相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。PI-RADS 3 Lesions: Role of Prostate MRI Texture Analysis in the Identification of Prostate Cancer
Dario Giambelluca et al.
CURRENT PROBLEMS IN DIAGNOSTIC RADIOLOGY (2021)
Deep learning approach for the segmentation of aneurysmal ascending aorta
Albert Comelli et al.
BIOMEDICAL ENGINEERING LETTERS (2021)
Development of a new fully three-dimensional methodology for tumours delineation in functional images
Albert Comelli et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2020)
A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method
Alessandro Stefano et al.
BMC BIOINFORMATICS (2020)
Fully 3D Active Surface with Machine Learning for PET Image Segmentation
Albert Comelli
JOURNAL OF IMAGING (2020)
Lung Segmentation on High-Resolution Computerized Tomography Images Using Deep Learning: A Preliminary Step for Radiomics Studies
Albert Comelli et al.
JOURNAL OF IMAGING (2020)
Correlation between Prostatitis, Benign Prostatic Hyperplasia and Prostate Cancer: A systematic review and Meta-analysis
Lei Zhang et al.
JOURNAL OF CANCER (2020)
Artificial Intelligence in Cardiovascular Imaging JACC State-of-the-Art Review
Damini Dey et al.
JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY (2019)
Clinically significant prostate cancer detection on MRI: A radiomic shape features study
Renato Cuocolo et al.
EUROPEAN JOURNAL OF RADIOLOGY (2019)
Prostate MRI technical parameters standardization: A systematic review on adherence to PI-RADSv2 acquisition protocol
Renato Cuocolo et al.
EUROPEAN JOURNAL OF RADIOLOGY (2019)
Robustness and Reproducibility of Radiomics in Magnetic Resonance Imaging A Phantom Study
Bettina Baessler et al.
INVESTIGATIVE RADIOLOGY (2019)
Integrated 3D anatomical model for automatic myocardial segmentation in cardiac CT imagery
N. Dahiya et al.
COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION (2019)
Machine learning applications in prostate cancer magnetic resonance imaging
Renato Cuocolo et al.
EUROPEAN RADIOLOGY EXPERIMENTAL (2019)
A survey on deep learning techniques for image and video semantic segmentation
Alberto Garcia-Garcia et al.
APPLIED SOFT COMPUTING (2018)
ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation
Eduardo Romera et al.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2018)
A smart and operator independent system to delineate tumours in Positron Emission Tomography scans
Albert Comelli et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2018)
Parameters Influencing PET Imaging Features: A Phantom Study with Irregular and Heterogeneous Synthetic Lesions
Francesca Gallivanone et al.
CONTRAST MEDIA & MOLECULAR IMAGING (2018)
A survey on deep learning in medical image analysis
Geert Litjens et al.
MEDICAL IMAGE ANALYSIS (2017)
Computational imaging reveals shape differences between normal and malignant prostates on MRI
Mirabela Rusu et al.
SCIENTIFIC REPORTS (2017)
A fully automatic method for biological target volume segmentation of brain metastases
Alessandro Stefano et al.
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY (2016)
A review on segmentation of positron emission tomography images
Brent Foster et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2014)
Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge
Geert Litjens et al.
MEDICAL IMAGE ANALYSIS (2014)
Fully Automated Prostate Segmentation on MRI: Comparison With Manual Segmentation Methods and Specimen Volumes
Baris Turkbey et al.
AMERICAN JOURNAL OF ROENTGENOLOGY (2013)
High-intensity focused ultrasound plus concomitant radiotherapy: a new weapon in oncology?
Giovanni Borasi et al.
JOURNAL OF THERAPEUTIC ULTRASOUND (2013)
A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images
Soumya Ghose et al.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2012)
Simultaneous truth and performance level estimation (STAPLE): An algorithm for the validation of image segmentation
SK Warfield et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2004)
Fast approximate energy minimization via graph cuts
Y Boykov et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2001)