AIMC Topic: Magnetic Resonance Imaging

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Constrained-CNN losses for weakly supervised segmentation.

Medical image analysis
Weakly-supervised learning based on, e.g., partially labelled images or image-tags, is currently attracting significant attention in CNN segmentation as it can mitigate the need for full and laborious pixel/voxel annotations. Enforcing high-order (gl...

Iterative fully convolutional neural networks for automatic vertebra segmentation and identification.

Medical image analysis
Precise segmentation and anatomical identification of the vertebrae provides the basis for automatic analysis of the spine, such as detection of vertebral compression fractures or other abnormalities. Most dedicated spine CT and MR scans as well as s...

Characterization of deep neural network features by decodability from human brain activity.

Scientific data
Achievements of near human-level performance in object recognition by deep neural networks (DNNs) have triggered a flood of comparative studies between the brain and DNNs. Using a DNN as a proxy for hierarchical visual representations, our recent stu...

Vision-aided brain-machine interface training system for robotic arm control and clinical application on two patients with cervical spinal cord injury.

Biomedical engineering online
BACKGROUND: While spontaneous robotic arm control using motor imagery has been reported, most previous successful cases have used invasive approaches with advantages in spatial resolution. However, still many researchers continue to investigate metho...

Machine learning identifies "rsfMRI epilepsy networks" in temporal lobe epilepsy.

European radiology
OBJECTIVES: Experimental models have provided compelling evidence for the existence of neural networks in temporal lobe epilepsy (TLE). To identify and validate the possible existence of resting-state "epilepsy networks," we used machine learning met...

Prediction of IDH1 Mutation Status in Glioblastoma Using Machine Learning Technique Based on Quantitative Radiomic Data.

World neurosurgery
OBJECTIVE: Isocitrate dehydrogenase 1 (IDH1) mutation status is an independent favorable prognostic factor for glioblastoma (GBM) and is usually determined by sequencing or immunohistochemistry. An accurate prediction of IDH1 mutation status via noni...

Radiomics-based machine learning methods for isocitrate dehydrogenase genotype prediction of diffuse gliomas.

Journal of cancer research and clinical oncology
PURPOSE: Reliable and accurate predictive models are necessary to drive the success of radiomics. Our aim was to identify the optimal radiomics-based machine learning method for isocitrate dehydrogenase (IDH) genotype prediction in diffuse gliomas.

An investigation of the effect of fat suppression and dimensionality on the accuracy of breast MRI segmentation using U-nets.

Medical physics
PURPOSE: Accurate segmentation of the breast is required for breast density estimation and the assessment of background parenchymal enhancement, both of which have been shown to be related to breast cancer risk. The MRI breast segmentation task is ch...

Automated selection of myocardial inversion time with a convolutional neural network: Spatial temporal ensemble myocardium inversion network (STEMI-NET).

Magnetic resonance in medicine
PURPOSE: Delayed enhancement imaging is an essential component of cardiac MRI, which is used widely for the evaluation of myocardial scar and viability. The selection of an optimal inversion time (TI) or null point (TI ) to suppress the background my...

Dose evaluation of MRI-based synthetic CT generated using a machine learning method for prostate cancer radiotherapy.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists
Magnetic resonance imaging (MRI)-only radiotherapy treatment planning is attractive since MRI provides superior soft tissue contrast over computed tomographies (CTs), without the ionizing radiation exposure. However, it requires the generation of a s...