regions separately. We evaluate our framework on 925 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Each subject has one pair consisting of a PET image and MRI scan which were collected at about the same times. Experimental results suggest that the representations from our MMS and PASCS-MP have stronger predictive power and their predicted Braak12 and Braak34 are closer to the real values compared to the measures derived from other approaches such as hippocampal surface area and volume, and shape morphometry features based on spherical harmonics (SPHARM).
Generation of white matter (WM) tractography for neonates primarily depends on a successful development of a diffuse tensor imaging (DTI)-based ATLAS. In this study, we present a deep-learning framework for WM tractography of neonates’ brain that is independent of any specific ATLAS. A convolutional neural network (CNN)-based deep-learning architecture is proposed for automated generation of WM tractography. Our dataset consists of DTI scan of 37 neonates (18 preterm and 19 term-born) that can be used to train the model. Although the proposed model is adopted for WM tractography, it can generally be applied for subcortical structures and cerebellum.
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