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.

Format:

Book

Contributor:

Melbourne, Andrew, Editor.

Licandro, Roxane, Editor.

DiFranco, Matthew., Editor.

Rota, Paolo, Editor.

Gau, Melanie., Editor.

Kampel, Martin, Editor.

Aughwane, Rosalind., Editor.

Moeskops, Pim., Editor.

Schwartz, Ernst., Editor.

Robinson, Emma, Editor.

Makropoulos, Antonios., Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 11076

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11076

Language:

English

Subjects (All):

Artificial intelligence.

Computer vision.

Medical informatics.

Computer arithmetic and logic units.

Artificial Intelligence.

Computer Vision.

Health Informatics.

Arithmetic and Logic Structures.

Local Subjects:

Artificial Intelligence.

Computer Vision.

Health Informatics.

Arithmetic and Logic Structures.

Physical Description:

1 online resource (XI, 180 pages) : 74 illustrations

Edition:

1st ed. 2018.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2018.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-030-00807-9

9783030008079

Access Restriction:

Restricted for use by site license.