Segmentation of the Prostate Transition Zone and Peripheral Zone on                     MR Images with Deep Learning

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.

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

Advances in Radiation Oncology ◽

10.1016/j.adro.2021.100746 ◽

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

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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 ◽

10.3390/diagnostics11071156 ◽

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.

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

IEEE Access ◽

10.1109/access.2021.3090474 ◽

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

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

Augmented Human Research ◽

10.1007/s41133-020-00042-y ◽

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

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Enhanced Analysis of Brain MR Images for Detection of Abnormal Tissues Using Deep Learning

Sustainable Communication Networks and Application - Lecture Notes on Data Engineering and Communications Technologies ◽

10.1007/978-981-15-8677-4_25 ◽

2021 ◽

pp. 305-319

Author(s):

Jyotindra Dharwa ◽

Shivang Patel

Keyword(s):

Deep Learning ◽

Mr Images ◽

Brain Mr Images

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Synthesis of Prostate MR Images for Classification Using Capsule Network-Based GAN Model

Sensors ◽

10.3390/s20205736 ◽

2020 ◽

Vol 20 (20) ◽

pp. 5736

Author(s):

Houqiang Yu ◽

Xuming Zhang

Keyword(s):

Prostate Cancer ◽

Deep Learning ◽

Data Augmentation ◽

Spatial Relationship ◽

Health Concern ◽

Complex Data ◽

Mr Images ◽

Generative Adversarial Network ◽

Generation Task ◽

Leibler Divergence

Prostate cancer remains a major health concern among elderly men. Deep learning is a state-of-the-art technique for MR image-based prostate cancer diagnosis, but one of major bottlenecks is the severe lack of annotated MR images. The traditional and Generative Adversarial Network (GAN)-based data augmentation methods cannot ensure the quality and the diversity of generated training samples. In this paper, we have proposed a novel GAN model for synthesis of MR images by utilizing its powerful ability in modeling the complex data distributions. The proposed model is designed based on the architecture of deep convolutional GAN. To learn the more equivariant representation of images that is robust to the changes in the pose and spatial relationship of objects in the images, the capsule network is applied to replace CNN used in the discriminator of regular GAN. Meanwhile, the least squares loss has been adopted for both the generator and discriminator in the proposed GAN to address the vanishing gradient problem of sigmoid cross entropy loss function in regular GAN. Extensive experiments are conducted on the simulated and real MR images. The results demonstrate that the proposed capsule network-based GAN model can generate more realistic and higher quality MR images than the compared GANs. The quantitative comparisons show that among all evaluated models, the proposed GAN generally achieves the smallest Kullback–Leibler divergence values for image generation task and provides the best classification performance when it is introduced into the deep learning method for image classification task.

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