Multimodality Imaging, Volume 1: Deep learning applications

PRELIMS.pdf
Preface
Purpose
Content and organization
Editor biographies
Mainak Biswas
Jasjit S Suri
List of contributors
CH001.pdf
Chapter 1 Deep learning and augmented radiology
1.1 Introduction
1.2 The present
1.2.1 Fatty liver disease risk stratification
1.2.2 Carotid intima–media thickness (cIMT) measurement using DL for segmentation
1.2.3 Assessment of the treatment effects in acute ischemic stroke (AIS) using DL from MR images
1.2.4 Diagnosis of prostate cancer using DL
1.2.5 CT-based respiratory disease prognosis using DL
1.3 Future
1.3.1 DL in radiology
1.3.2 The future of radiology
1.4 The potential of deep learning
1.4.1 Economy
1.4.2 Augmented radiology and DL
1.4.3 Further development of DL
1.5 Challenges and risks in DL
1.5.1 Safety
1.5.2 Privacy
1.5.3 Legality
1.6 Conclusion
References
CH002.pdf
Chapter 2 Deep learning in biomedical imaging
2.1 Introduction
2.2 Deep learning models
2.2.1 Deep belief networks
2.2.2 Autoencoder
2.2.3 Convolutional neural networks
2.2.4 Deep residual network
2.3 DL based biomedical imaging systems
2.3.1 Cardiovascular application
2.3.2 Neurology application
2.3.3 Mammography application
2.3.4 Microscopy applications
2.3.5 Dermatology application
2.3.6 Gastroenterology applications
2.3.7 Pulmonary application
2.4 Discussion
2.4.1 Graphics processing unit and open-source software for deep learning
References
CH003.pdf
Chapter 3 A review of artificial intelligence in brain tumor classification and segmentation
3.1 Introduction
3.2 Brain cancer pathophysiology
3.2.1 Architecture at the cellular level
3.2.2 Links between brain tumors and genes
3.3 Imaging modality
3.3.1 Computed tomography imaging
3.3.2 Magnetic resonance imaging
3.3.3 Biopsy
3.3.4 Hyperstereoscopy imaging
3.3.5 MR spectroscopy
3.4 Guidelines for tumor grading by the WHO
3.5 Brain tumor tests
3.5.1 Biomarker test
3.5.2 Biopsy
3.5.3 Imaging test
3.6 Classification methods
3.6.1 Machine learning
3.6.2 Deep learning
3.6.3 Brain image analysis using deep learning
3.6.4 A plausible solution for brain cancer classification
3.7 Brain cancer and other brain disorders
3.7.1 Stroke
3.7.2 Alzheimer’s disease
3.7.3 Parkinson’s disease
3.7.4 Leukoaraiosis
3.7.5 Multiple sclerosis
3.7.6 Wilson’s disease
3.8 Discussion
3.8.1 A note on cancer detection biomarkers
3.9 Conclusion
References
CH004.pdf
Chapter 4 MRI based brain tumor classification and its validation: a transfer learning paradigm
4.1 Introduction
4.2 Background literature survey
4.3 Demographics and data preparation
4.3.1 Patient demographics
4.3.2 Data preparation
4.4 Methodology
4.4.1 CNN model
4.4.2 The architecture of AlexNet
4.4.3 Transfer learning and workflow
4.4.4 Weight optimization
4.4.5 A generalization system for tumor classification
4.5 Experimental protocol, results, and performance evaluation
4.5.1 Parameter selection and simulation
4.5.2 Results
4.5.3 Performance evaluation
4.6 Model validation and verification
4.6.1 Hypothesis validation
4.6.2 Software verification
4.7 Discussion
4.8 Conclusion
Appendix A
References
CH005.pdf
Chapter 5 Magnetic resonance based Wilson’s disease tissue characterization in an artificial intelligence framework using transfer learning
5.1 Introduction
5.2 Background literature
5.3 Methodology
5.3.1 Patient demographics
5.3.2 Data augmentation
5.3.3 Pre-processing: skull and background removal
5.4 Global architecture: transfer learning
5.4.1 AlexNet
5.4.2 ResNet50
5.4.3 DenseNet161
5.4.4 XceptionNet
5.4.5 InceptionV3
5.4.6 SivaSuriNet
5.5 Results
5.6 Characterization
5.6.1 Mean feature strength
5.6.2 Higher-order spectrum
5.7 Discussion
5.8 Conclusion
References
CH006.pdf
Chapter 6 Artificial intelligence based carotid plaque tissue characterisation and classification from ultrasound images using a deep learning paradigm
6.1 Introduction
6.2 Methodology
6.2.1 Patient demographics
6.2.2 Exclusion criteria
6.2.3 Ultrasound data acquisition and preprocessing
6.2.4 Plaque delineation
6.2.5 Ultrasound plaque data augmentation
6.2.6 Supercomputer specifications
6.2.7 Deep learning architecture
6.2.8 Experimental protocol
6.2.9 Machine learning for benchmarking deep learning
6.2.10 Performance parameters using the DL and ML methods
6.3 Results
6.3.1 Deep learning data analysis and benchmarking against machine learning
6.3.2 Plaque characterisation in a deep learning framework
6.4 Discussion
6.4.1 A note on the unbalanced datasets for symptomatic and asymptomatic plaques
6.4.2 Benchmarking against techniques available in the literature
6.4.3 A special note on the comparison of supercomputer hardware to a local machine
6.4.4 Strengths, weaknesses, and extensions
6.5 Conclusion
Disclosure/Conflict of interest
Appendix
References
CH007.pdf
Chapter 7 Quantification of plaque volume using a two-stage deep learning paradigm
7.1 Introduction
7.2 Background
7.3 Data acquisition
7.4 Methodology
7.5 Experimental protocol and results
7.6 Statistical tests
7.7 Discussion
7.8 Conclusion
References
CH008.pdf
Chapter 8 Stenosis measurement from ultrasound carotid artery images in the deep learning paradigm
8.1 Introduction
8.2 Patient demographics and image acquisition
8.3 Methodology
8.4 Experimental protocol, performance parameters, and results
8.5 Statistical tests, variability and error bias analysis, and risk characterization
8.6 Discussion
8.7 Conclusion
References
CH009.pdf
Chapter 9 A systematic review of conventional and deep learning models for the measurement of plaque burden
9.1 Introduction
9.2 Chronological generation of cIMT regional segmentation and cIMT measurement
9.3 Ml application for cIMT and PA measurement
9.3.1 ANN model for cIMT region detection
9.3.2 Extreme learning machine radial basis neural network model for cIMT region detection
9.3.3 Fuzzy K-means classifier for cIMT region extraction
9.4 Deep learning application for cIMT and PA extraction
9.4.1 ANN autoencoder based cIMT region segmentation
9.4.2 Fully convolutional network for cIMT region estimation
9.4.3 FCN for PA measurement
9.4.4 Two-stage patching based AI model for cIMT and PA measurement
9.5 Discussion
9.5.1 Benchmarking
9.5.2 A short note on cardiovascular risk assessment
9.5.3 A note on the clinical impact of AI methods on cIMT/PA techniques
9.5.4 A note on inter- and intra-observer variability analysis on the evaluation of AI models
9.5.5 A short note on 10 year risk estimation using cIMT and PA
9.5.6 Statistical power analysis and diagnostic-odds ratio
9.6 Conclusions
Appendix A Mathematical representations of the ML and DL paradigms
References
CH010.pdf
Chapter 10 Ultrasound fatty liver disease risk stratification using an extreme learning machine framework
10.1 Introduction
10.2 Data demographics, collection, and preparation
10.2.1 Sub-sampling of US datasets (S4 and S8)
10.3 Methodology
10.3.1 The three-layered ELM architecture
10.3.2 Tissue characterization and risk stratification using ELM and SVM frameworks
10.3.3 Feature mining
10.4 Experimental protocol
10.4.1 Experiment 1: the effect of the size of the training data on accuracy using four CV protocols
10.4.2 Experiment 2: the effect of the training set size using the sub-sampling strategy
10.4.3 Experiment 3: ELM and SVM time complexity
10.5 Results
10.5.1 Experiment 1: the effect of training data size on accuracy using the four CV protocols
10.5.2 Experiment 2: the effect of the training data size in parts during the CV protocols
10.5.3 Experiment 3: time comparison between ELM and SVM
10.6 Performance evaluations
10.6.1 ROC curves
10.6.2 Reliability and stability analysis
10.7 Discussion
10.7.1 Benchmarking
10.7.2 ELM and BPNN comparison
10.7.3 A special note on the ELM and SVM
10.7.4 Strengths, weaknesses, and future work
10.8 Conclusions
Appendix A Scientific validation
Appendix B Results of the ELM/SVM classifier for the S4 and S8 datasets
References
CH011.pdf
Chapter 11 Symtosis: deep learning based liver ultrasound tissue characterisation and risk stratification
11.1 Introduction
11.2 Patient demographics and acquisition
11.3 Methodology
11.3.1 Risk stratification model
11.3.2 CNN architecture
11.4 Results
11.4.1 Image pre-processing for the DL, ELM, and SVM
11.4.2 The effect of data size on stratification accuracy
11.4.3 Stratification analysis using the ‘liver segregation index’
11.5 Performance evaluation: ROC, reliability, and timing analysis
11.5.1 ROC analysis
11.5.2 Reliability analysis
11.5.3 Timing analysis
11.6 Discussion
11.7 Conclusion
References
CH012.pdf
Chapter 12 Characterization of COVID-19 severity in infected lungs via artificial intelligence transfer learning
12.1 Introduction
12.2 Methodology
12.2.1 Patient demographics
12.2.2 Data acquisition
12.2.3 Baseline characteristics
12.2.4 Segmentation
12.2.5 Augmentation
12.2.6 Models
12.3 Results
12.4 Characterization
12.5 Discussion
References