AIMC Topic: Pelvis

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Comparison of Deep Learning-Based and Patch-Based Methods for Pseudo-CT Generation in MRI-Based Prostate Dose Planning.

International journal of radiation oncology, biology, physics Sep 7, 2019
PURPOSE: Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) for magnetic resonance imaging (MRI) based dose planning. This study aims to evaluate and compare DLMs (U-Net and generative adversarial network [GAN]) usin...
Magnetic Resonance Imaging Prostatic Neoplasms Prostate Pelvis Tomography, X-Ray Computed Male Deep Learning Radiotherapy, Intensity-Modulated Bone and Bones Humans Urinary Bladder Uncertainty Rectum Femur Head Reference Values Radiotherapy Dosage
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A machine learning-assisted decision-support model to better identify patients with prostate cancer requiring an extended pelvic lymph node dissection.

BJU international Aug 28, 2019
OBJECTIVES: To develop a machine learning (ML)-assisted model to identify candidates for extended pelvic lymph node dissection (ePLND) in prostate cancer by integrating clinical, biopsy, and precisely defined magnetic resonance imaging (MRI) findings...
Humans Machine Learning Magnetic Resonance Imaging Male Lymph Node Excision Prostatic Neoplasms Decision Trees Prostate Middle Aged Pelvis Retrospective Studies Lymph Nodes Support Vector Machine Aged Aged, 80 and over
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Sex estimation: a comparison of techniques based on binary logistic, probit and cumulative probit regression, linear and quadratic discriminant analysis, neural networks, and naïve Bayes classification using ordinal variables.

International journal of legal medicine Aug 23, 2019
The performance of seven classification methods, binary logistic (BLR), probit (PR) and cumulative probit (CPR) regression, linear (LDA) and quadratic (QDA) discriminant analysis, artificial neural networks (ANN), and naïve Bayes classification (NBC)...
Skull Pelvis Sex Determination by Skeleton Statistics as Topic Neural Networks, Computer Machine Learning Algorithms Female Male Logistic Models Humans Bayes Theorem Discriminant Analysis
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mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification.

IEEE transactions on medical imaging Aug 16, 2019
We propose a new method for generating synthetic CT images from modified Dixon (mDixon) MR data. The synthetic CT is used for attenuation correction (AC) when reconstructing PET data on abdomen and pelvis. While MR does not intrinsically contain any ...
Abdomen Support Vector Machine Magnetic Resonance Imaging Fuzzy Logic Cluster Analysis Pelvis Positron-Emission Tomography Humans Tomography, X-Ray Computed Image Processing, Computer-Assisted
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Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment.

Skeletal radiology Aug 8, 2019
OBJECTIVE: To develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We hypothesized that a deep CNN approach would achieve high accuracy when compared to manual segme...
Tomography, X-Ray Computed Female Male Middle Aged Humans Neural Networks, Computer Retrospective Studies Pelvis Datasets as Topic Contrast Media Muscle, Skeletal Adipose Tissue Body Composition
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Is a Drain Needed After Robotic Radical Prostatectomy With or Without Pelvic Lymph Node Dissection? Results of a Single-Center Randomized Clinical Trial.

Journal of endourology Jul 31, 2019
To investigate by means of a randomized clinical trial the safety of no drain in the pelvic cavity after robot-assisted radical prostatectomy (RARP) with or without extended pelvic lymph node dissection (ePLND). From May to December 2016, 112 conse...
Humans Male Robotic Surgical Procedures Lymph Node Excision Pelvis Robotics Prostatectomy Prostatic Neoplasms
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Deep learning approaches using 2D and 3D convolutional neural networks for generating male pelvic synthetic computed tomography from magnetic resonance imaging.

Medical physics Jul 26, 2019
PURPOSE: The improved soft tissue contrast of magnetic resonance imaging (MRI) compared to computed tomography (CT) makes it a useful imaging modality for radiotherapy treatment planning. Even when MR images are acquired for treatment planning, the s...
Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Prostatic Neoplasms Pelvis Aged Aged, 80 and over Male Middle Aged Humans Retrospective Studies Tomography, X-Ray Computed
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Dosimetric study on learning-based cone-beam CT correction in adaptive radiation therapy.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists Apr 1, 2019
INTRODUCTION: Cone-beam CT (CBCT) image quality is important for its quantitative analysis in adaptive radiation therapy. However, due to severe artifacts, the CBCTs are primarily used for verifying patient setup only so far. We have developed a lear...
Quality Improvement Radiotherapy Dosage Humans Radiosurgery Retrospective Studies Machine Learning Pelvis Radiometry Organs at Risk Brain Neoplasms Radiotherapy Planning, Computer-Assisted Cone-Beam Computed Tomography Decision Trees
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Automatic classification of tissues on pelvic MRI based on relaxation times and support vector machine.

PloS one Feb 22, 2019
Tissue segmentation and classification in MRI is a challenging task due to a lack of signal intensity standardization. MRI signal is dependent on the acquisition protocol, the coil profile, the scanner type, etc. While we can compute quantitative phy...
Image Processing, Computer-Assisted Humans Support Vector Machine Prospective Studies Magnetic Resonance Imaging Adult Pelvis Male
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Learning-based CBCT correction using alternating random forest based on auto-context model.

Medical physics Dec 11, 2018
PURPOSE: Quantitative Cone Beam CT (CBCT) imaging is increasing in demand for precise image-guided radiotherapy because it provides a foundation for advanced image-guided techniques, including accurate treatment setup, online tumor delineation, and p...
Radiation Dosage Cone-Beam Computed Tomography Image Processing, Computer-Assisted Pelvis Artifacts Humans Machine Learning Brain
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