AIMC Topic: Image Processing, Computer-Assisted

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Parallel imaging in time-of-flight magnetic resonance angiography using deep multistream convolutional neural networks.

Magnetic resonance in medicine
PURPOSE: To develop and evaluate a method of parallel imaging time-of-flight (TOF) MRA using deep multistream convolutional neural networks (CNNs).

Incorporation of a spectral model in a convolutional neural network for accelerated spectral fitting.

Magnetic resonance in medicine
PURPOSE: MRSI has shown great promise in the detection and monitoring of neurologic pathologies such as tumor. A necessary component of data processing includes the quantitation of each metabolite, typically done through fitting a model of the spectr...

Recurrent inference machines for reconstructing heterogeneous MRI data.

Medical image analysis
Deep learning allows for accelerated magnetic resonance image (MRI) reconstruction, thereby shortening measurement times. Rather than using sparsifying transforms, a prerequisite in Compressed Sensing (CS), suitable MRI prior distributions are learne...

Prostate cancer classification with multiparametric MRI transfer learning model.

Medical physics
PURPOSE: Prostate cancer classification has a significant impact on the prognosis and treatment planning of patients. Currently, this classification is based on the Gleason score analysis of biopsied tissues, which is neither accurate nor risk free. ...

PIXER: an automated particle-selection method based on segmentation using a deep neural network.

BMC bioinformatics
BACKGROUND: Cryo-electron microscopy (cryo-EM) has become a widely used tool for determining the structures of proteins and macromolecular complexes. To acquire the input for single-particle cryo-EM reconstruction, researchers must select hundreds of...

A dataset of laryngeal endoscopic images with comparative study on convolution neural network-based semantic segmentation.

International journal of computer assisted radiology and surgery
PURPOSE: Automated segmentation of anatomical structures in medical image analysis is a prerequisite for autonomous diagnosis as well as various computer- and robot-aided interventions. Recent methods based on deep convolutional neural networks (CNN)...

Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion.

Medical physics
PURPOSE: We propose a deep learning-based approach to breast mass classification in sonography and compare it with the assessment of four experienced radiologists employing breast imaging reporting and data system 4th edition lexicon and assessment p...

Spinal cord detection in planning CT for radiotherapy through adaptive template matching, IMSLIC and convolutional neural networks.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The spinal cord is a very important organ that must be protected in treatments of radiotherapy (RT), considered an organ at risk (OAR). Excess rays associated with the spinal cord can cause irreversible diseases in patients ...

Querying Representative and Informative Super-Pixels for Filament Segmentation in Bioimages.

IEEE/ACM transactions on computational biology and bioinformatics
Segmenting bioimage based filaments is a critical step in a wide range of applications, including neuron reconstruction and blood vessel tracing. To achieve an acceptable segmentation performance, most of the existing methods need to annotate amounts...

Application of convolutional neural networks for classification of adult mosquitoes in the field.

PloS one
Dengue, chikungunya and Zika are arboviruses transmitted by mosquitos of the genus Aedes and have caused several outbreaks in world over the past ten years. Morphological identification of mosquitos is currently restricted due to the small number of ...