AIMC Topic: Image Processing, Computer-Assisted

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A Deep Learning Framework for Segmenting Brain Tumors Using MRI and Synthetically Generated CT Images.

Sensors (Basel, Switzerland)
Multi-modal three-dimensional (3-D) image segmentation is used in many medical applications, such as disease diagnosis, treatment planning, and image-guided surgery. Although multi-modal images provide information that no single image modality alone ...

Attention modulates neural representation to render reconstructions according to subjective appearance.

Communications biology
Stimulus images can be reconstructed from visual cortical activity. However, our perception of stimuli is shaped by both stimulus-induced and top-down processes, and it is unclear whether and how reconstructions reflect top-down aspects of perception...

Generative adversarial network enables rapid and robust fluorescence lifetime image analysis in live cells.

Communications biology
Fluorescence lifetime imaging microscopy (FLIM) is a powerful tool to quantify molecular compositions and study molecular states in complex cellular environment as the lifetime readings are not biased by fluorophore concentration or excitation power....

Shape Prediction of Nasal Bones by Digital 2D-Photogrammetry of the Nose Based on Convolution and Back-Propagation Neural Network.

Computational and mathematical methods in medicine
In rhinoplasty, it is necessary to consider the correlation between the anthropometric indicators of the nasal bone, so that it prevents surgical complications and enhances the patient's satisfaction. The penetrating form of high-energy electromagnet...

Angular super-resolution in X-ray projection radiography using deep neural network: Implementation on rotational angiography.

Biomedical journal
BACKGROUND: Rotational angiography acquires radiographs at multiple projection angles to demonstrate superimposed vasculature. However, this comes at the expense of the inherent risk of increased ionizing radiation. In this paper, building upon a suc...

Functional cortical localization of tongue movements using corticokinematic coherence with a deep learning-assisted motion capture system.

Scientific reports
Corticokinematic coherence (CKC) between magnetoencephalographic and movement signals using an accelerometer is useful for the functional localization of the primary sensorimotor cortex (SM1). However, it is difficult to determine the tongue CKC beca...

Coarse-to-fine airway segmentation using multi information fusion network and CNN-based region growing.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Automatic airway segmentation from chest computed tomography (CT) scans plays an important role in pulmonary disease diagnosis and computer-assisted therapy. However, low contrast at peripheral branches and complex tree-lik...

Generalizing deep learning brain segmentation for skull removal and intracranial measurements.

Magnetic resonance imaging
Total intracranial volume (TICV) and posterior fossa volume (PFV) are essential covariates for brain volumetric analyses with structural magnetic resonance imaging (MRI). Detailed whole brain segmentation provides a non-invasive way to measure brain ...

Automated segmentation of articular disc of the temporomandibular joint on magnetic resonance images using deep learning.

Scientific reports
Temporomandibular disorders are typically accompanied by a number of clinical manifestations that involve pain and dysfunction of the masticatory muscles and temporomandibular joint. The most important subgroup of articular abnormalities in patients ...