AIMC Topic: Image Processing, Computer-Assisted

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Highly accelerated multishot echo planar imaging through synergistic machine learning and joint reconstruction.

Magnetic resonance in medicine
PURPOSE: To introduce a combined machine learning (ML)- and physics-based image reconstruction framework that enables navigator-free, highly accelerated multishot echo planar imaging (msEPI) and demonstrate its application in high-resolution structur...

Adaptation of CytoProcessor for cervical cancer screening of challenging slides.

Diagnostic cytopathology
BACKGROUND: Current automated cervical cytology screening systems require purchase of a dedicated preparation machine and use of a specific staining protocol. CytoProcessor (DATEXIM, Caen, France) is a new automated system, designed to integrate seam...

Classification of Polar Maps from Cardiac Perfusion Imaging with Graph-Convolutional Neural Networks.

Scientific reports
Myocardial perfusion imaging is a non-invasive imaging technique commonly used for the diagnosis of Coronary Artery Disease and is based on the injection of radiopharmaceutical tracers into the blood stream. The patient's heart is imaged while at res...

Model-free prostate cancer segmentation from dynamic contrast-enhanced MRI with recurrent convolutional networks: A feasibility study.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is a method of temporal imaging that is commonly used to aid in prostate cancer (PCa) diagnosis and staging. Typically, machine learning models designed for the segmentation and detecti...

Topological correction of infant white matter surfaces using anatomically constrained convolutional neural network.

NeuroImage
Reconstruction of accurate cortical surfaces without topological errors (i.e., handles and holes) from infant brain MR images is very important in early brain development studies. However, infant brain MR images typically suffer extremely low tissue ...

A multi-objective optimization approach for brain MRI segmentation using fuzzy entropy clustering and region-based active contour methods.

Magnetic resonance imaging
In this paper, we present a new multi-objective optimization approach for segmentation of Magnetic Resonance Imaging (MRI) of the human brain. The proposed algorithm not only takes advantages but also solves major drawbacks of two well-known compleme...

Brain tumor detection using statistical and machine learning method.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Brain tumor occurs because of anomalous development of cells. It is one of the major reasons of death in adults around the globe. Millions of deaths can be prevented through early detection of brain tumor. Earlier brain tumo...

Variational autoencoder: An unsupervised model for encoding and decoding fMRI activity in visual cortex.

NeuroImage
Goal-driven and feedforward-only convolutional neural networks (CNN) have been shown to be able to predict and decode cortical responses to natural images or videos. Here, we explored an alternative deep neural network, variational auto-encoder (VAE)...

Combining convolutional neural networks and star convex cuts for fast whole spine vertebra segmentation in MRI.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: We propose an automatic approach for fast vertebral body segmentation in three-dimensional magnetic resonance images of the whole spine. Previous works are limited to the lower thoracolumbar section and often take minutes to...

ONCOhabitats: A system for glioblastoma heterogeneity assessment through MRI.

International journal of medical informatics
BACKGROUND: Neuroimaging analysis is currently crucial for an early assessment of glioblastoma, to help improving treatment and tumor follow-up. To this end, multiple functional and morphological MRI sequences are usually employed, requiring the deve...