AIMC Topic: Prostate

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Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images.

Medical image analysis Jul 15, 2019
The goal of this work was to develop a method for accurate and robust automatic segmentation of the prostate clinical target volume in transrectal ultrasound (TRUS) images for brachytherapy. These images can be difficult to segment because of weak or...
Humans Prostatic Neoplasms Prostate Artifacts Pattern Recognition, Automated Brachytherapy Ultrasonography Image Processing, Computer-Assisted Anatomic Landmarks Neural Networks, Computer Male Deep Learning
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3D APA-Net: 3D Adversarial Pyramid Anisotropic Convolutional Network for Prostate Segmentation in MR Images.

IEEE transactions on medical imaging Jul 11, 2019
Accurate and reliable segmentation of the prostate gland using magnetic resonance (MR) imaging has critical importance for the diagnosis and treatment of prostate diseases, especially prostate cancer. Although many automated segmentation approaches, ...
Imaging, Three-Dimensional Image Processing, Computer-Assisted Magnetic Resonance Imaging Neural Networks, Computer Algorithms Humans Male Prostate Anisotropy
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Editorial Comment to Robot-assisted single-port radical prostatectomy: A phase 1 clinical study.

International journal of urology : official journal of the Japanese Urological Association Jul 8, 2019
Male Robotics Humans Prostate Prostatectomy
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Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks.

IEEE journal of biomedical and health informatics Jun 14, 2019
In this paper, we propose a bi-modality medical image synthesis approach based on sequential generative adversarial network (GAN) and semi-supervised learning. Our approach consists of two generative modules that synthesize images of the two modaliti...
Neural Networks, Computer Brain Male Humans Prostate Retina Supervised Machine Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Multimodal Imaging
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Ultrasound prostate segmentation based on multidirectional deeply supervised V-Net.

Medical physics May 29, 2019
PURPOSE: Transrectal ultrasound (TRUS) is a versatile and real-time imaging modality that is commonly used in image-guided prostate cancer interventions (e.g., biopsy and brachytherapy). Accurate segmentation of the prostate is key to biopsy needle p...
Supervised Machine Learning Observer Variation Humans Ultrasonography Male Deep Learning Image Processing, Computer-Assisted Prostate
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Model-free prostate cancer segmentation from dynamic contrast-enhanced MRI with recurrent convolutional networks: A feasibility study.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society May 19, 2019
Dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is a method of temporal imaging that is commonly used to aid in prostate cancer (PCa) diagnosis and staging. Typically, machine learning models designed for the segmentation and detecti...
Prostatic Neoplasms Prostate Feasibility Studies Image Processing, Computer-Assisted Contrast Media Magnetic Resonance Imaging Humans Neural Networks, Computer Machine Learning Male Middle Aged Algorithms Aged
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Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound.

IEEE transactions on medical imaging Apr 25, 2019
Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of essential importance for image-guided prostate interventions and treatment planning. However, developing such automatic solutions remains very challenging due to the missin...
Male Deep Learning Ultrasonography Imaging, Three-Dimensional Prostatic Neoplasms Prostate Humans Neural Networks, Computer
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Deep Learning for Real-time, Automatic, and Scanner-adapted Prostate (Zone) Segmentation of Transrectal Ultrasound, for Example, Magnetic Resonance Imaging-transrectal Ultrasound Fusion Prostate Biopsy.

European urology focus Apr 23, 2019
BACKGROUND: Although recent advances in multiparametric magnetic resonance imaging (MRI) led to an increase in MRI-transrectal ultrasound (TRUS) fusion prostate biopsies, these are time consuming, laborious, and costly. Introduction of deep-learning ...
Image-Guided Biopsy Prostate Rectum Biopsy Humans Middle Aged Aged, 80 and over Ultrasonography Artificial Intelligence Adult Deep Learning Magnetic Resonance Imaging Aged Male
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Re: What is the role of NeuroSAFE in robotic radical prostatectomy?

Journal of robotic surgery Apr 12, 2019
Robotic Surgical Procedures Prostate Prostatectomy Humans Male Robotics
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A deep-learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot-assisted radical prostatectomy.

BJU international Mar 20, 2019
OBJECTIVES: To predict urinary continence recovery after robot-assisted radical prostatectomy (RARP) using a deep learning (DL) model, which was then used to evaluate surgeon's historical patient outcomes.
Prostate Deep Learning Prostatectomy Models, Statistical Urinary Incontinence Recovery of Function Cohort Studies Surgeons Humans Robotic Surgical Procedures Treatment Outcome Middle Aged Aged Postoperative Complications Male
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