AIMC Topic: Radiography, Abdominal

Clear Filters Showing 61 to 70 of 93 articles

Liver vessel segmentation based on extreme learning machine.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) May 4, 2016
Liver-vessel segmentation plays an important role in vessel structure analysis for liver surgical planning. This paper presents a liver-vessel segmentation method based on extreme learning machine (ELM). Firstly, an anisotropic filter is used to remo...
Radiography, Abdominal Anisotropy Tomography, X-Ray Computed Reproducibility of Results Sensitivity and Specificity Imaging, Three-Dimensional Diffusion Radiotherapy Planning, Computer-Assisted False Positive Reactions Image Processing, Computer-Assisted Liver Humans Machine Learning Algorithms
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3D Fast Automatic Segmentation of Kidney Based on Modified AAM and Random Forest.

IEEE transactions on medical imaging Jan 5, 2016
In this paper, a fully automatic method is proposed to segment the kidney into multiple components: renal cortex, renal column, renal medulla and renal pelvis, in clinical 3D CT abdominal images. The proposed fast automatic segmentation method of kid...
Radiography, Abdominal Imaging, Three-Dimensional Kidney Models, Theoretical Decision Trees Humans Machine Learning Tomography, X-Ray Computed
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Predicting High Imaging Utilization Based on Initial Radiology Reports: A Feasibility Study of Machine Learning.

Academic radiology Oct 27, 2015
RATIONALE AND OBJECTIVES: Imaging utilization has significantly increased over the last two decades, and is only recently showing signs of moderating. To help healthcare providers identify patients at risk for high imaging utilization, we developed a...
Male Machine Learning Tomography, X-Ray Computed Support Vector Machine Humans Unnecessary Procedures Adult Feasibility Studies Radiography, Abdominal Sensitivity and Specificity Research Report
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Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors.

Medical image analysis Jul 4, 2015
This paper addresses the automated segmentation of multiple organs in upper abdominal computed tomography (CT) data. The aim of our study is to develop methods to effectively construct the conditional priors and use their prediction power for more ac...
Humans Algorithms Tomography, X-Ray Computed Reproducibility of Results Unsupervised Machine Learning Viscera Sensitivity and Specificity Radiographic Image Interpretation, Computer-Assisted Radiographic Image Enhancement Imaging, Three-Dimensional Pattern Recognition, Automated Radiography, Abdominal
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Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning.

Medical image analysis May 21, 2015
Abdominal segmentation on clinically acquired computed tomography (CT) has been a challenging problem given the inter-subject variance of human abdomens and complex 3-D relationships among organs. Multi-atlas segmentation (MAS) provides a potentially...
Radiographic Image Interpretation, Computer-Assisted Pattern Recognition, Automated Radiographic Image Enhancement Radiography, Abdominal Tomography, X-Ray Computed Reproducibility of Results Sensitivity and Specificity Liver Neoplasms Humans Machine Learning Algorithms
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A natural language processing pipeline for pairing measurements uniquely across free-text CT reports.

Journal of biomedical informatics Sep 6, 2014
OBJECTIVE: To standardize and objectivize treatment response assessment in oncology, guidelines have been proposed that are driven by radiological measurements, which are typically communicated in free-text reports defying automated processing. We st...
ROC Curve Computational Biology Natural Language Processing Area Under Curve Radiology Information Systems Radiology Software Humans Radiography, Abdominal Radiographic Image Interpretation, Computer-Assisted Algorithms Machine Learning Data Mining Tomography, X-Ray Computed Medical Oncology Reproducibility of Results
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Enhancing Oncological Surveillance Through Large Language Model-Assisted Analysis: A Comparative Study of GPT-4 and Gemini in Evaluating Oncological Issues From Serial Abdominal CT Scan Reports.

Academic radiology May 1, 2025
RATIONALE AND OBJECTIVES: We aimed to compare the capabilities of two leading large language models (LLMs), GPT-4 and Gemini, in analyzing serial radiology reports, to highlight oncological issues that require further clinical attention.
Middle Aged Aged Humans Natural Language Processing Adult Radiology Information Systems Tomography, X-Ray Computed Large Language Models Sensitivity and Specificity Abdominal Neoplasms Female Radiography, Abdominal Male Reproducibility of Results
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Evaluation of SR-DLR in low-dose abdominal CT: superior image quality and noise reduction.

Abdominal radiology (New York) May 1, 2025
OBJECTIVES: To evaluate the effectiveness of super-resolution deep learning reconstruction (SR-DLR) in low-dose abdominal computed tomography (CT) imaging compared with hybrid iterative reconstruction (HIR) and conventional deep learning reconstructi...
Adult Male Middle Aged Deep Learning Tomography, X-Ray Computed Radiation Dosage Radiographic Image Interpretation, Computer-Assisted Humans Signal-To-Noise Ratio Female Artifacts Algorithms Radiography, Abdominal Aged Retrospective Studies Aged, 80 and over
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Feasibility of deep learning algorithm in diagnosing lumbar central canal stenosis using abdominal CT.

Skeletal radiology May 1, 2025
OBJECTIVE: To develop a deep learning algorithm for diagnosing lumbar central canal stenosis (LCCS) using abdominal CT (ACT) and lumbar spine CT (LCT).
Retrospective Studies Tomography, X-Ray Computed Female Aged Humans Algorithms Deep Learning Radiographic Image Interpretation, Computer-Assisted Lumbar Vertebrae Feasibility Studies Spinal Stenosis Aged, 80 and over Sensitivity and Specificity Male Middle Aged Radiography, Abdominal
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Generalizability of AI-based image segmentation and centering estimation algorithm: a multi-region, multi-center, and multi-scanner study.

Radiation protection dosimetry Apr 22, 2025
We created and validated an open-access AI algorithm (AIc) for assessing image segmentation and patient centering in a multi-body-region, multi-center, and multi-scanner study. Our study included 825 head, chest, and abdomen-pelvis CT from 275 patien...
Female Reproducibility of Results Pelvis Aged Aged, 80 and over Adult Head Radiographic Image Interpretation, Computer-Assisted Humans Male Radiography, Thoracic Artificial Intelligence Middle Aged Radiography, Abdominal Algorithms Image Processing, Computer-Assisted Tomography, X-Ray Computed
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