4.6 Article

CT prostate segmentation based on synthetic MRI-aided deep attention fully convolution network

Related references

Note: Only part of the references are listed.
Article Radiology, Nuclear Medicine & Medical Imaging

Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation

Bo Wang et al.

MEDICAL PHYSICS (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Multiparametric MRI-guided dose boost to dominant intraprostatic lesions in CT-based High-dose-rate prostate brachytherapy

Tonghe Wang et al.

BRITISH JOURNAL OF RADIOLOGY (2019)

Article Computer Science, Artificial Intelligence

Attention gated networks: Learning to leverage salient regions in medical images

Jo Schlemper et al.

MEDICAL IMAGE ANALYSIS (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Ultrasound prostate segmentation based on multidirectional deeply supervised V-Net

Yang Lei et al.

MEDICAL PHYSICS (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks

Yang Lei et al.

MEDICAL PHYSICS (2019)

Article Engineering, Biomedical

Ultrasound Image Segmentation: A Deeply Supervised Network With Attention to Boundaries

Deepak Mishra et al.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2019)

Proceedings Paper Computer Science, Theory & Methods

Unpaired Brain MR-to-CT Synthesis Using a Structure-Constrained CycleGAN

Heran Yang et al.

DEEP LEARNING IN MEDICAL IMAGE ANALYSIS AND MULTIMODAL LEARNING FOR CLINICAL DECISION SUPPORT, DLMIA 2018 (2018)

Article Radiology, Nuclear Medicine & Medical Imaging

Segmentation of the prostate and organs at risk in male pelvic CT images using deep learning

Samaneh Kazemifar et al.

BIOMEDICAL PHYSICS & ENGINEERING EXPRESS (2018)

Article Engineering, Biomedical

Fully automated organ segmentation in male pelvic CT images

Anjali Balagopal et al.

PHYSICS IN MEDICINE AND BIOLOGY (2018)

Article Radiology, Nuclear Medicine & Medical Imaging

Improved prostate delineation in prostate HDR brachytherapy with TRUS-CT deformable registration technology: A pilot study with MRI validation

Xiaofeng Yang et al.

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Densely Connected Convolutional Networks

Gao Huang et al.

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

Article Oncology

Cancer treatment and survivorship statistics, 2016

Kimberly D. Miller et al.

CA-A CANCER JOURNAL FOR CLINICIANS (2016)

Article Computer Science, Interdisciplinary Applications

Accurate Segmentation of CT Male Pelvic Organs via Regression-Based Deformable Models and Multi-Task Random Forests

Yaozong Gao et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2016)

Article Computer Science, Artificial Intelligence

A learning-based CT prostate segmentation method via joint transductive feature selection and regression

Yinghuan Shi et al.

NEUROCOMPUTING (2016)

Article Computer Science, Artificial Intelligence

Semi-Automatic Segmentation of Prostate in CT Images via Coupled Feature Representation and Spatial-Constrained Transductive Lasso

Yinghuan Shi et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2015)

Article Computer Science, Interdisciplinary Applications

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)

Editorial Material Oncology

QUANTITATIVE ANALYSES OF NORMAL TISSUE EFFECTS IN THE CLINIC (QUANTEC): AN INTRODUCTION TO THE SCIENTIFIC ISSUES

Soren M. Bentzen et al.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2010)

Article Oncology

Prostate volume contouring: A 3D analysis of segmentation using 3DTRUS, CT, and MR

Wendy L. Smith et al.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2007)

Review Radiology, Nuclear Medicine & Medical Imaging

Imaging prostate cancer: A multidisciplinary perspective

Hedvig Hricak et al.

RADIOLOGY (2007)

Article Computer Science, Interdisciplinary Applications

A shape-based approach to the segmentation of medical imagery using level sets

A Tsai et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2003)