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Abdomen

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Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation.

Physics in medicine and biology
Segmentation of liver in abdominal computed tomography (CT) is an important step for radiation therapy planning of hepatocellular carcinoma. Practically, a fully automatic segmentation of liver remains challenging because of low soft tissue contrast ...

Reducing Navigators in Free-Breathing Abdominal MRI via Temporal Interpolation Using Convolutional Neural Networks.

IEEE transactions on medical imaging
Navigated 2-D multi-slice dynamic magnetic resonance imaging (MRI) acquisitions are essential for MR guided therapies. This technique yields time-resolved volumetric images during free-breathing, which are ideal for visualizing and quantifying breath...

Detecting Evidence of Intra-abdominal Surgical Site Infections from Radiology Reports Using Natural Language Processing.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Free-text reports in electronic health records (EHRs) contain medically significant information - signs, symptoms, findings, diagnoses - recorded by clinicians during patient encounters. These reports contain rich clinical information which can be le...

Efficient organ localization using multi-label convolutional neural networks in thorax-abdomen CT scans.

Physics in medicine and biology
Automatic localization of organs and other structures in medical images is an important preprocessing step that can improve and speed up other algorithms such as organ segmentation, lesion detection, and registration. This work presents an efficient ...

NiftyNet: a deep-learning platform for medical imaging.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functional...

Respiratory motion correction for free-breathing 3D abdominal MRI using CNN-based image registration: a feasibility study.

The British journal of radiology
OBJECTIVE: Free-breathing abdomen imaging requires non-rigid motion registration of unavoidable respiratory motion in three-dimensional undersampled data sets. In this work, we introduce an image registration method based on the convolutional neural ...

Iterative Low-Dose CT Reconstruction With Priors Trained by Artificial Neural Network.

IEEE transactions on medical imaging
Dose reduction in computed tomography (CT) is essential for decreasing radiation risk in clinical applications. Iterative reconstruction algorithms are one of the most promising way to compensate for the increased noise due to reduction of photon flu...

Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network.

IEEE transactions on medical imaging
Given the potential risk of X-ray radiation to the patient, low-dose CT has attracted a considerable interest in the medical imaging field. Currently, the main stream low-dose CT methods include vendor-specific sinogram domain filtration and iterativ...

A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images.

Computer methods and programs in biomedicine
Accurately assessment of adipose tissue volume inside a human body plays an important role in predicting disease or cancer risk, diagnosis and prognosis. In order to overcome limitation of using only one subjectively selected CT image slice to estima...

Initial Experience Using a Telerobotic Ultrasound System for Adult Abdominal Sonography.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
PURPOSE: The study sought to assess the feasibility of performing adult abdominal examinations using a telerobotic ultrasound system in which radiologists or sonographers can control fine movements of a transducer and all ultrasound settings from a r...