Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis : First International Workshop, DATRA 2018 and Third International Workshop, PIPPI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings / edited by Andrew Melbourne, Roxane Licandro, Matthew DiFranco, Paolo Rota, Melanie Gau, Martin Kampel, Rosalind Aughwane, Pim Moeskops, Ernst Schwartz, Emma Robinson, Antonios Makropoulos.

Cham : Springer International Publishing : Imprint: Springer, 2018.
1 online resource (XI, 180 pages) : 74 illustrations
1st ed. 2018.
Computer Science (Springer-11645)
LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics SL 6, 11076
Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11076
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Artificial intelligence.
Optical data processing.
Medical informatics.
Computer arithmetic and logic units.
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Artificial Intelligence. (search)
Image Processing and Computer Vision. (search)
Health Informatics. (search)
Arithmetic and Logic Structures. (search)
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This book constitutes the refereed joint proceedings of the First International Workshop on Data Driven Treatment Response Assessment, DATRA 2018 and the Third International Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 5 full papers presented at DATRA 2018 and the 12 full papers presented at PIPPI 2018 were carefully reviewed and selected. The DATRA papers cover a wide range of exploring pattern recognition technologies for tackling clinical issues related to the follow-up analysis of medical data with focus on malignancy progression analysis, computer-aided models of treatment response, and anomaly detection in recovery feedback. The PIPPI papers cover topics of advanced image analysis approaches focused on the analysis of growth and development in the fetal, infant and paediatric period.
DeepCS: Deep Convolutional Neural Network and SVM based Single Image Super-Resolution
Automatic Segmentation of Thigh Muscle in Longitudinal 3D T1-Weighted Magnetic Resonance (MR) Images
Detecting Bone Lesions in Multiple Myeloma Patient Using Transfer Learning
Quantification of Local Metabolic Tumor Volume Changes by Registering Blended PET-CT Images for Prediction of Pathologic Tumor Response
Optimizing External Surface Sensor Locations for Respiratory Tumor Motion Prediction
Segmentation of Fetal Adipose Tissue Using Efficient CNNs for Portable Ultrasound
Automatic Shadow Detection in 2D Ultrasound Images
Multi-Channel Groupwise Registration to Construct and Ultrasound-Specific Fetal Brain Atlas
Investigating Brain Age Deviation in Preterm Infants: A Deep Learning Approach
Segmentation of Pelvic Vessels in Pediatric MRI Using a Patch-Based Deep Learning Approach
Multi-View Image Reconstruction: Application to Fetal Ultrasound Compounding
EchoFusion: Tracking and Reconstruction of Objects in 4D Freehand Ultrasound Imaging Without External Trackers
Better Feature Matching for Placental Panorama Construction
Combining Deep Learning and Multi-Atlas Label Fusion for Automated Placenta Segmentation from 3DUS
LSTM Spatial Co-transformer Networks for Registration of 3D Fetal US and MR Brain Images
Automatic and Efficient Standard Plane Recognition in Fetal Ultrasound Images via Multi-Scale Dense Networks
Paediatric Liver Segmentation for Low-Contrast CT Images.
Melbourne, Andrew, editor., Editor,
Licandro, Roxane. editor., Editor,
DiFranco, Matthew. editor., Editor,
Rota, Paolo, editor., Editor,
Gau, Melanie. editor., Editor,
Kampel, Martin, editor., Editor,
Aughwane, Rosalind. editor., Editor,
Moeskops, Pim. editor., Editor,
Schwartz, Ernst. editor., Editor,
Robinson, Emma, editor., Editor,
Makropoulos, Antonios. editor., Editor,
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