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Magnetic Resonance Imaging

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A deep learning approach for automatic 3D segmentation of hip cartilage and labrum from direct hip MR arthrography.

Scientific reports
The objective was to use convolutional neural networks (CNNs) for automatic segmentation of hip cartilage and labrum based on 3D MRI. In this retrospective single-center study, CNNs with a U-Net architecture were used to develop a fully automated seg...

Predicting cognitive decline: Deep-learning reveals subtle brain changes in pre-MCI stage.

The journal of prevention of Alzheimer's disease
BACKGROUND: Mild cognitive impairment (MCI) and preclinical MCI (e.g., subjective cognitive decline, SCD) are considered risk states of dementia, such as Alzheimer's Disease (AD). However, it is challenging to accurately predict conversion from norma...

SegmentAnyBone: A universal model that segments any bone at any location on MRI.

Medical image analysis
Magnetic Resonance Imaging (MRI) is pivotal in radiology, offering non-invasive and high-quality insights into the human body. Precise segmentation of the MRIs into different organs and tissues would be very beneficial as it would allow more accurate...

DeepCERES: A deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI.

NeuroImage
This paper introduces a novel multimodal and high-resolution human brain cerebellum lobule segmentation method. Unlike current tools that operate at standard resolution (1 mm) or using mono-modal data, the proposed method improves cerebellum lobule s...

DeepPrep: an accelerated, scalable and robust pipeline for neuroimaging preprocessing empowered by deep learning.

Nature methods
Neuroimaging has entered the era of big data. However, the advancement of preprocessing pipelines falls behind the rapid expansion of data volume, causing substantial computational challenges. Here we present DeepPrep, a pipeline empowered by deep le...

WDRIV-Net: a weighted ensemble transfer learning to improve automatic type stratification of lumbar intervertebral disc bulge, prolapse, and herniation.

Biomedical engineering online
The degeneration of the intervertebral discs in the lumbar spine is the common cause of neurological and physical dysfunctions and chronic disability of patients, which can be stratified into single-(e.g., disc herniation, prolapse, or bulge) and com...

Integrating manual annotation with deep transfer learning and radiomics for vertebral fracture analysis.

BMC medical imaging
BACKGROUND: Vertebral compression fractures (VCFs) are prevalent in the elderly, often caused by osteoporosis or trauma. Differentiating acute from chronic VCFs is vital for treatment planning, but MRI, the gold standard, is inaccessible for some. Ho...

Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI.

European radiology experimental
BACKGROUND: Gadolinium-enhanced "sampling perfection with application-optimized contrasts using different flip angle evolution" (SPACE) sequence allows better visualization of brain metastases (BMs) compared to "magnetization-prepared rapid acquisiti...

A deep ensemble learning framework for glioma segmentation and grading prediction.

Scientific reports
The segmentation and risk grade prediction of gliomas based on preoperative multimodal magnetic resonance imaging (MRI) are crucial tasks in computer-aided diagnosis. Due to the significant heterogeneity between and within tumors, existing methods ma...

A comparison of different machine learning classifiers in predicting xerostomia and sticky saliva due to head and neck radiotherapy using a multi-objective, multimodal radiomics model.

Biomedical physics & engineering express
. Although radiotherapy techniques are a primary treatment for head and neck cancer (HNC), they are still associated with substantial toxicity and side effects. Machine learning (ML) based radiomics models for predicting toxicity mostly rely on featu...