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 The Reproducibility of Deep Learning-Based Segmentation of the Prostate Gland and Zones on T2-Weighted MR Images

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1690
Author(s):  
Mohammed R. S. Sunoqrot ◽  
Kirsten M. Selnæs ◽  
Elise Sandsmark ◽  
Sverre Langørgen ◽  
Helena Bertilsson ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prostate Gland ◽  
Peripheral Zone ◽  
Clinical Implementation ◽  
Mr Images ◽  
Cad Systems ◽  
Prostate Segmentation ◽  
Volume Of Interest ◽  
Segmentation Methods ◽  
Detection And Diagnosis

Volume of interest segmentation is an essential step in computer-aided detection and diagnosis (CAD) systems. Deep learning (DL)-based methods provide good performance for prostate segmentation, but little is known about the reproducibility of these methods. In this work, an in-house collected dataset from 244 patients was used to investigate the intra-patient reproducibility of 14 shape features for DL-based segmentation methods of the whole prostate gland (WP), peripheral zone (PZ), and the remaining prostate zones (non-PZ) on T2-weighted (T2W) magnetic resonance (MR) images compared to manual segmentations. The DL-based segmentation was performed using three different convolutional neural networks (CNNs): V-Net, nnU-Net-2D, and nnU-Net-3D. The two-way random, single score intra-class correlation coefficient (ICC) was used to measure the inter-scan reproducibility of each feature for each CNN and the manual segmentation. We found that the reproducibility of the investigated methods is comparable to manual for all CNNs (14/14 features), except for V-Net in PZ (7/14 features). The ICC score for segmentation volume was found to be 0.888, 0.607, 0.819, and 0.903 in PZ; 0.988, 0.967, 0.986, and 0.983 in non-PZ; 0.982, 0.975, 0.973, and 0.984 in WP for manual, V-Net, nnU-Net-2D, and nnU-Net-3D, respectively. The results of this work show the feasibility of embedding DL-based segmentation in CAD systems, based on multiple T2W MR scans of the prostate, which is an important step towards the clinical implementation.

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

scholarly journals An automatic deep learning-based workflow for glioblastoma survival prediction using pre-operative multimodal MR images: a feasibility study

2021 ◽  
pp. 100746
Author(s):  
Jie Fu ◽  
Kamal Singhrao ◽  
Xinran Zhong ◽  
Yu Gao ◽  
Sharon X. Qi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Feasibility Study ◽  
Survival Prediction ◽  
Mr Images

scholarly journals Method for Diagnosing the Bone Marrow Edema of Sacroiliac Joint in Patients with Axial Spondyloarthritis Using Magnetic Resonance Image Analysis Based on Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1156
Author(s):  
Kang Hee Lee ◽  
Sang Tae Choi ◽  
Guen Young Lee ◽  
You Jung Ha ◽  
Sang-Il Choi
Keyword(s):  
Bone Marrow ◽  
Deep Learning ◽  
Bone Marrow Edema ◽  
Axial Spondyloarthritis ◽  
Mr Images ◽  
Sacroiliac Joints ◽  
Learning Network ◽  
Active Sacroiliitis ◽  
Deep Learning Network ◽  
Marrow Edema

Axial spondyloarthritis (axSpA) is a chronic inflammatory disease of the sacroiliac joints. In this study, we develop a method for detecting bone marrow edema by magnetic resonance (MR) imaging of the sacroiliac joints and a deep-learning network. A total of 815 MR images of the sacroiliac joints were obtained from 60 patients diagnosed with axSpA and 19 healthy subjects. Gadolinium-enhanced fat-suppressed T1-weighted oblique coronal images were used for deep learning. Active sacroiliitis was defined as bone marrow edema, and the following processes were performed: setting the region of interest (ROI) and normalizing it to a size suitable for input to a deep-learning network, determining bone marrow edema using a convolutional-neural-network-based deep-learning network for individual MR images, and determining sacroiliac arthritis in subject examinations based on the classification results of individual MR images. About 70% of the patients and normal subjects were randomly selected for the training dataset, and the remaining 30% formed the test dataset. This process was repeated five times to calculate the average classification rate of the five-fold sets. The gradient-weighted class activation mapping method was used to validate the classification results. In the performance analysis of the ResNet18-based classification network for individual MR images, use of the ROI showed excellent detection performance of bone marrow edema with 93.55 ± 2.19% accuracy, 92.87 ± 1.27% recall, and 94.69 ± 3.03% precision. The overall performance was additionally improved using a median filter to reflect the context information. Finally, active sacroiliitis was diagnosed in individual subjects with 96.06 ± 2.83% accuracy, 100% recall, and 94.84 ± 3.73% precision. This is a pilot study to diagnose bone marrow edema by deep learning based on MR images, and the results suggest that MR analysis using deep learning can be a useful complementary means for clinicians to diagnose bone marrow edema.

scholarly journals DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia from MR Images

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Suriya Murugan ◽  
Chandran Venkatesan ◽  
M G Sumithra ◽  
Xiao-Zhi Gao ◽  
B Elakkiya ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Diagnosis ◽  
Learning Model ◽  
Mr Images ◽  
Deep Learning Model

Deep Learning-Based Binary Classification of ADHD Using Resting State MR Images

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Vikas Khullar ◽  
Karuna Salgotra ◽  
Harjit Pal Singh ◽  
Davinder Pal Sharma
Keyword(s):  
Deep Learning ◽  
Resting State ◽  
Binary Classification ◽  
Mr Images

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
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