AIMC Topic: Tomography, X-Ray Computed

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Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation.

Medical image analysis
Accurate lung nodule segmentation from computed tomography (CT) images is of great importance for image-driven lung cancer analysis. However, the heterogeneity of lung nodules and the presence of similar visual characteristics between nodules and the...

Method of Improved Fuzzy Contrast Combined Adaptive Threshold in NSCT for Medical Image Enhancement.

BioMed research international
Noises and artifacts are introduced to medical images due to acquisition techniques and systems. This interference leads to low contrast and distortion in images, which not only impacts the effectiveness of the medical image but also seriously affect...

Feature fusion for lung nodule classification.

International journal of computer assisted radiology and surgery
PURPOSE: This article examines feature-based nodule description for the purpose of nodule classification in chest computed tomography scanning.

Fully automatic detection of lung nodules in CT images using a hybrid feature set.

Medical physics
PURPOSE: The aim of this study was to develop a novel technique for lung nodule detection using an optimized feature set. This feature set has been achieved after rigorous experimentation, which has helped in reducing the false positives significantl...

Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network.

IEEE transactions on medical imaging
Given the potential risk of X-ray radiation to the patient, low-dose CT has attracted a considerable interest in the medical imaging field. Currently, the main stream low-dose CT methods include vendor-specific sinogram domain filtration and iterativ...

An intelligent support system for automatic detection of cerebral vascular accidents from brain CT images.

Computer methods and programs in biomedicine
OBJECTIVE: This paper presents a Radial Basis Functions Neural Network (RBFNN) based detection system, for automatic identification of Cerebral Vascular Accidents (CVA) through analysis of Computed Tomographic (CT) images.

Spotting L3 slice in CT scans using deep convolutional network and transfer learning.

Computers in biology and medicine
In this article, we present a complete automated system for spotting a particular slice in a complete 3D Computed Tomography exam (CT scan). Our approach does not require any assumptions on which part of the patient's body is covered by the scan. It ...

Organ Location Determination and Contour Sparse Representation for Multiorgan Segmentation.

IEEE journal of biomedical and health informatics
Organ segmentation on computed tomography (CT) images is of great importance in medical diagnoses and treatment. This paper proposes organ location determination and contour sparse representation methods (OLD-CSR) for multiorgan segmentation (liver, ...

Automatic Segmentation of Kidneys using Deep Learning for Total Kidney Volume Quantification in Autosomal Dominant Polycystic Kidney Disease.

Scientific reports
Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most common inherited disorder of the kidneys. It is characterized by enlargement of the kidneys caused by progressive development of renal cysts, and thus assessment of total kidney volume ...

Deep monocular 3D reconstruction for assisted navigation in bronchoscopy.

International journal of computer assisted radiology and surgery
PURPOSE: In bronchoschopy, computer vision systems for navigation assistance are an attractive low-cost solution to guide the endoscopist to target peripheral lesions for biopsy and histological analysis. We propose a decoupled deep learning architec...