Editorial for “Deep Learning Whole‐Gland and Zonal Prostate Segmentation on a Public MRI Dataset”

2021 ◽  
Author(s):  
Iosif A. Mendichovszky
Keyword(s):  
Deep Learning ◽  
Prostate Segmentation

Deep Learning Whole‐Gland and Zonal Prostate Segmentation on a Public MRI Dataset

2021 ◽  
Author(s):  
Renato Cuocolo ◽  
Albert Comelli ◽  
Alessandro Stefano ◽  
Viviana Benfante ◽  
Navdeep Dahiya ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prostate Segmentation

Deep learning-based artificial intelligence applications in prostate MRI: brief summary

2021 ◽  
Author(s):  
Baris Turkbey ◽  
Masoom A. Haider
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Diagnostic Performance ◽  
Lesion Detection ◽  
Western World ◽  
Complex Nature ◽  
Cancer Type ◽  
Prostate Mri ◽  
Prostate Segmentation ◽  
Established Role

Prostate cancer (PCa) is the most common cancer type in males in the Western World. MRI has an established role in diagnosis of PCa through guiding biopsies. Due to multistep complex nature of the MRI-guided PCa diagnosis pathway, diagnostic performance has a big variation. Developing artificial intelligence (AI) models using machine learning, particularly deep learning, has an expanding role in radiology. Specifically, for prostate MRI, several AI approaches have been defined in the literature for prostate segmentation, lesion detection and classification with the aim of improving diagnostic performance and interobserver agreement. In this review article, we summarize the use of radiology applications of AI in prostate MRI.

Automatic Prostate Segmentation using Deep Learning and MR Images

2018 ◽  
Vol 102 (3) ◽  
pp. e379
Author(s):  
Y. Yuan ◽  
W. Qin ◽  
M.K. Buyyounouski ◽  
S.L. Hancock ◽  
H.P. Bagshaw ◽  
...  
Keyword(s):  
Deep Learning ◽  
Mr Images ◽  
Prostate Segmentation

scholarly journals Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging

2021 ◽  
Vol 11 (2) ◽  
pp. 782 ◽  
Author(s):  
Albert Comelli ◽  
Navdeep Dahiya ◽  
Alessandro Stefano ◽  
Federica Vernuccio ◽  
Marzia Portoghese ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Prostate Gland ◽  
Dice Similarity Coefficient ◽  
Processing Unit ◽  
Imaging Features ◽  
Resonance Imaging ◽  
Central Processing ◽  
Prostate Segmentation

Magnetic Resonance Imaging-based prostate segmentation is an essential task for adaptive radiotherapy and for radiomics studies whose purpose is to identify associations between imaging features and patient outcomes. Because manual delineation is a time-consuming task, we present three deep-learning (DL) approaches, namely UNet, efficient neural network (ENet), and efficient residual factorized convNet (ERFNet), whose aim is to tackle the fully-automated, real-time, and 3D delineation process of the prostate gland on T2-weighted MRI. While UNet is used in many biomedical image delineation applications, ENet and ERFNet are mainly applied in self-driving cars to compensate for limited hardware availability while still achieving accurate segmentation. We apply these models to a limited set of 85 manual prostate segmentations using the k-fold validation strategy and the Tversky loss function and we compare their results. We find that ENet and UNet are more accurate than ERFNet, with ENet much faster than UNet. Specifically, ENet obtains a dice similarity coefficient of 90.89% and a segmentation time of about 6 s using central processing unit (CPU) hardware to simulate real clinical conditions where graphics processing unit (GPU) is not always available. In conclusion, ENet could be efficiently applied for prostate delineation even in small image training datasets with potential benefit for patient management personalization.

Automatic MR prostate segmentation by deep learning with holistically-nested networks

2017 ◽  
Author(s):  
Ruida Cheng ◽  
Holger R. Roth ◽  
Nathan Lay ◽  
Le Lu ◽  
Baris Turkbey ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prostate Segmentation

scholarly journals A Review on Prostate Cancer Detection using Deep Learning Techniques

2020 ◽  
Vol 1 (2) ◽  
pp. 26-33
Author(s):  
Narmatha C ◽  
◽  
Surendra Prasad M ◽  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Medical Image Analysis ◽  
Diagnostic Methods ◽  
Convolutional Networks ◽  
Prostate Segmentation ◽  
Learning Techniques ◽  
Imaging Methodology ◽  
Medical Effect

The second most diagnosed disease of men throughout the world is Prostate cancer (PCa). 28% of cancers in men result in the prostate, making PCa and its identification an essential focus in cancer research. Hence, developing effective diagnostic methods for PCa is very significant and has critical medical effect. These methods could improve the advantages of treatment and enhance the patients' survival chance. Imaging plays a significant role in the identification of PCa. Prostate segmentation and classification is a difficult process, and the difficulties fundamentally vary with one imaging methodology then onto the next. For segmentation and classification, deep learning algorithms, specifically convolutional networks, have quickly become an optional technique for medical image analysis. In this survey, various types of imaging modalities utilized for diagnosing PCa is reviewed and researches made on the detection of PCa is analyzed. Most of the researches are done in machine learning based and deep learning based techniques. Based on the results obtained from the analysis of these researches, deep learning based techniques plays a significant and promising part in detecting PCa. Most of the techniques are based on computer aided detection (CAD) systems, which follows preprocessing, segmentation, feature extraction, and classification processes, which yield efficient results in detecting PCa. As a conclusion from the analysis of some recent works, deep learning based techniques are adequate for the detection of PCa.

scholarly journals Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images

2020 ◽  
Vol 47 (6) ◽  
pp. 2413-2426 ◽  
Author(s):  
Nathan Orlando ◽  
Derek J. Gillies ◽  
Igor Gyacskov ◽  
Cesare Romagnoli ◽  
David D’Souza ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transrectal Ultrasound ◽  
Ultrasound Images ◽  
Prostate Segmentation
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