AIMC Topic: Image Processing, Computer-Assisted

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Automated brain tumour segmentation from multi-modality magnetic resonance imaging data based on new particle swarm optimisation segmentation method.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Segmentation of brain tumours is a complex problem in medical image processing and analysis. It is a time-consuming and error-prone task. Therefore, computer-aided detection systems need to be developed to decrease physicians' workload an...

Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning.

European journal of nuclear medicine and molecular imaging
PURPOSE: Attenuation correction and scatter compensation (AC/SC) are two main steps toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI systems. These can be effectively tackled via deep learning (DL) methods. However, t...

X-ray CT image denoising with MINF: A modularized iterative network framework for data from multiple dose levels.

Computers in biology and medicine
In clinical applications, multi-dose scan protocols will cause the noise levels of computed tomography (CT) images to fluctuate widely. The popular low-dose CT (LDCT) denoising network outputs denoised images through an end-to-end mapping between an ...

Deep learning-based image reconstruction improves radiologic evaluation of pituitary axis and cavernous sinus invasion in pituitary adenoma.

European journal of radiology
PURPOSE: To compare performance of 1-mm deep learning reconstruction (DLR) with 3-mm routine MRI imaging for the delineation of pituitary axis and identification of cavernous sinus invasion for pituitary macroadenoma.

Structure-preserved meta-learning uniting network for improving low-dose CT quality.

Physics in medicine and biology
Deep neural network (DNN) based methods have shown promising performances for low-dose computed tomography (LDCT) imaging. However, most of the DNN-based methods are trained on simulated labeled datasets, and the low-dose simulation algorithms are us...

Developing an integrated approach based on geographic object-based image analysis and convolutional neural network for volcanic and glacial landforms mapping.

Scientific reports
Rapid detection and mapping of landforms are crucially important to improve our understanding of past and presently active processes across the earth, especially, in complex and dynamic volcanoes. Traditional landform modeling approaches are labor-in...

Fully automatic tumor segmentation of breast ultrasound images with deep learning.

Journal of applied clinical medical physics
BACKGROUND: Breast ultrasound (BUS) imaging is one of the most prevalent approaches for the detection of breast cancers. Tumor segmentation of BUS images can facilitate doctors in localizing tumors and is a necessary step for computer-aided diagnosis...

A multi-perspective information aggregation network for automated-staging detection of nasopharyngeal carcinoma.

Physics in medicine and biology
Accurate-staging is important when planning personalized radiotherapy. However,-staging via manual slice-by-slice inspection is time-consuming while tumor sizes and shapes are heterogeneous, and junior physicians find such inspection challenging. Wit...

Impact of deep learning image reconstructions (DLIR) on coronary artery calcium quantification.

European radiology
BACKGROUND: Deep learning image reconstructions (DLIR) have been recently introduced as an alternative to filtered back projection (FBP) and iterative reconstruction (IR) algorithms for computed tomography (CT) image reconstruction. The aim of this s...

Improving accelerated MRI by deep learning with sparsified complex data.

Magnetic resonance in medicine
PURPOSE: To obtain high-quality accelerated MR images with complex-valued reconstruction from undersampled k-space data.