AIMC Topic: Image Processing, Computer-Assisted

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Deep learning image reconstruction for quality assessment of iodine concentration in computed tomography: A phantom study.

Journal of X-ray science and technology
BACKGROUND: Recently, deep learning reconstruction (DLR) technology aiming to improve image quality with minimal radiation dose has been applied not only to pediatric scans, but also to computed tomography angiography (CTA).

Research on Segmentation Technology in Lung Cancer Radiotherapy Based on Deep Learning.

Current medical imaging
BACKGROUND: Lung cancer has the highest mortality rate among cancers. Radiation therapy (RT) is one of the most effective therapies for lung cancer. The correct segmentation of lung tumors (LTs) and organs at risk (OARs) is the cornerstone of success...

Liver fat analysis using optimized support vector machine with support vector regression.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Fatty liver disease is a common condition caused by excess fat in the liver. It consists of two types: Alcoholic Fatty Liver Disease, also called alcoholic steatohepatitis, and Non-Alcoholic Fatty Liver Disease (NAFLD). As per epidemiolog...

Image restoration for blurry optical images caused by photon diffusion with deep learning.

Journal of the Optical Society of America. A, Optics, image science, and vision
Optical macroscopic imaging techniques have shown great significance in the investigations of biomedical issues by revealing structural or functional information of living bodies through the detection of visible or near-infrared light derived from di...

Segmentation of Clinical Target Volume From CT Images for Cervical Cancer Using Deep Learning.

Technology in cancer research & treatment
Segmentation of clinical target volume (CTV) from CT images is critical for cervical cancer brachytherapy, but this task is time-consuming, laborious, and not reproducible. In this work, we aim to propose an end-to-end model to segment CTV for cervi...

An end-to-end multi-task system of automatic lesion detection and anatomical localization in whole-body bone scintigraphy by deep learning.

Bioinformatics (Oxford, England)
SUMMARY: Limited by spatial resolution and visual contrast, bone scintigraphy interpretation is susceptible to subjective factors, which considerably affects the accuracy and repeatability of lesion detection and anatomical localization. In this work...

Review on Deep Learning Methodologies in Medical Image Restoration and Segmentation.

Current medical imaging
This paper comprehensively reviews two major image processing tasks, such as restoration and segmentation in the medical field, from a deep learning perspective. These processes are essential because restoration removes noise and segmentation extract...

[A generative adversarial network-based unsupervised domain adaptation method for magnetic resonance image segmentation].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Intelligent medical image segmentation methods have been rapidly developed and applied, while a significant challenge is domain shift. That is, the segmentation performance degrades due to distribution differences between the source domain and the ta...

Intelligent imaging: Applications of machine learning and deep learning in radiology.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
Artificial intelligence (AI) in radiology is transforming medical image analysis. While applications in triaging for priority reporting and radiomic feature analysis have been widely reported, perhaps the most important applications lie in noise redu...