AIMC Topic: Image Processing, Computer-Assisted

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Machine learning: a useful radiological adjunct in determination of a newly diagnosed glioma's grade and IDH status.

Journal of neuro-oncology
INTRODUCTION: Machine learning methods have been introduced as a computer aided diagnostic tool, with applications to glioma characterisation on MRI. Such an algorithmic approach may provide a useful adjunct for a rapid and accurate diagnosis of a gl...

Weed Growth Stage Estimator Using Deep Convolutional Neural Networks.

Sensors (Basel, Switzerland)
This study outlines a new method of automatically estimating weed species and growth stages (from cotyledon until eight leaves are visible) of in situ images covering 18 weed species or families. Images of weeds growing within a variety of crops were...

Prostate segmentation in MRI using a convolutional neural network architecture and training strategy based on statistical shape models.

International journal of computer assisted radiology and surgery
PURPOSE: Most of the existing convolutional neural network (CNN)-based medical image segmentation methods are based on methods that have originally been developed for segmentation of natural images. Therefore, they largely ignore the differences betw...

3D/2D model-to-image registration by imitation learning for cardiac procedures.

International journal of computer assisted radiology and surgery
PURPOSE: In cardiac interventions, such as cardiac resynchronization therapy (CRT), image guidance can be enhanced by involving preoperative models. Multimodality 3D/2D registration for image guidance, however, remains a significant research challeng...

A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection.

IEEE/ACM transactions on computational biology and bioinformatics
The important role of angiogenesis in cancer development has driven many researchers to investigate the prospects of noninvasive cancer diagnosis based on the technology of contrast-enhanced ultrasound (CEUS) imaging. This paper presents a deep learn...

DRINet for Medical Image Segmentation.

IEEE transactions on medical imaging
Convolutional neural networks (CNNs) have revolutionized medical image analysis over the past few years. The U-Net architecture is one of the most well-known CNN architectures for semantic segmentation and has achieved remarkable successes in many di...

Monitoring tool usage in surgery videos using boosted convolutional and recurrent neural networks.

Medical image analysis
This paper investigates the automatic monitoring of tool usage during a surgery, with potential applications in report generation, surgical training and real-time decision support. Two surgeries are considered: cataract surgery, the most common surgi...

Neural network control of focal position during time-lapse microscopy of cells.

Scientific reports
Live-cell microscopy is quickly becoming an indispensable technique for studying the dynamics of cellular processes. Maintaining the specimen in focus during image acquisition is crucial for high-throughput applications, especially for long experimen...

Optimized generation of high-resolution phantom images using cGAN: Application to quantification of Ki67 breast cancer images.

PloS one
In pathology, Immunohistochemical staining (IHC) of tissue sections is regularly used to diagnose and grade malignant tumors. Typically, IHC stain interpretation is rendered by a trained pathologist using a manual method, which consists of counting e...