AIMC Topic: Image Processing, Computer-Assisted

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High quality imaging from sparsely sampled computed tomography data with deep learning and wavelet transform in various domains.

Medical physics
PURPOSE: Sparsely sampled computed tomography (CT) has been attracting attention as a technique that can reduce the high radiation dose of conventional CT. In general, iterative reconstruction techniques have been applied to sparsely sampled CT to re...

A deep learning approach to automatic detection of early glaucoma from visual fields.

PloS one
PURPOSE: To investigate the suitability of multi-scale spatial information in 30o visual fields (VF), computed from a Convolutional Neural Network (CNN) classifier, for early-glaucoma vs. control discrimination.

Deep-learned placental vessel segmentation for intraoperative video enhancement in fetoscopic surgery.

International journal of computer assisted radiology and surgery
INTRODUCTION: Twin-to-twin transfusion syndrome (TTTS) is a potentially lethal condition that affects pregnancies in which twins share a single placenta. The definitive treatment for TTTS is fetoscopic laser photocoagulation, a procedure in which pla...

Metal artifact reduction on cervical CT images by deep residual learning.

Biomedical engineering online
BACKGROUND: Cervical cancer is the fifth most common cancer among women, which is the third leading cause of cancer death in women worldwide. Brachytherapy is the most effective treatment for cervical cancer. For brachytherapy, computed tomography (C...

Hyperspectral Tissue Image Segmentation Using Semi-Supervised NMF and Hierarchical Clustering.

IEEE transactions on medical imaging
Hyperspectral imaging (HSI) of tissue samples in the mid-infrared (mid-IR) range provides spectro-chemical and tissue structure information at sub-cellular spatial resolution. Disease states can be directly assessed by analyzing the mid-IR spectra of...

Deep Semi Supervised Generative Learning for Automated Tumor Proportion Scoring on NSCLC Tissue Needle Biopsies.

Scientific reports
The level of PD-L1 expression in immunohistochemistry (IHC) assays is a key biomarker for the identification of Non-Small-Cell-Lung-Cancer (NSCLC) patients that may respond to anti PD-1/PD-L1 treatments. The quantification of PD-L1 expression current...

Brain CT and MRI medical image fusion using convolutional neural networks and a dual-channel spiking cortical model.

Medical & biological engineering & computing
The aim of medical image fusion is to improve the clinical diagnosis accuracy, so the fused image is generated by preserving salient features and details of the source images. This paper designs a novel fusion scheme for CT and MRI medical images bas...

Precision immunoprofiling by image analysis and artificial intelligence.

Virchows Archiv : an international journal of pathology
Clinical success of immunotherapy is driving the need for new prognostic and predictive assays to inform patient selection and stratification. This requirement can be met by a combination of computational pathology and artificial intelligence. Here, ...

Identifying Brain Networks at Multiple Time Scales via Deep Recurrent Neural Network.

IEEE journal of biomedical and health informatics
For decades, task functional magnetic resonance imaging has been a powerful noninvasive tool to explore the organizational architecture of human brain function. Researchers have developed a variety of brain network analysis methods for task fMRI data...

Supervised machine learning quality control for magnetic resonance artifacts in neonatal data sets.

Human brain mapping
Quality control (QC) of brain magnetic resonance images (MRI) is an important process requiring a significant amount of manual inspection. Major artifacts, such as severe subject motion, are easy to identify to naïve observers but lack automated iden...