scholarly journals CDA-Net for Automatic Prostate Segmentation in MR Images

2020 ◽  
Vol 10 (19) ◽  
pp. 6678
Author(s):  
Zhiying Lu ◽  
Mingyue Zhao ◽  
Yong Pang
Keyword(s):  
Attention Mechanism ◽  
Dice Similarity Coefficient ◽  
Mr Images ◽  
Prostate Segmentation ◽  
Sequence Correlation ◽  
Correlation Processing ◽  
Mri Scans ◽  
Roi Extraction ◽  
Two Stages ◽  
Essential Prerequisite

Automatic and accurate prostate segmentation is an essential prerequisite for assisting diagnosis and treatment, such as guiding biopsy procedures and radiation therapy. Therefore, this paper proposes a cascaded dual attention network (CDA-Net) for automatic prostate segmentation in MRI scans. The network includes two stages of RAS-FasterRCNN and RAU-Net. Firstly, RAS-FasterRCNN uses improved FasterRCNN and sequence correlation processing to extract regions of interest (ROI) of organs. This ROI extraction serves as a hard attention mechanism to focus the segmentation of the subsequent network on a certain area. Secondly, the addition of residual convolution block and self-attention mechanism in RAU-Net enables the network to gradually focus on the area where the organ exists while making full use of multiscale features. The algorithm was evaluated on the PROMISE12 and ASPS13 datasets and presents the dice similarity coefficient of 92.88% and 92.65%, respectively, surpassing the state-of-the-art algorithms. In a variety of complex slice images, especially for the base and apex of slice sequences, the algorithm also achieved credible segmentation performance.

scholarly journals An automatic method for prostate segmentation on 3D MRI scans using local phylogenetic indexes and XGBoost

2021 ◽  
Author(s):  
Giovanni L. F. da Silva ◽  
Francisco Y. C. de Oliveira ◽  
João O. B. Diniz ◽  
Petterson S. Diniz ◽  
Darlan B. P. Quintanilha ◽  
...  
Keyword(s):  
High Performance ◽  
Current Practice ◽  
Dice Similarity Coefficient ◽  
Automatic Method ◽  
Performance Potential ◽  
Prostate Segmentation ◽  
Correct Determination ◽  
Mri Scans ◽  
3D Magnetic Resonance Imaging

The detection, diagnosis, and treatment of prostate cancer depends on the correct determination of the prostate anatomy. In current practice, the prostate segmentation is performed manually by a radiologist, which is extremely time-consuming that demands experience and concentration. Therefore, this paper proposes an automatic method for prostate segmentation on 3D magnetic resonance imaging scans using a superpixel technique, phylogenetic indexes, and an optimized XGBoost algorithm. The proposed method has been evaluated on the Prostate 3T and PROMISE12 databases presenting a dice similarity coefficient of 84.48% and a volumetric similarity of 95.91%, demonstrating the high-performance potential of the proposed method.

scholarly journals Improved PET/MRI attenuation correction in the pelvic region using a statistical decomposition method on T2-weighted images

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Elin Wallstén ◽  
Jan Axelsson ◽  
Joakim Jonsson ◽  
Camilla Thellenberg Karlsson ◽  
Tufve Nyholm ◽  
...  
Keyword(s):  
Attenuation Correction ◽  
Research Study ◽  
Standardized Uptake Value ◽  
Ground Truth ◽  
Whole Body ◽  
Mr Images ◽  
Pelvic Region ◽  
Mri Scans ◽  
The Mean ◽  
Prostate Cancer Research

Abstract Background Attenuation correction of PET/MRI is a remaining problem for whole-body PET/MRI. The statistical decomposition algorithm (SDA) is a probabilistic atlas-based method that calculates synthetic CTs from T2-weighted MRI scans. In this study, we evaluated the application of SDA for attenuation correction of PET images in the pelvic region. Materials and method Twelve patients were retrospectively selected from an ongoing prostate cancer research study. The patients had same-day scans of [11C]acetate PET/MRI and CT. The CT images were non-rigidly registered to the PET/MRI geometry, and PET images were reconstructed with attenuation correction employing CT, SDA-generated CT, and the built-in Dixon sequence-based method of the scanner. The PET images reconstructed using CT-based attenuation correction were used as ground truth. Results The mean whole-image PET uptake error was reduced from − 5.4% for Dixon-PET to − 0.9% for SDA-PET. The prostate standardized uptake value (SUV) quantification error was significantly reduced from − 5.6% for Dixon-PET to − 2.3% for SDA-PET. Conclusion Attenuation correction with SDA improves quantification of PET/MR images in the pelvic region compared to the Dixon-based method.

scholarly journals Automatic prostate segmentation using multiobjective active appearance modelin MR images

2019 ◽  
Vol 27 (6) ◽  
pp. 4361-4377
Author(s):  
Ahad SALIMI ◽  
Mohamad Ali POURMINA ◽  
Mohammad-Shahram MOIN
Keyword(s):  
Mr Images ◽  
Prostate Segmentation ◽  
Active Appearance

Fully automatic prostate segmentation in MR images using a new hybrid active contour-based approach

2018 ◽  
Vol 12 (8) ◽  
pp. 1629-1637 ◽  
Author(s):  
Ahad Salimi ◽  
Mohammad Ali Pourmina ◽  
Mohammad-Shahram Moin
Keyword(s):  
Active Contour ◽  
Mr Images ◽  
Prostate Segmentation ◽  
Fully Automatic

TH-CD-206-02: BEST IN PHYSICS (IMAGING): 3D Prostate Segmentation in MR Images Using Patch-Based Anatomical Signature

2016 ◽  
Vol 43 (6Part46) ◽  
pp. 3883-3883 ◽  
Author(s):  
X Yang ◽  
A Jani ◽  
P Rossi ◽  
H Mao ◽  
W Curran ◽  
...  
Keyword(s):  
Mr Images ◽  
Prostate Segmentation

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.

Sign in / Sign up

Export Citation Format

Share Document