AIMC Topic: Tomography, X-Ray Computed

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Segmenting the Brain Surface From CT Images With Artifacts Using Locally Oriented Appearance and Dictionary Learning.

IEEE transactions on medical imaging
The accurate segmentation of the brain surface in post-surgical computed tomography (CT) images is critical for image-guided neurosurgical procedures in epilepsy patients. Following surgical implantation of intracranial electrodes, surgeons require a...

Pelvic Organ Segmentation Using Distinctive Curve Guided Fully Convolutional Networks.

IEEE transactions on medical imaging
Accurate segmentation of pelvic organs (i.e., prostate, bladder, and rectum) from CT image is crucial for effective prostate cancer radiotherapy. However, it is a challenging task due to: 1) low soft tissue contrast in CT images and 2) large shape an...

Traditional machine learning for limited angle tomography.

International journal of computer assisted radiology and surgery
PURPOSE: The application of traditional machine learning techniques, in the form of regression models based on conventional, "hand-crafted" features, to artifact reduction in limited angle tomography is investigated.

Robust liver vessel extraction using 3D U-Net with variant dice loss function.

Computers in biology and medicine
PURPOSE: Liver vessel extraction from CT images is essential in liver surgical planning. Liver vessel segmentation is difficult due to the complex vessel structures, and even expert manual annotations contain unlabeled vessels. This paper presents an...

MRI-based treatment planning for brain stereotactic radiosurgery: Dosimetric validation of a learning-based pseudo-CT generation method.

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 without ionizing radiation compared with computed tomography (CT). However, it requires the generation of pseudo CT f...

Automated prediction of dosimetric eligibility of patients with prostate cancer undergoing intensity-modulated radiation therapy using a convolutional neural network.

Radiological physics and technology
The quality of radiotherapy has greatly improved due to the high precision achieved by intensity-modulated radiation therapy (IMRT). Studies have been conducted to increase the quality of planning and reduce the costs associated with planning through...

Identifying epidermal growth factor receptor mutation status in patients with lung adenocarcinoma by three-dimensional convolutional neural networks.

The British journal of radiology
OBJECTIVE:: Genetic phenotype plays a central role in making treatment decisions of lung adenocarcinoma, especially the tyrosine-kinase-inhibitors-sensitive mutations of the epidermal growth factor receptor (EGFR) gene. We constructed three-dimension...

Automated deep-neural-network surveillance of cranial images for acute neurologic events.

Nature medicine
Rapid diagnosis and treatment of acute neurological illnesses such as stroke, hemorrhage, and hydrocephalus are critical to achieving positive outcomes and preserving neurologic function-'time is brain'. Although these disorders are often recognizabl...

Automated classification of osteomeatal complex inflammation on computed tomography using convolutional neural networks.

International forum of allergy & rhinology
BACKGROUND: Convolutional neural networks (CNNs) are advanced artificial intelligence algorithms well suited to image classification tasks with variable features. These have been used to great effect in various real-world applications including handw...

FissureNet: A Deep Learning Approach For Pulmonary Fissure Detection in CT Images.

IEEE transactions on medical imaging
Pulmonary fissure detection in computed tomography (CT) is a critical component for automatic lobar segmentation. The majority of fissure detection methods use feature descriptors that are hand-crafted, low-level, and have local spatial extent. The d...