Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis

1. Front Matter

   Pages i-xvii

   PDF

2. First Workshop on Smart UltraSound Imaging

   1. Front Matter

      Pages 1-1

      PDF

   2. Straight to the Point: Reinforcement Learning for User Guidance in Ultrasound

      • Fausto Milletari, Vighnesh Birodkar, Michal Sofka

      Pages 3-10

   3. Registration of Untracked 2D Laparoscopic Ultrasound Liver Images to CT Using Content-Based Retrieval and Kinematic Priors

      • João Ramalhinho, Henry Tregidgo, Moustafa Allam, Nikolina Travlou, Kurinchi Gurusamy, Brian Davidson et al.

      Pages 11-19

   4. Direct Detection and Measurement of Nuchal Translucency with Neural Networks from Ultrasound Images

      • Tianchi Liu, Mengdi Xu, Ziyu Zhang, Changping Dai, Haiyu Wang, Rui Zhang et al.

      Pages 20-28

   5. Automated Left Ventricle Dimension Measurement in 2D Cardiac Ultrasound via an Anatomically Meaningful CNN Approach

      • Andrew Gilbert, Marit Holden, Line Eikvil, Svein Arne Aase, Eigil Samset, Kristin McLeod

      Pages 29-37

   6. SPRNet: Automatic Fetal Standard Plane Recognition Network for Ultrasound Images

      • Jiajun Liang, Rian Huang, Peiyao Kong, Shengli Li, Tianfu Wang, Baiying Lei

      Pages 38-46

   7. Representation Disentanglement for Multi-task Learning with Application to Fetal Ultrasound

      • Qingjie Meng, Nick Pawlowski, Daniel Rueckert, Bernhard Kainz

      Pages 47-55

   8. Adversarial Learning for Deformable Image Registration: Application to 3D Ultrasound Image Fusion

      • Zisheng Li, Masahiro Ogino

      Pages 56-64

   9. Monitoring Achilles Tendon Healing Progress in Ultrasound Imaging with Convolutional Neural Networks

      • Piotr Woznicki, Przemyslaw Przybyszewski, Norbert Kapinski, Jakub Zielinski, Beata Ciszkowska-Lyson, Bartosz Borucki et al.

      Pages 65-73

   10. Deep Learning-Based Pneumothorax Detection in Ultrasound Videos

       • Courosh Mehanian, Sourabh Kulhare, Rachel Millin, Xinliang Zheng, Cynthia Gregory, Meihua Zhu et al.

       Pages 74-82

   11. Deep Learning Based Minimum Variance Beamforming for Ultrasound Imaging

       • Renxin Zhuang, Junying Chen

       Pages 83-91

3. 4th Workshop on Perinatal, Preterm and Paediatric Image Analysis

   1. Front Matter

      Pages 93-93

      PDF

   2. Estimation of Preterm Birth Markers with U-Net Segmentation Network

      • Tomasz Włodarczyk, Szymon Płotka, Tomasz Trzciński, Przemysław Rokita, Nicole Sochacki-Wójcicka, Michał Lipa et al.

      Pages 95-103

   3. Investigating Image Registration Impact on Preterm Birth Classification: An Interpretable Deep Learning Approach

      • Irina Grigorescu, Lucilio Cordero-Grande, A. David Edwards, Joseph V. Hajnal, Marc Modat, Maria Deprez

      Pages 104-112

   4. Dual Network Generative Adversarial Networks for Pediatric Echocardiography Segmentation

      • Libao Guo, Yujin Hu, Baiying Lei, Jie Du, Muyi Mao, Zelong Jin et al.

      Pages 113-122

   5. Reproducibility of Functional Connectivity Estimates in Motion Corrected Fetal fMRI

      • Daniel Sobotka, Roxane Licandro, Michael Ebner, Ernst Schwartz, Tom Vercauteren, Sebastien Ourselin et al.

      Pages 123-132

   6. Plug-and-Play Priors for Reconstruction-Based Placental Image Registration

      • Jiarui Xing, Ulugbek Kamilov, Wenjie Wu, Yong Wang, Miaomiao Zhang

      Pages 133-142

   7. A Longitudinal Study of the Evolution of the Central Sulcus’ Shape in Preterm Infants Using Manifold Learning

      • Héloïse de Vareilles, Zhongyi Sun, Manon Benders, Clara Fischer, François Leroy, Linda de Vries et al.

      Pages 143-152

   8. Prediction of Failure of Induction of Labor from Ultrasound Images Using Radiomic Features

      • María Inmaculada García Ocaña, Karen López-Linares Román, Jorge Burgos San Cristóbal, Ana del Campo Real, Iván Macía Oliver

      Pages 153-160

This book constitutes the refereed joint proceedings of the First International Workshop on Smart Ultrasound Imaging, SUSI 2019, and the 4th International Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019.

The 10 full papers presented at SUSI 2019 and the 10 full papers presented at PIPPI 2019 were carefully reviewed and selected.

The SUSI papers cover a wide range of medical applications of B-Mode ultrasound, including cardiac (echocardiography), abdominal (liver), fetal, musculoskeletal, and lung.

The PIPPI papers cover the detailed scientific study of volumetric growth, myelination and cortical microstructure, placental structure and function.

• artificial intelligence
• deep learning
• image analysis
• image reconstruction
• image segmentation
• machine learning
• medical imaging
• MRI
• neural networks
• signal processing
• ultrasonic imaging
• ultrasound images

• Shanghai Jiao Tong University, Shanghai, China

  Qian Wang

• King's College London, London, UK

  Alberto Gomez, Jana Hutter

• GE Vingmed Ultrasound, GE Healthcare, Oslo, Norway

  Kristin McLeod

• School of Imaging Sciences & Biomedical Engineering, King's College London, London, UK

  Veronika Zimmer, Emma Robinson, Daan Christiaens, Andrew Melbourne

• ImFusion GmbH, Munich, Germany

  Oliver Zettinig

• Computer Vision Lab, TU Vienna, Vienna, Austria

  Roxane Licandro

• Boston Children's Hospital, Boston, UK

  Esra Abaci Turk

Policies and ethics